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EVALUATION OF AN INFORMATION COMMUNICATION TECHNOLOGY PILOT
IN SUPPORT OF PUBLIC HEALTH IN SOUTH ASIA
- REAL-TIME BIOSURVEILLANCE PROGRAM By Nuwan T. Waidyanatha
Final Technical Report v1.0
December 31, 2010

Published by:
International Development Research Center
Digital Library
Ottawa, Canada
IDRC Project Number: 10530-001
IDRC Project Title: Evaluating a Real-Time Biosurveillance Program: A Pilot Project Countries: India and Sri Lanka
Research Institution: LIRNEasia*
Address of Research Institution: 12 Balcombe Place, Colombo 08, Sri Lanka Name(s) of Researcher/Members of Research Team:
Vinya Ariyaratne1, Ashok Jhunjhunwala2, K. Vijayraghavan3, Artur Dubrawski4, Gordon Gow5, Nuwan Waidyanatha6
Contact Information of Researcher/Research Team members:
1

Lanka Jathika Sarvodaya Shramadana Sangamaya, web: www.sarvodaya.org, email: [email protected] IITM's Rural Technology and Business Incubator, web: www.rtbi.in, email: [email protected] 3
National Center for Biological Sciences, web: www.ncbs.res.in, email: [email protected] 4
Carnegie Mellon University's Auton Lab, web: www.autonlab.org, email: [email protected] 5
Faculty of Extensions, University of Alberta, web: www.ualberta.ca, email: [email protected] 6
LIRNEasia, web: www.lirneasia.net, email: [email protected] 2

This report is presented as received from project recipient. It has not been subjected to peer review or other review processes.
*

LIRNEASIA IS A REGIONAL INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) POLICY AND REGULATION THINK TANK ACTIVE ACROSS THE ASIA PACIFIC. TO THAT END, LIRNEASIA ENDEAVORS TO CATALYZE THE TRANSFORMATION OF GOVERNANCE AND REGULATION OF ICTS IN THE EMERGING ASIA PACIFIC REGION FROM OBSTRUCTIVE, INHIBITING REGIMES, INTO ONES THAT WILL ALLOW OPPORTUNITIES FOR PEOPLE TO USE ICTS IN WAYS THAT WILL IMPROVE THEIR LIVES. OUR IMMEDIATE PRIORITY IS BUILDING A TEAM OF ASIA PACIFIC ICT POLICY AND REGULATORY PROFESSIONALS THAT CAN WORK ON EQUAL TERMS WITH THE BEST IN THE WORLD.

RTBP Final Technical Report v1.0

IDRC Grant 10530-001

Table of Contents
1 Abstract...............................................................................................................................................13 1.1 Keywords................................................................................................................................15 2 Research Problem...............................................................................................................................15 3 Objectives............................................................................................................................................17 4 Methodology.......................................................................................................................................18 4.1 Proposed evaluation method...................................................................................................18 4.2 Revised evaluation method.....................................................................................................20 5 Project Activities.................................................................................................................................22 6 Project Outputs....................................................................................................................................27 6.1 User Requirements Specification (URS)................................................................................27 6.2 Software Requirement Specifications (SRS)..........................................................................28 6.3 Technology Products...............................................................................................................28 6.3.1 mHealthSurvey ..........................................................................................................28 6.3.2 Sahana Biosurveillance Module (BSM).....................................................................29 6.3.3 T-Cube Web Interface (TCWI)...................................................................................30 6.3.4 Sahana Alerting Broker (SABRO).............................................................................31 6.4 User Manuals and Standard Operating Procedures................................................................32 6.5 Evaluation Toolkit and Guide.................................................................................................32 6.6 Training and awareness workshops........................................................................................32 6.7 Policy Briefs...........................................................................................................................33 6.8 Book Chapters........................................................................................................................33 6.9 Journal articles........................................................................................................................33 6.10 Research papers (Conference/Symposium proceedings)......................................................34 6.11 Lectures & Keynote..............................................................................................................36 6.12 Media....................................................................................................................................36 7 Project Outcomes................................................................................................................................37 7.1 Going Beyond the pilot...........................................................................................................37 7.1.1 Overcoming the resistance in Sri Lanka....................................................................37 7.1.2 Towards scaling in Sri Lanka.....................................................................................37 7.1.3 Early adopter of RTBP - India....................................................................................38 7.1.4 Weak desire to push forward......................................................................................38 7.2 Needed capacity improvements for future adaptations..........................................................39 7.2.1 Discrepancies of data quality impacting analytics.....................................................39 7.2.2 Feedback on the mHealthSurvey technology.............................................................41 7.2.3 Automated event detection.........................................................................................42 7.2.4 Disseminating adverse events for early response.......................................................44 7.3 Gender and age specific findings............................................................................................47 7.3.1 Hospital visitation patterns.........................................................................................47 7.3.2 Chronic (life-style) diseases.......................................................................................47 7.3.3 Empowering the Nurse...............................................................................................49 Waidyanatha

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7.4 Economic analysis..................................................................................................................49 8 Overall Assessment and Recommendations........................................................................................51 8.1 Alternative Digitizing Techniques..........................................................................................51 8.2 Health service specific Graphic User Interfaces.....................................................................51 8.3 Personal Health Records (PHR).............................................................................................52 8.4 Ontology for standardizing Epidemiological Data (EpiD).....................................................53 8.5 EHR Security, Privacy, and Trust...........................................................................................53 8.6 Minimize false and missed alarm rates...................................................................................54 8.7 Syndromic surveillance..........................................................................................................54 8.8 Standardized health risk knowledge share..............................................................................55 8.9 Sustainability Models.............................................................................................................55 8.10 Policy and legal changes.......................................................................................................56 8.11 Practical evaluation techniques.............................................................................................56 8.12 eHealth Knowledge sharing..................................................................................................57 9 GLOSSARY OF ACRONYMS AND TERMS...................................................................................59 10 APPENDIX A – Requirements Analyses..........................................................................................62 10.1 Axiomatic Design Framework..............................................................................................62 10.2 Healthcare worker assessment of the ICT system................................................................65 10.3 User Requirement Specifications.........................................................................................66 10.3.1 Overview towards a URS.........................................................................................66 10.3.2 Present day disease surveillance and reporting........................................................66 10.3.3 Derived requirements...............................................................................................87 10.3.4 Functions, Actors, and Roles of envisaged RTBP....................................................87 10.3.5 Anticipated problems................................................................................................89 10.3.6 Optimal set of Inputs, Outputs, and functionality of ICT system............................89 10.3.7 Inventory of Health Facilities...................................................................................95 10.3.8 The proposed hospital re-categorization................................................................106 10.3.9 The nomenclature of hospital to be changed..........................................................106 10.3.10 ICD 10 Examples ................................................................................................109 11 APPENDIX B – Software Requirement Specification....................................................................117 11.1 Shana Biosurveillance Case Management Module and Database......................................117 11.1.1 Overview................................................................................................................117 11.1.2 Network Architecture.............................................................................................118 11.1.3 Software Architecture.............................................................................................120 11.1.4 Main Scenarios.......................................................................................................123 11.1.5 General guidelines to GUI controls design............................................................128 11.1.6 Guidelines for database..........................................................................................128 11.1.7 Usability of software components..........................................................................129 11.1.8 Core Object Functionality......................................................................................130 11.1.9 Location..................................................................................................................131 11.1.10 Service..................................................................................................................134 11.1.11 Person...................................................................................................................140 11.1.12 Facility..................................................................................................................146 11.1.13 BSM Module Functionality..................................................................................150 11.1.14 Diagnosis..............................................................................................................153 Waidyanatha

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11. References....................................................................................................................165 11.2 Mobile health applications software requirements.............................................................167 11.2.1 Overview................................................................................................................167 11.2.1 Functionality...........................................................................................................168 11.2.2 References..............................................................................................................183 11.3 T-Cube Web Interface (statistical data mining & visualization tools)................................184 11.3.1 Overview................................................................................................................184 11.3.2 Product perspective................................................................................................184 11.3.3 User characteristics.................................................................................................184 11.3.4 Requirements..........................................................................................................184 11.3.5 Constraints..............................................................................................................185 11.3.6 Definitions, Abbreviations......................................................................................186 11.3.7 Functionality...........................................................................................................186 11.3.8 Semi-synthetic data................................................................................................187 11.3.9 Time series analysis................................................................................................187 11.3.10 Panels....................................................................................................................188 11.3.11 Sample outbreaks..................................................................................................191 11.3.12 Spatial data analysis.............................................................................................191 11.3.13 Spatial data visualization......................................................................................191 11.3.14 Multivariate Bayesian spatial scan (MBSS).........................................................192 11.3.15 Sample outbreaks.................................................................................................193 11.3.16 Pivot tables...........................................................................................................195 11.3.17 Ongoing User Interface Work...............................................................................195 11.3.18 References............................................................................................................197 11.4 Common Alerting Protocol enabled Sahana Alerting Broker.............................................204 11.4.1 Overview................................................................................................................204 11.4.2 Objectives...............................................................................................................205 11.4.3 RTBP Alerting and Notification Subsystems.........................................................205 11.4.4 Message creation and validation............................................................................206 11.4.5 Message distribution...............................................................................................208 11.4.6 Message delivery....................................................................................................209 11.4.7 Message acknowledgement....................................................................................211 11.4.8 Message system administration..............................................................................211 11.4.9 Message Attributes.................................................................................................212 11.4.10 Alert format..........................................................................................................212 11.4.11 Message Prioritization with the HazInfo Project..................................................221 11.4.12 Auto Generate the Message Attribute {Description}...........................................223 11.4.13 Required Software Components...........................................................................223 11.4.14 Message Editor.....................................................................................................224 11.4.15 Delivery Configuration.........................................................................................224 11.4.16 Transport Gateway...............................................................................................225 11.4.17 Use cases of the Alert and Notification system....................................................225 11.4.18 Entity Relationships.............................................................................................229 11.4.19 Recommendations for Transport Technology Data Structure..............................231 11.4.20 Mapping of Delivery to Technologies..................................................................233 Waidyanatha

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11.4.21 Delivery/Receipt User stories with Examples.....................................................233 12 APPENDIX C- User reference documents.....................................................................................237 12.1 mHealthSurvey user manual...............................................................................................237 12.1.1 Introduction............................................................................................................237 12.1.2 How to use this Application...................................................................................238 12.1.3 Server connection ..................................................................................................238 12.1.4 Profile....................................................................................................................239 Save – To save the entered data to server...........................................................................239 12.1.5 Error and Exception on Profile...............................................................................240 12.1.6 Location..................................................................................................................240 12.1.7 Health Survey.........................................................................................................241 12.1.8 Offline Survey........................................................................................................244 12.1.1 Basic Instructions...................................................................................................245 12.2 TCWI user manual..............................................................................................................247 12.2.1 1. Introduction........................................................................................................247 12.2.2 Time Series Analysis..............................................................................................248 12.2.3 Loading External Data (Advanced Usage).............................................................249 12.3 Query Selection Panel.........................................................................................................250 12.3.2 Visualization of Time Series..................................................................................252 12.4 Analysis Panel.....................................................................................................................254 12.4.1 Time Series Modeling and Forecasting Functions.................................................255 12.4.2 Temporal Anomaly Detection Functions ...............................................................257 12.4.3 Saved Queries List Panel........................................................................................263 12.4.4 Spatio-Temporal Analysis......................................................................................264 12.4.5 Attribute Selection Panel........................................................................................265 12.5 Spatial Scan........................................................................................................................265 12.5.1 Summarization of Data with Pivot Tables..............................................................267 12.5.2 Future Work............................................................................................................270 12.6 Sahana Alerting Broker user manual..................................................................................271 12.6.1 Introduction............................................................................................................271 12.6.2 Messaging/Alerting Module...................................................................................271 12.6.3 Templates................................................................................................................272 12.6.4 Alerts......................................................................................................................275 12.7 4.2 View Alerts...................................................................................................................283 12.7.2 Resources...............................................................................................................286 12.8 mHealthSurvey standard operating procedures..................................................................287 12.8.1 Acquiring a mobile phone......................................................................................287 12.8.2 Getting connected to the network...........................................................................287 12.8.3 Installing the mHealthSurvey.................................................................................288 12.8.4 Initializing the mHealthSurvey..............................................................................288 12.8.5 Submitting Patient records.....................................................................................288 12.8.6 Application maintenance........................................................................................289 12.8.7 Mobile Phone maintenance....................................................................................289 12.8.8 Reporting Bugs and problems................................................................................289 12.8.9 Contact information................................................................................................289 Waidyanatha

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12.9 TCWI standard operating procedures.................................................................................290 12.10 SABRO standard operating procedures............................................................................297 12.10.1 ALERT AND SITUATIONAL-AWARENESS MESSAGING...........................297 12.10.2 Training................................................................................................................298 12.10.3 Prerequisites.........................................................................................................298 12.10.4 Initializing messaging templates..........................................................................299 12.10.5 Establishing recipients and groups.......................................................................300 12.10.6 Creating a CAP Alert and situ-aware messages...................................................301 12.10.7 Delivering the Alert and Situ-Aware messages....................................................305 12.10.8 Reporting bugs and problems...............................................................................306 12.10.9 Contact information..............................................................................................306 12.11 Quick Reference Guide – mHealthSurvey........................................................................307 12.11.1 Mobile Phone SPECIFICATIONS.......................................................................307 12.11.2 Enable INTERNET Access..................................................................................307 12.11.3 INSTALL Software..............................................................................................307 12.11.4 SETUP your profile and locations........................................................................308 12.11.5 SUBMIT outpatient and inward records..............................................................309 12.11.6 OFFLINE Survey.................................................................................................311 12.11.7 SUPPORT Services..............................................................................................311 12.12 TCWI quick reference guide............................................................................................312 12.12.1 How to load data sets...........................................................................................312 12.12.2 How to query the data in T-cube..........................................................................312 12.12.3 How to navigate the time series panel..................................................................313 12.12.4 How to navigate map panel..................................................................................314 12.12.5 How to do pre-defined screening.........................................................................314 12.12.6 How to run pre-configured pivot table (report)....................................................316 12.12.7 How to run analytical method..............................................................................317 12.13 SABRO quick reference guide.........................................................................................322 12.13.1 Accessing the Messaging/Alerting Module..........................................................322 12.13.2 Create New Alert.................................................................................................323 12.13.3 Issuing an Alert.....................................................................................................325 12.13.4 Select Contact.......................................................................................................326 12.13.5 Select delivery type..............................................................................................326 12.13.6 Send Message.......................................................................................................327 12.13.7 Updating and resending alert................................................................................327 12.13.8 View Web Alerts...................................................................................................328 13 Appendix D – Policy briefs for India and Sri Lanka.......................................................................329 13.1 Indian brief..........................................................................................................................329 13.2 Sri Lanka brief....................................................................................................................331

Index of Tables
Table 1: Activities and description (implementation and management) with timeliness.........................15 Table 2: Customer Attributes...................................................................................................................52 Waidyanatha

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Table 3: Functional Requirements...........................................................................................................52 Table 4: Constraints..................................................................................................................................53 Table 5: Design Parameters......................................................................................................................53 Table 6: Incidence matrix of functional requirements and design parameters.........................................54 Table 7 Government health organizational structure actors with their roles and responsibilities............57 Table 8: List of notifiable diseases in Sri Lanka and the notification mode............................................57 Table 9: Healthcare facilities governed by the MOH in Kurunegala District..........................................58 Table 10 Average time taken to complete each leg of the information flow...........................................61 Table 11: H-544 from data entry Competed by General Practitioner/House officer/Senior Health Officer/Consultant and sent to MOH.........................................................................................61 Table 12: H-411 form data entry completed by PHI and sent to MOH...................................................62 Table 13: H-411a form data entry completed by MOH/OIC sent to Director of Health Services, with WRCD ......................................................................................................................................63 Table 14 Notification registry data entered and maintained by MOH.....................................................64 Table 15: H-399 form data entry completed by MOH/OIC and sent to DHS, with Communicable Disease Report...........................................................................................................................64 Table 16: Government health system actors with their roles and responsibilities...................................69 Table 17: Healthcare facilities governed by the DDHS in Thirupathur Block........................................69 Table 18: Average time taken to complete each leg of the information flow...........................................71 Table 19 Public Health Center morbidity report entry input attributes....................................................72 Table 20 RTBP ICT system functions, actors, and roles/responsibilities................................................76 Table 21 attributes of visitation data collection from the providers by the Suwacevo and VHN............78 Table 22 Information stored in the database............................................................................................79 Table 23 Analysis done by RAs (or Epidemiologists) of the collected datasets......................................80 Table 24 Weekly reports and urgent alerts issued by RA (Epidemiologist) to all healthcare workers....81 Table 25 Sample of Diagnosis (diseases), symptoms, and signs.............................................................82 Table 26 Attributes associated with the Healthcare Provider identification............................................83 Table 27 Geographical coverage definitions with hierarchy....................................................................83 Table 28 Kurunegala district, Sri Lanka health facility inventory...........................................................83 Table 29 Kurunegala district, Sri Lanka health facility inventory...........................................................84 Table 30 Layers, objects, and descriptions of elements in the layers in Figure 2..................................107 Table 31 description of use cases in Figure 1.........................................................................................118 Table 32 Location category information................................................................................................118 Table 33 Location detail information.....................................................................................................118 Table 34 Location detail information.....................................................................................................119 Table 35 Other object controls quasi modally accessing the location object.........................................119 Table 36 description of use cases in Figure 8........................................................................................121 Table 37 Service category information..................................................................................................122 Table 38 Service type information.........................................................................................................122 Table 39 Service status information.......................................................................................................123 Table 40 Service type item information ................................................................................................123 Table 41 Service item detail information ..............................................................................................123 Table 42 Service item detail information ..............................................................................................124 Table 43 Other GUIs quasi modally accessing the service controls: forms & reports..........................125 Table 44 description of use cases in Figure 1........................................................................................127 Waidyanatha

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Table 45 Person role information...........................................................................................................128 Table 46 Person type information..........................................................................................................128 Table 47 Person state information..........................................................................................................129 Table 48 Person information..................................................................................................................129 Table 49 Other GUIs quasi modally accessing the Person controls: forms & reports...........................130 Table 50 description of use cases in Figure 10......................................................................................132 Table 51 Facility category information..................................................................................................133 Table 52 Facility type information.........................................................................................................133 Table 53 Facility Status information......................................................................................................133 Table 54 Facility information.................................................................................................................134 Table 55 Other GUIs quasi modally accessing the Facility controls: forms & reports..........................134 Table 56 description of use cases in Figure 12......................................................................................138 Table 57 Disease Type information........................................................................................................140 Table 58 Disease information.................................................................................................................140 Table 59 Symptom detail information....................................................................................................141 Table 60 Sign detail information............................................................................................................141 Table 61 Causality Factor detail information.........................................................................................141 Table 62 Other GUIs quasi modally accessing the location object........................................................142 Table 63 description of use cases in Figure 1........................................................................................144 Table 64 Cases detail information..........................................................................................................146 Table 65 Other GUIs quasi modally accessing the location object........................................................147 Table 66: Abbreviations.........................................................................................................................168 Table 67: Daily distribution...................................................................................................................169 Table 68: Cities present in the semi-synthetic data................................................................................170 Table 69: Diseases present in the semi-synthetic data...........................................................................171 Table 70 Summary of message creation options....................................................................................189 Table 71 Suggested CAP elements and example for the delivery types................................................191 Table 72 CAP values for an urgent priority message.............................................................................202 Table 73 CAP values for a high priority message..................................................................................203 Table 74 CAP values for a low priority message...................................................................................203 Table 75 Description of the individual elements of Figure 1.................................................................207 Table 76 Mapping delivery types to technologies displays...................................................................213 Table 77: Example of a contingency table obtained for a single test in the Temporal Scan..................236 Table 78: “CUG-health-dis-outbreak”:..................................................................................................252 Table 79: “PUB-health-dis-outbreak”:...................................................................................................253 Table 80: “MOH-health-dengue-10-08-2009”:......................................................................................258 Table 81: “pub-health-dengue-10-08-2009”:.........................................................................................259 Table 82: Contacts for health workers...................................................................................................265 Table 83: Contacts list for health officials.............................................................................................282

List of Figures
Figure 1: Original proposed comparison-based (quasi-experimental) evaluation framework.................13 Waidyanatha

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Figure 2: Functional components, evaluated layers, and interoperating elements of the RTBP framework..................................................................................................................................15 Figure 3: time series ratios of noisy and clean data.................................................................................32 Figure 4: India - data submission delays..................................................................................................33 Figure 5: Sri Lanka - data submission delays..........................................................................................33 Figure 6: Doctor bias on preference of diagnosis....................................................................................34 Figure 7: Moving average trend of common cold in Thirupathur Block.................................................35 Figure 8: Propagation of common cold in Sivaganga district immediately before and during rainy season.........................................................................................................................................36 Figure 9: India - way health risk information is received now................................................................37 Figure 10: Sri Lanka - way health risk information is received now.......................................................37 Figure 11: India - female hospital visitations over male (purple plot).....................................................40 Figure 12: Sri Lanka - female hospital visitation over male (purple plot)...............................................40 Figure 13: India - logarithmic odds ratio plot for chronic diseases by age groups..................................41 Figure 14: Sri Lanka - logarithmic odds ratio plot for chronic diseases by age groups..........................41 Figure 15: comparison of the present and introduced systems................................................................42 Figure 16: TCO distribution by health facilities, health departments, and health workers.....................42 Figure 17: Domain mapping....................................................................................................................54 Figure 18: Organizational structure of the Sri Lanka Government Healthcare Officials; integer in parenthesis is the number of each entity in the country.............................................................58 Figure 19: Sri Lanka epidemiological information reporting sequence of functions..............................62 Figure 20: Organizational structure of the Indian Government Healthcare Officials.............................70 Figure 21: General State level notification process.................................................................................72 Figure 22: Village health nurse capture of village data............................................................................73 Figure 23: H-544 Form for communicating disease from MOH to PHI (Sri Lanka)..............................87 Figure 24: H-411 form prepared by PHI and communicated to MOH (Sri Lanka).................................89 Figure 25: H-411a form communicated by MOH to regional and national levels (Sri Lanka)...............90 Figure 26: Weekly Epidemiological Report (WER) published on the web by the Epidemiology Unit (Sri Lanka).................................................................................................................................91 Figure 27: PART I - Public Health Center Morbidity Report Entry report (entered through the web). . .92 Figure 28: PART II - Public Health Center Morbidity Report Entry report (entered through the web). .93 Figure 29: PART III - Public Health Center Morbidity Report Entry report (entered through the web).94 Figure 30: SHN-BSM Communication Architecture.............................................................................106 Figure 31: Layers and objects of the complete Biosurveillance product software architecture............108 Figure 32: Edit sequence diagram..........................................................................................................111 Figure 33: Add sequence diagram..........................................................................................................112 Figure 34: Delete sequence diagram......................................................................................................113 Figure 35: View associated information to add, edit, or delete..............................................................114 Figure 36: location use case diagram.....................................................................................................119 Figure 37: service use case diagram.......................................................................................................123 Figure 38: person use case diagram.......................................................................................................128 Figure 39: Facility use case diagram......................................................................................................133 Figure 40: SHN BSM Module Entity Relationship diagram.................................................................137 Figure 41: diagnosis (disease, sign, symptoms, and causality factor) use case diagram.......................139 Figure 42: Class inheritance diagram.....................................................................................................143 Waidyanatha

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Figure 43: Health Cases use case diagram.............................................................................................145 Figure 44: Cases object and the inherited and associated objects..........................................................148 Figure 45: Mobile application initialization and data submission use cases.........................................154 Figure 46: Information flow between mobile phone and database........................................................156 Figure 47: Flow chart for loading data from database...........................................................................157 Figure 48: Health Survey form data flow chart.....................................................................................158 Figure 49: Offline Survey flow chart.....................................................................................................160 Figure 50: Profile form flow chart.........................................................................................................161 Figure 51: Location form flow chart......................................................................................................163 Figure 52: T-Cube web interface............................................................................................................168 Figure 53: Query selection panel...........................................................................................................170 Figure 54: 1.File upload/clear panel.......................................................................................................170 Figure 55: Time series visualization panel.............................................................................................171 Figure 56: Temporal scan analysis.........................................................................................................172 Figure 57: Time series operation panel..................................................................................................172 Figure 58: Alert showing increased activity (upper tail test).................................................................173 Figure 59: Time series massive screening panel....................................................................................174 Figure 60: Food poisoning outbreak in Monaragela..............................................................................175 Figure 61: Leptospirosis outbreak in Colombo......................................................................................175 Figure 62: Viral Hepatitis outbreak in Kandy........................................................................................175 Figure 63: Spatial data visualization of all data on the map..................................................................177 Figure 64: Attribute selection panel under spatial analysis....................................................................177 Figure 65: Spatial data visualization for few cities and diseases...........................................................178 Figure 66: MBSS results for disease Dengue fever on July 3, 2008......................................................179 Figure 67: Food poisoning outbreak around Kurunegala on Aug 13, 2008...........................................180 Figure 68: Leptospirosis outbreak around Colombo on Mar 26, 2008..................................................181 Figure 69: Dysentery outbreak in central Sri Lanka on Apr 22, 2008...................................................182 Figure 70: Pivot table for semi synthetic data........................................................................................183 Figure 71: Leptospirosis disease counts in Colombo using pivot table.................................................184 Figure 72: Alert and Notification subsystems with inputs and outputs.................................................187 Figure 73: Rendering a CAP-XML message for end-user devices........................................................190 Figure 74: Software components of the EDXL/CAP multi-transport sub-module................................203 Figure 75: Use case diagram for EDXL/CAP enabled alert and notification........................................205 Figure 76: Entity relationship diagram for the CAP sub-module schema.............................................208 Figure 77: Main menu of the application...............................................................................................217 Figure 78: Profile registration form.......................................................................................................218 Figure 79: Location(s) retrieval form.....................................................................................................219 Figure 80: Health Survey Form one (demographics).............................................................................220 Figure 81: Health Survey Form two (Diagnosis)...................................................................................221 Figure 82: Search and select symptoms.................................................................................................222 Figure 83: Front panel of the T-Cube Web Interface.............................................................................227 Figure 84: Starting view of the Query Selection Panel..........................................................................229 Figure 85: List view of the Query Selection Panel................................................................................230 Figuer 86: Tile view of Query Selection Panel......................................................................................230 Figure 87: Visualization of time series...................................................................................................231 Waidyanatha

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Figure 88: Changing the appearance of the time series plots.................................................................232 Figure 89: Analysis Panel......................................................................................................................233 Figure 90: Example result of executing temporal scan on Colombo dengue fever data. ......................236 Figure 91: Saved Queries List Panel......................................................................................................239 Figure 92: Inspecting one result of the massive screening with temporal scan....................................240 Figure 93: Zooming in on the results from Figure 10............................................................................240 Figure 94: Drill down using another run of massive screening reveals that people older than 45 years seem to be the age group most spectacularly affected by the June 2009 viral hepatitis outbreak in Vavuniya..............................................................................................................................241 Figure 95: Map visualization.................................................................................................................242 Figure 96: Time series view under the Map tab.....................................................................................242 Figure 97: Attribute Selection Panel under the Map tab........................................................................243 Figure 98: Selecting the Spatial Scan parameters..................................................................................243 Figure 99: Detecting an outbreak of Leptospirosis with Spatial Scan (map view)................................244 Figure 100: Detecting an outbreak of Leptospirosis with Spatial Scan (time series view)...................244 Figure 101: An example 2-way Pivot Table...........................................................................................245 Figure 102: Example 3-way pivot table.................................................................................................246 Figure 103: Example Time Series view under Pivot Table tab..............................................................246 Figure 104: Example pie chart under the Pivot Table tab......................................................................247 Figure 105: Create template...................................................................................................................249 Figure 106: View list of templates.........................................................................................................249 Figure 107: View template.....................................................................................................................250 Figure 108: Upload template message...................................................................................................251 Figure 109: New alert: select mode.......................................................................................................252 Figure 110: New alert: enter name and type..........................................................................................253 Figure 111: Alert metadata.....................................................................................................................253 Figure 112: Create CAP alert tab...........................................................................................................254 Figure 113: Create CAP - Information tab.............................................................................................255 Illustration 114: Create CAP – Information tab showing message information....................................256 Figure 115: Create CAP - location lookup.............................................................................................257 Figure 116: Create CAP - add new location...........................................................................................257 Figure 117: View list of alerts................................................................................................................259 Figure 118: View alert............................................................................................................................260 Figure 119: Select contact......................................................................................................................260 Figure 120: Select delivery type............................................................................................................261 Figure 121: View converted message and send.....................................................................................261 Figure 122: Expanded Query Panel with attributes...............................................................................267 Figure 123: Time Series display showing saved query and temporal scan results with Analysis Panel .................................................................................................................................................267 Figure 124: Loading the map for spatial scan........................................................................................268 Figure 125: Spatial Scan attribute selection panel.................................................................................268 Figure 126: Spatial scan score and time series plot for disease under investigation.............................269 Figure 127: Spatial Scan map with counts and scores of disease under investigation..........................269 Figure 128: Massive Screening Panel setting of parameters and values...............................................270 Figure 129: Pivot Table with disease in rows and locations in columns with counts in cells................271 Waidyanatha

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Figure 130: Pivot Table for selected disease split by age group and locations......................................271 Figure 163: File upload panel................................................................................................................288 Figure 164: Query panel........................................................................................................................288 Figure 165: Advance query....................................................................................................................289 Figure 166: Time series panel................................................................................................................289 Figure 167: Map panel...........................................................................................................................290 Figure 168: Predefined screening panel.................................................................................................291 Figure 169: Time series highlighted with alert and most significant p-values......................................291 Figure 170: Pivot table panel.................................................................................................................292 Figure 171: Sample pivot table report....................................................................................................292 Figure 172: Statistical estimations panel................................................................................................293 Figure 173: Sample output of moving average......................................................................................293 Figure 174: Temporal Scan parameters..................................................................................................294 Figure 175: Sample temporal scan output..............................................................................................294 Figure 176: CuSum parameters..............................................................................................................294 Figure 177: Sample CuSum output........................................................................................................295 Figure 178: Spatial scan parameters......................................................................................................295 Figure 179: Sample spatial scan results.................................................................................................295 Figure 180: Sample plot of Temporal Scan on map...............................................................................296 Figure 181: Temporal scan detects fever cases in a location.................................................................296

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1 ABSTRACT
The Real-Time Biosurveillance Program (RTBP) was a multi-partner initiative to study the potential for new Information and Communication Technologies (ICTs) to improve early detection and notification of disease outbreaks in selected regions of Sri Lanka and India. Experts in the field of biosurveillance and health informatics have argued that improvements in disease detection and notification can be achieved by introducing more efficient means of gathering, analyzing, and reporting on data from multiple locations. New ICTs are regarded as an important means to achieve these efficiency gains. The primary research objective of RTBP was to examine these claims more closely by producing evidence to indicate in what ways and to what extent the introduction of new ICTs might achieve efficiency gains when integrated with existing disease surveillance and detection systems. Under the current disease surveillance systems in Sri Lanka and India, patient data from regional and community health centers continues to be gathered largely through paper-based forms and procedures. These forms are then sent to regional health officials where data analysis is carried out by qualified staff to identify potential disease outbreaks. Notifications are then issued from the regional health administrations to local authorities, again using paper-based reporting methods. The RTBP substituted or complemented each of these existing procedures with ICT-based components. Patient records were gathered using software application implemented on mobile phones and transmitted to a central server using commercial cellular data services. Patient data were drawn from the central server and its statistical analysis for any significant trends that might be indicative of emerging threat to public health was carried out using advanced software developed by Carnegie Mellon University’s Auton Lab. Results were then made available to regional and local health officials as electronic notifications accessible through a variety of devices, including mobile phones. The project achieved a number of key objectives at the outset, including the development of a Javabased application for collecting patient data using low cost mobile phones; the successful implementation of Auton Lab’s analytic software and T-Cube Web Interface for analyzing patient records and near real-time prediction of disease outbreaks; and the adoption and implementation of Common Alerting Protocol for multi-channel health alerting. Moreover, the project team successfully integrated each of these three key components into an operational system that collected individual patient records, over 330,000 in Sri Lanka and over 130,000 in India, over a 15 month course of study. Over the life of the project, the system identified over a dozen instances of potential disease outbreaks, with four of those (Chicken Pox, Acute Diarrheal Disease, Respiratory Tract Infection, and Mumps) being confirmed by health authorities. The project demonstrated that new ICTs can dramatically reduce turnaround time for outbreak detection and alerting, from current period of weeks to a matter of days or even hours. The project also demonstrated the feasibility of using low cost mobile phones and existing commercial cellular infrastructure and services to enable affordable, real-time reporting of patient records from frontline health centers.

The project also identified a number of existing and ongoing challenges with the implementation of each of the components of the RTBP. It was found, for example, that the standard mobile phone numeric key pad is not the most efficient means of digitizing patient records. Frontline health workers Waidyanatha

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found it difficult to use, particularly when entering large numbers of records. Many records entered into the system also contained errors that could likely be eliminated through improved user interface design and application on the mobile phone. Future research in this area should investigate innovative data entry methods such as optical character recognition, quick response (QR) codes, or touch screen interfaces in order to optimize efficiency and accuracy of data entry at frontline health centers, with the consideration that such methods are suited to implementation on low cost devices, as well as study the utility of post-collection data analyses tailored to detect entries suspicious of human error, to enable scalable and agile data quality assurance

The utility of the T-Cube Web Interface detection analyses proved to be more than simply detecting disease outbreaks. Health officials involved with the project indicated that it could be a useful tool to support long term planning and allocation of health resources as well as identifying its ability to monitor a wide range of health concerns that affect regional and national health planning. The project realized that the reliability of detection analyses was predominantly affected by the low quality data that is a consequence of the doctors’ bias in preliminary diagnosis and inconsistencies in the terminology. In addition, to become widely accepted, the tools must be compatible with pre-existing processes and with the intuition of the expert users. Achieving this goal required a few iterations. Health officials who have been using TCWI, had initially been offered a generic user interface with programmable statistics and queries, and they found it relatively difficult to use, given that they did not have a strong background in statistics and were technically poor in operating the tool with training typically involving a couple of iterations of 4 hours each time. Thereafter, the designers customized the TCWI eliminating the underlying complexities and simplifying execution of majority of usage scenarios often to just a click of a button. This approach proved to be effective. Experience from working with health officials suggests that future implementations of TCWI will need to be customized to the individual implementation with the health department's requirements and a more rigorous training and certification program would need to be developed and introduced for senior health officials to ensure its effective use for disease surveillance. The licensing cost of TCWI may seem relatively high when accounted for a handful of users. However, is less than 5% of the entire RTBP total cost of ownership when compared with national level implementations, and the attainable benefits in timeliness and accuracy of disease outbreak detection and identification grossly outweigh the acquisition and maintenance costs. It should be noted that TCWI has not only been found useful in its primary area of application – detection and characterization of emerging diseases. We have shown its utility in monitoring dynamics of chronic conditions such as diabetes or high blood pressure, in resource allocation planning, and we identified opportunities in other areas such as agriculture. Future work on TCWI should explore those opportunities, as well as target configurability and usability of the tool perhaps, among other means, with the use of machine learning to incorporate user feedback in learning improved event detection models throughout the system lifetime. Downstream alerting of health knowledge in relation to adverse events, with field health workers, is almost nonexistent in the current practices of both countries. To fill this gap RTBP was successful in developing and testing a health alerting component using the Sahana Alerting Broker. Sahana is a Free and Open Source Software (FOSS) collection of disaster management modules that work as a platform for integrating multi-organization response efforts in providing critical information to communication needs of the responders.

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A series of staged tests revealed that alerting messages could be sent to frontline health care workers by means of mobile phone or email, as well as other means as necessary. Message protocols used Common Alerting Protocol with a specific adaptation of that standard based on a protocol used by US Centres for Disease Control for health alerting. Our research also discovered that the alerting component was being used by health officials to meet other messaging requirements in order to improve efficiencies in routine operations. This finding suggests the potential value of introducing a more general purpose health notification system using mobile phone text messaging. Future research in health alerting and notification should examine possibilities for integrating a national system with regional or international alert systems in order to enable timely notification of concerns across borders in the region.

In terms of institutional support for the RTBP, the project faced various challenges in both Sri Lanka and India. Much of the initial support came from the assistance of the community-based organization Sarvodaya. However, with early demonstration of success, government health officials in Sri Lanka increasingly began to show support for the project. For example, the Sri Lankan Health Ministry has secured internal resources and private partnerships to expand the RTBP into two districts in order to examine the scalability of the system in anticipation of further expanding it to a national scale. While the State of Tamil Nadu government in India was an early adopter and supporter of the project, its ongoing interest in the project going forward is uncertain.

Overall results from our work demonstrate the feasibility of introducing an RTBP from a technical and operational standpoint. Initial findings show significant efficiency gains in terms of disease reporting, outbreak detection, and health alerting; with cost savings over 35% in both countries when compared to the existing systems. However, further research is needed to better understand the challenges associated with scaling such a system up to a regional or national level of implementation. In particular, further work needs to be done to optimize data entry over low cost mobile devices, to address usability and training requirements for the analytics platform, and to continue to enhance and integrate health alerting into national and regional systems and practices. Moreover, extensive stakeholder consultation will be necessary to ensure the various policy, legal, and operational implications of a national or regional RTBP are better understood, addressed, and effectively managed in the future.

1.1 Keywords
public health, epidemiological surveillance, mobile phones, statistical data mining algorithms, spatiotemporal visualization tools, common alerting protocol, policy research

2 RESEARCH PROBLEM
Research Question: “Can software programs that analyze health statistics and mobile phone applications that send and receive the health information potentially be effective in the early detection and mitigation of disease outbreaks?”

The existing disease surveillance and notification systems in India and Sri Lanka were introduced a century back but were revised over time, prompted by the emergence of epidemics such as yellow fever, SARS, and H1NI. The two countries' emphasis is on monitoring around twenty-five reportable Waidyanatha

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infectious diseases (termed notifiable diseases). This legal requirement is a slow and labor-intensive process that sees several weeks pass before epidemiologists receive aggregates of this handful of diseases for any kind of analysis (Appendix A section 10.3.2). The limitations have led to human and socio-economic losses: Leptospirosis outbreak in Sri Lanka 1, Chikungunya in Tamil Nadu2, and chronic kidney disease3. An unusual number of patients presenting with similar symptoms concentrated in a particular geographic areas could have signaled epidemiologists of an abnormal event and may have effectively mitigated their consequences. In addition, lifestyle or non-infectious diseases like diabetes, hypertension, asthma, and arthritis are affecting national health budgets and loss of household productivity in developing countries (Sampath et al, 2010).

There are already significant indications of disease burden occurring in the world as a result of climate change and population increase; there are many causative factors including infective agents like bacteria and viruses. What would happen if these microorganisms mutated and reappeared in a different form in India or Sri Lanka? The disease could be carried into the country by two individuals who arrive from overseas but live in two different parts of the country. Is the present system able to distinctly identify the outliers with possibly similar symptoms reported by two different healthcare workers in the different areas? If not, then it is important that the infected cases with similar symptoms be centrally detected immediately before a mass contamination and spreading takes place. Another significant risk factor that needs attention is the emergence of novel and pandemic viruses; the frequent travel/migration of people to and from the areas which are currently affected by the influenza, known to be highly virulent and mutable viruses. To add to the complexity the symptoms (fever, cough, respiratory tract infection, and pain) may be common enough to be considered unimportant and be neglected in tropical habitats. However, an unusual increase in a geographic area is alarming to epidemiologists. Certainly, a human being does not have the capacity or diligence to search through all the hospital information strings to identify clusters of similar patterns in spatially distributed data sets. Statistical analysis of large datasets is time-consuming. A surveillance system must be ready to detect changes in complex, multivariate data very quickly (ideally, in real-time), while maintaining the ability to test a huge number of hypotheses regarding geospatial co-locations, temporal correlations of the individual cases and their demographic characteristics, in order to detect a possible outbreak. That requires advanced algorithms for detection of abnormal patterns, which would efficiently handle large sets of multivariate data and effectively signal statistically significant departures from the expected. Advances in algorithms, data structures and artificial intelligence allow for practical applications of data-driven outbreak detection methods that can handle the complexity of the task at hand by learning from examples in historical data and from real or simulated outbreaks recorded in it4. 1

Agampodi, S., Somaratne, P., Priyantha, M., Peter, M. (2008). An interim report of Leptospriosis outbreak in Sri Lanka – 2008, publication of the Epidemiology Unit of Sri Lanka.
2
Ganesan, M., Prashant, S.., Janakiraman, N., and Waidayanatha, N., (2010), Real-time Biosurviellance Program: Field Experiences from Tamil Nadu, India. Health, Poverty and Human Development, Indian Association for Social Sciences and Health (IASSH) (in press)

3
Siriwardana, C. (2008, August 12). Sri Lanka Kidney disease epidemic leaves doctors baffled, world wide web news article, Science and Development Network.
4
Neil, D. and Moore, A. (2006). “Methods for Detecting Spatial and Spatio-Temporal Clusters”, In Handbook of Biosurveillance, M. Wagner, A. Moore, and R. Aryel, Eds. London: Elsevier Academic Press, 2006, pp. 243-254. Waidyanatha

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Socio-economists see that the key problem is not software but accurate and timely entry of data by healthcare workers. Therefore, the project had extended the user interfacing to the last-mile by using cellular networks that span the nation as opposed to waiting for broadband connectivity to be widely available. The belief is that the introduction of Wireless Local Loop (WLL) applications, as opposed to traditional computer desktop applications with broadband connections, will increase the likelihood of early detection and warning of communicable diseases; the demand-side ICT market study “Teleuse on a Shoestring5” reveals that coverage of the WLL market is far beyond that of the fixed line market in India and Sri Lanka. It is hoped that adopting data acquisition software applications that work on mobile phones will reduce the latencies in communicating epidemiological information. In both countries, the current paper-based system does not feed the infectious disease information to central processing in a timely manner. It is evident from the Weekly Epidemiology Report (WER) published by the epidemiology unit in Colombo that reporting is lagging by 3-4 weeks. The lag is not caused by the time taken to edit the information to fit the template, but rather because the Integrated Disease Control Nurses in the Districts lag in processing the statistics and delivering timely information up the chain through the paper-based system. Similarly, the Integrated Disease Surveillance and Program (IDSP) manually collects data and uses spreadsheets to analyze them once again using humans who only look for weekly aggregates of known diseases and symptoms that may reach or surpass the thresholds. Investigating data a week later may not be fast enough to reveal an escalating disease burden that may have reached the tipping point.

3 OBJECTIVES
“The problem that this program promised to solve was to strengthen existing disease surveillance and detection communication systems by reducing latencies in detecting and communicating disease information and set a standard interoperable protocol for disease information communication with National and International Health Organizations in the region” List of the objectives in point form (all have been completed) 

Deploy a human-centric mobile phone sensor system for gathering health-related information through healthcare workers from clinics and hospitals

Train the healthcare workers to use the technology and adopt processes for submitting outpatient disease and syndrome information

Evaluate the usability, adaptability, and effectiveness of the mobile phone-based data acquisition process

Deploy advanced detection software algorithms such as spatio-temporal scanning, Bayesian modeling, and multivariate time series analysis for statistical data mining for State/Regional

5

Zainudeen, A. and De Silva, H. (2007). Beyond Universal Access. CPRsouth: Research for improving ICT governance in the Asia-Pacific, Manila, Philippines.
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Epidemiology Units to detect disease outbreaks

Train the state/regional epidemiology units with the tools and processes to analyze the gathered health-related data to detect disease outbreaks in near real-time

Evaluate the ability of the detection system to assist the national/state/regional epidemiology units with their task of discovering outbreaks ahead of time, efficiently carrying out the analysis in a timely manner, reliably predicting disease outbreaks with minimal ambiguity, and interpreting the analyzed information with zero complexity,

Deploy a disease outbreak notification software tool for state/regional epidemiology units to use for notifying divisional and community healthcare workers of a possible disease outbreak as well as monitor the situations with feedback reports on the response actions

Train the state/regional epidemiology units on the software tool and processes in notifying possible disease outbreak as well as instructions on protocols  Evaluate the notification system for its usability, reliability, and effectiveness in managing communications with healthcare workers during and emerging disease outbreak emergency situation

Disseminate the outcome of the research to policy makers, practitioners, and researchers to study the lessons learned

The overall intention of the project is to develop a complete real-time detection and warning system, comprised of four components:
1. establish the electronic communication system
2. introduce computer based detection and monitoring
3. implement the RTBP
4. evaluate the biosurveillance program

4 METHODOLOGY
The methodology, evaluative tools, and outcomes of the RTBP are discussed in the supplemental document titled, “An evaluation toolkit and guide for a Real-Time Biosurveillance Program” (abbreviated as eval-toolkit-n-guide), which will be published in an open access domain along with this technical report. Readers are encouraged to refer to the supplemental document to get an in-depth knowledge of the evaluations although this document will brief on the methodology and outcomes for the sake of completeness. There may be minor overlaps between the two documents.

4.1 Proposed evaluation method
The original design of the proposed RTBP research matrix was to investigate the divergence of the outcomes between groups that were exposed to the RTBP and those who were not (unexposed) as well as to cross segregate those groups to ones that were well-organized with a community health facility and those who were less organized without a community-based health facility, shown in Figure 1. In Figure 1, ‘H’ denotes healthcare worker, and ‘C’ denotes community. The cells in magenta are the organized communities with the presence of a community-based healthcare facility (row denoted with Waidyanatha

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+ symbol) and the cells with orange background are the communities that do not have a formal community-based healthcare facility (rows denoted with a - symbol). Basically, each healthcare worker (yellow cells) will cover an organized community and a less organized community.

Figure 1: Original proposed comparison-based (quasi-experimental) evaluation framework

This approach was practically unaccomplished for a few reasons: 1) There was no formal involvement of a community-based organization in India. 2) Only one epidemiological center (IDSP Unit) participated in the project in Sri Lanka, as opposed to two in India.

3) Resources were concentrated in proving the concept of the workability of the technology-based program.
4) Resources were inadequate to document subjective evidence in the villages or communities to test the hypotheses.
5) Complexities and expenses of practically conducting a comparison-based or quasi-experiential evaluation were too large.
6) Non-uniformity of utilization and services between two comparative health facilities made it difficult to normalize the evidence.
7) Collapse of the Sri Lankan government at the early stages caused the project to divert resources to fill the deficit of digitizing health records, which was initially anticipated to be done by government healthcare workers.

Hypotheses based on the original proposed research design as in Figure 1 1. . Healthcare workers in divisions 1 & 2, exposed to the RTBP, will respond more effectively in communicating disease to the respective epidemiology center than the healthcare workers in the control divisions 3 & 4, who were unexposed to the RTBP. 2. Epidemiology units in divisions 1 & 2, exposed to the RTBP, will detect disease outbreaks accurately and contain the outbreak more efficiently than epidemiology units in control divisions 3 & 4, who were unexposed to the RTBP. 3. Healthcare workers and epidemiology units in divisions 1 & 2, exposed to the RTBP, will show interest and recognize the benefits in adopting e-Health programs, as opposed to the healthcare workers and epidemiology units in the control divisions 3 & 4, who were unexposed to the RTBP.

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4. Communities in divisions 1 & 2, exposed to the RTBP, will have more confidence in national disease surveillance and notification programs than the communities in the control divisions 3 & 4, who were unexposed to the RTBP.

5. Healthcare workers and epidemiology units in divisions 1 & 2, exposed to the RTBP, will, in addition to their RTBP function, leverage ICTs in other areas and thereby enrich their daily activities more than the healthcare workers and epidemiology units in the control divisions 3 & 4, who were unexposed to the RTBP. 6. Communities that have non-governmental community-based healthcare organizations will perform better in monitoring, communicating, and containing disease outbreaks than communities that do not have a formal non-governmental community-based healthcare organization.

4.2 Revised evaluation method
Nevertheless, the evaluation framework was revised to one that was objective-based (determine whether the RTBP under study met the design requirements), decision facilitation (resolve issues important to developers and administrators for individuals to make decisions about the future of the RTBP), quasi-legal (establish mock drills, exercises, certifications, and other formal adversary proceeding to judge the RTBP), professional reviews (regular site-visit approach with researchers spending time with the users in the field), and responsive (seek to represent the viewpoints of those who were users of the RTBP; to not be judgmental but be illuminated). Thus, the essence of the RTBP was a formative study aimed at producing insight as to the effectiveness of the component or subsystems and identifying potential improvements to those components (Friedman and Wyatt, 2006)6. The overall vision and strategy for evaluating the RTBP was adopted from the literature by Ammenwerth et al (2004)7. General methods for subjective and objective quantitative and qualitative evaluation of bioinformatics systems were introduced by Friedman and Wyatt (2006). More specific to RTBP, Lewis (2003)8 and Wagner (2008)9 have proposed biosurveillance system and public health informatics evaluation methods with a broad set of evaluation criteria on the usability of the technology, effect on structural or process quality and social consequences of introducing the technology. Anderson and Aydin (2005)10 describe methods and key aspects of qualitatively evaluating the organizational impact of introducing ICTs in healthcare. To perform the economic analyses to compare the existing paper-based system with the RTBP introduced technology system, the researchers calculated the investments, specifically as the total cost of ownership, and then analyzed the efficiency 6

Friedman, C. and Wyatt, J. (2006). Evaluation methods in Bioinformatics, second edition, Health Informatics Series, Springer Science+Business Media.
7
Ammenwerth, E., Jytte Brender, J., Pirkko Nykänen, P., Prokosch, H-U., Rigby, M., and Talmon, T. (2004). Visions and strategies to improve evaluation of health information systems Reflections and lessons based on the HIS-EVAL workshop in Innsbruck, International Journal of Medical Informatics, Elsevier publications, Vol. 73, pp 479-491. 8

Lewis, D. (2008) Evaluation of Public Health Informatics. Public Health Informatics and Information Systems (eds O'Carroll, P., Yasnoff, W., Ward, M., and Ripp, L), Health Informatics Series, pp 239-266. 9

Wagner, M. (2008). Methods for testing Biosurveillance systems, Handbook of Biosurveillance (eds. Wagner, M., Moore, M., and Aryel, R.), pp 507-515, Elsevier academic press.
10
Anderson, J. and Aydin, C. (2005). Evaluating the organizational impact in healthcare information systems, Second edition, Health Informatics Series, Springer Science +Business Media. Waidyanatha

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gains, cost benefits, cost effectiveness ratios, and one-sided sensitivity. The abstract technology design of the RTBP was an integral of data collection, event detection, and alerting functional components. The evaluation framework (Figure 2) for field-testing of biosurveillance systems examined the attributes of the system or system functional components that were further divided into four general categories:

• Institutional challenges (e.g., healthcare workers, health officials, epidemiologists, policy makers),
• Content standards (e.g., ontologies, semantics, syntax, vocabulary, data-standards), • Application or computing resources (e.g., mobile applications, detection analytics software, databases)
• Technology (e.g., mobile devices, computers, servers, modems, wireless links, Internet connections, cellular networks).
The arrows between the vertical functional components
and
horizontal
evaluation
layers
depict
the
interconnection (or interoperability) between the
respective elements. The micro-level research questions
and methods for evaluation were subdivided into those
that
were
based
upon
calculations,
interviews/questionnaires,
and
observations.
The
methodology in sections 6, 7, & 8 of the eval-toolkit-nguide elaborates on the specifics. The healthcare workers, health officials, other staff, and
Figure 2: Functional components, evaluated layers, community-based organization members that were and interoperating elements of the RTBP framework involved in evaluating the RTBP (all participants were exposed to the RTBP) are as follows Kurunegala District, Sri Lanka: Four Medical Officer of Health (MOH) divisions--Wariyapola, Udubeddewa, Pannala, and Kuliyapitiya-- belonging to the Kurunegala health administrative district with each MOH division covering an average population of 500,000 are the autonomous departments responsible for public health surveillance and response in their division; 15 Sarvodaya Suwadana Center volunteers were recruited by the project, in the capacity of research assistants to digitize the patient data at hospitals and assist with other project activities. The 15 research assistants were assigned to 12 hospitals for gathering outpatient data. Public Health Inspectors (PHIs), Personal Program Assistants (PPAs), and Medical Officers of Health (MOH) acted in the capacity of health officials and authorities engaged in assessing the event detection and alerting tools. In addition, the Kurunegala Regional epidemiology unit and the Wayamba Province director of health services departments assisted in the planning, policy and decision process of the RTBP.

Sivaganga District, India: Four Primary Health Centers (PHC) in the Thirupathur block—Nerkuppai (Block PHC), Sevani Patti (Additional PHC), Keelasevalpatty (Additional PHC), Thirukostiyur (Additional PHC)—covering an average population of 500,000, have two primary responsibilities – 1) provide primary level healthcare, and 2) administer the Health Sub Centers (HSC) and affiliated village Waidyanatha

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health nurses (VHNs). Twenty-four VHNs were involved in submitting disease health records of patients seeking care at their respective HSCs. Two data-entry assistants were recruited to digitize outpatient health records from Nerkupai and Keelasevalpatty PHCs while the sector health nurses (SHNs) of the Thirukostiyur and Sevanipatti PHCs digitized outpatient records. The SHNs and Public Health Inspectors of the PHCs acted in the capacity of health officials for the purpose of evaluating the detection and alerting tools; also, the deputy director of Health Services associated Integrated Disease Surveillance (IDSP) staff participated in assessing the detection and alerting tools. The deputy director of health services, district health educator, and chief entomologist engaged in the planning, policy, and decision process in relation to the RTBP.

5 PROJECT ACTIVITIES
Table 1: Activities and description (implementation and management) with timeliness Task

Notes

Begin End
Dates

Project Pre
Launch
Hold a Partner
Meeting

Activities before launching the project
Two-day workshop was organized and hosted by Indian Institute of Technology Madras's Rural Technology and Business
Incubator (IITM's RTBI). All partners presented their role in the project, then agreed on the work plan and deliverables

B: 2008-Jul11
E: 2008Aug-06

blog, work plan, presentations, and report http://lirneasia.net/2008/08/rtbp-partner-meeting-repot/ Recruit Research
Assistants

Select Divisional
Health Areas,
Healthcare
Worker
Personnel, and
Communities

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The post for a research assistant (RA) was advertised and then recruited a B.Sc. Science graduate in India and B.Sc. Biology graduate in Sri Lanka. The Indian RA resigned in April 2009 and was immediately replaced with an available staff member in

RTBI: PhD graduate in Agriculture. The Sri Lankan RA resigned in January 2010; the post was re-advertised and recruited a preintern medical doctor in March 2010.

B: 2008-Jul11

The individual principal investigators consulted with the local health authorities to decide on the pilot districts: India: Sivaganga and Sri Lanka: Kurunegala; the areas were selected based on
RTBI and Sarvodaya's prior experience working in those districts. Four Primary Health Centers and 24 Health Sub Centers in India and Four medical officers of health divisions with twelve
hospitals in Sri Lanka were chosen. India: Village Health Nurses and Sector Health Nurses were the resource engaging in the
health record digitization with the Integrated Disease
Surveillance Program staff engaging in detection analysis and alerting; Sri Lanka: Sarvodaya Suwadana Center recruits engaged

B: 2008Sep-01

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Notes

Begin End
Dates

in the digitizing of health records and Medical Officer of Health department and regional epidemiology unit engaged in the
detection analysis and alerting components.
Procure RA
Equipment

Quotations were taken for laptops and mobile phones, then
purchased for use by the RAs

B: 2008Oct-01
E: 2008Nov-31

Design System
Write Software
Requirement
specifications

Understand the local environment and collect system requirements IITM's RTBI, CMU's Auton Lab, UoA/LIRNEasia/Respere wrote
the SRS; given that the innovation was not clear to the
technology developers, the actual software had to be developed and tested to understand the scope through a practical hands-on experience; for example, the TCWI SRS could not be written
until Auton Lab had a clear idea of the data that was collected (attributes and frequency)

B: 2008Sep-01
E: 2009-Jun2009

User Requirement Specifications (Appendix B):
http://lirneasia.net/2009/02/rtbp-urs-v1/
Demo Prototype

The mHealthSurvey mobile application was first demonstrated in November 2008 to the health workers in India; feedback was
incorporated; it was demonstrated to the Sri Lankan data entry operators and health departments in April 2009.

B: 2008Nov-01
E: 2009Apr-10

India demo blog: http://lirneasia.net/2009/02/m-health-strip10kgs-vhn/ The doctrine on the TCWI was presented to the RTBP team by
Auton Lab, which included an introduction to the statistical testing methods; one day workshop was held at Sarvoday HQ,
Moratuwa, Sri Lanka
T-Cube demo: http://lirneasia.net/2009/04/analytics-training-rtbp/ Organize
Healthcare
Worker Planning
Workshop

Workshops were conducted in Indian and Sri Lanka with the
health workers and health officials in the respective district; a short questionnaire was conducted to understand the demographics of the pilot locations

B: 2008Oct-01
E: 2008Nov-25

Sri Lanka workshop http://lirneasia.net/2008/10/rtbp-lk-planworkshop/ India workshop: http://lirneasia.net/2009/02/vhn-mobile-voice/

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Task

Notes

Begin End
Dates

Release Beta
Develop beta
Release

The first release of the working system for internal testing The first release of the mHealthSurvey was in Nov 2008 but required several modifications; the working solution was ready in Aril 2009.

The TCWI first release was with the replication study conducted in March 2009. The beta implementation was in June 2009.

B: 2008Nov-01
E: 2009Aug-31

SABRO first release was in August 2009 and was tested by the research team
Procure System
Equipment

Training
Prepare Training
Manuals and
Operational
guidelines

The teams in India and Sri Lanka obtained quotations for the suitable mobile phones, servers, and Global System for Mobile (GSM) Communications modems. The equipment was purchased
in April in time to install and configure the hardware for the implementations and training.

B:2009-Jan01
E: 2009Mar-31

Healthcare Worker and Epidemiology Unit training
The technology partners: RTBI, Auton Lab, and Respere develop comprehensive user manuals (see Appendix C Sections 12.1 – 12.3)
LIRNEasia, RTBI, and UoA developed a series of standard
operating procedure guides for the three components: data
collection, event detection, and alerting (see Appendix C Sections 12.9 – 12.11)

B: 2009Apr-01
E: 31-Oct2009

SOP: http://lirneasia.net/2009/12/rtbp-sop-v0-3/
Organize
Healthcare
Worker Training
Workshop

mHealthSurvey training workshops was conducted in Karaikudi, Tamil Nadu with 29 healthcare workers and five health officials; members from LIRNEasia and RTBI conducted the training,
report: http://lirneasia.net/2009/05/vhn-training/

S: 2009May-01
E: 2010Apr-30

mHealthSurvey training was conducted with 16 Suwacevo and
four divisional coordinators at the Sarvodaya Kurunegala district office, members from RTBI and LIRNEasia conducted the
training, report: http://lirneasia.net/2009/05/lk-healthworkertrainin/ mHealthSurvey users were given follow up training with
individuals and in small groups as and when required
LIRNEasia and RTBI conducted the first training of TCWI and
SABRO in India and Sri Lanka in October 2009. This was
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Notes

Begin End
Dates

introductory and intended only to get the users to start testing the software. A more detailed training of TCWI followed in
December 2009 and January 2010 that was conducted by Auton
Lab; report: http://lirneasia.net/2009/05/autonlab-april-visit/ A second round of training on SABRO with the health officials was conducted in March 2010 before evaluating the alerting
component. This was simultaneously carried out by LIRNEasia
and RTBI in Kurunegala and Sivaganga districts, respectively; reports: http://lirneasia.net/2010/04/rtbp-kuru-alert-exer/
Activation
Deploy final
release of ICT
system

Finalize all system components of RTBP

Conduct
Interoperability
Testing

Project could not complete this task. One reason was the SABRO software took longer than anticipated to mature. Second reason was the users were not as competent with CAP as the
investigators had anticipated.

Commission
System for
evaluation stage

The acceptable working technology solution, conducting of
training, and introduction to standard operating procedures was completed by November 2009. However, there are shortcomings
that required fixing. The researchers determined that the
workable components could be commissioned for the sake of the pilot test.

Evaluation

Running of mock-drills over a 1 year period

Plan simulation
evaluation
activities

The evaluation activities began with the mHealthSurvey
certification exercise in early July 2009. The evaluations were conducted over 18 months. The planning of the events was done in stages; i.e. each time an exercise was completed the next set of evaluation exercises were planned. The final subjective

evaluation exercise was the assessing the alerting component, which ended in June 2010.

B: 2009-Jul01

Design RTBP
Evaluation
Toolkit

At the early stages of planning the evaluations, the researchers carried out literature surveys and discussions to decide on the achievable and sufficient evaluations. These methods were
document in the Evaluation guidelines:
http://lirneasia.net/2009/10/rtbp-eval-guide/ . The eval-toolkit-nguide is an extension of this document, which includes the results

B: 2009Apr-01

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mHealthSurvey, TCWI, and SABRO went through several
iterations of modifications as the researchers and developers came to learn the shortcomings as well as the users requesting changes once they had some hands on experience with the tools.

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E: 2010Mar-20

B: 2009Nov-01
E: 2009Nov-20

E: 2010Mar-31

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Notes

Begin End
Dates

and methodology.
Organize
simulations
planning
workshop

Evaluation planning workshops were held in India and Sri Lanka involving all project stakeholders. Participants range from health officials, health workers, and community-based organization
members; report: http://lirneasia.net/2010/01/eval-planworkshop-beauty-of-rtbp/

B: 2009Dec-01

Hold a Public
Lecture

India public lecture was planned to be held at the IITM's RTBI Research Park in conjunction with the interim findings meeting and media events. Dr. Shariq Khoja (Aga Khan University,
Pakistan) could not travel to deliver the lecture because he could not obtain the visa.

B: 2010May-01

E: 2010-Jan31

E: 2010Sep-31

Sri Lanka public lecture was delivered by Dr. Angelo Ramos
(Molave Development Foundation, Inc. Philippines). It was held at the Sri Lanka Medical Association; report:
Run simulated
exercises

Data collection (mHealthSurvey): calculated the timeliness, data quality, and miscoding errors; assessed the self-intuitiveness and usability through a certification exercise; interviews and group discussions to realize the acceptability and improvements;

economic analysis
mHealthSurvey certification exercise report:
http://lirneasia.net/2009/06/gow-visit-june-2009/
Reports: http://lirneasia.net/2009/10/rtbp-cert-exer-young-old/

Analysis
Discuss research
findings at a
workshop in each
country

Event detection (T-Cube Web Interface): interviews and group discussions to realize the policy and procedures; replication study to assess the functionality; cohort study to realize the utility and reliability; simulation to assess the usability; technology

acceptance assessment to evaluate the usefulness, perceived ease of use, behavioral interaction, attitude towards using, and
psychological attachment; economic analysis
Alerting (Sahana Alerting Broker):
http://lirneasia.net/2010/04/rtbp-kuru-alert-exer/
Assess the results and discuss the findings
India interim findings workshop took place at the IITM's
Research Park, which brought together selected field health
workers, health officials, and staff from Sivagana district as well as the RTBP team members from India, Sri Lanka, and USA;
report: http://lirneasia.net/2010/07/rtbp-in-news/

B: 2010Apr-01
E: 2010June-15

Sri Lanka interim findings workshop took place in Kurunegala district at the Blue Skyp Hotel, with Provincial Director,
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Begin End
Dates

Provincial Staff, chief Medical Officers, Public Health
Inspectors, Sarvodaya Suwacevo, RTBP researchers from Sri
Lanka and India; report: http://lirneasia.net/2010/07/rtbp-lkfindings/ Comparison
Research Meeting

Final Report

The comparison research findings meeting took place in
Negambo, Sri Lanka, which involved all project partners, mainly researchers. The one-day workshop discussed and finalized the research outcomes as well as formalized the recommendations
that would lead to the next cycle of research.

B: 2010Aug-01

Final Technical Report would have been due in July 2010;
however with the extension of the project it was rescheduled for delivery on 31-December-2010

B: 2010Dec-10

Research
Disseminations

Publications to be produced by Researchers on the topics below

Epidemiology
Policy
Epidemiology eHealth Programs

Sections 6.7 – 6.10 outline all the policy briefs, publications and proceedings produced through the project, which address all
other the topics listed.

E: 2010Sep-15

E: 2011-Jan02

B: 2009Apr-01
E: 2010Dec-31

Interoperability in
Epidemiology
through e-Health
E-Health RTBP
Implementation
challenges
Outcome of the
RTBP in India
and Sri Lanka

6 PROJECT OUTPUTS
6.1 User Requirements Specification (URS)
The RTBP is based on the concept of a closed system; system with feedback and error correction. The input to the system are simple environmental attributes, which are features such as the season and the day of week that cause trends in the data, and response attributes, which are the remaining features such as syndrome, diagnosis, gender, and age. The output generated by the system is detections of Waidyanatha

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possible disease outbreaks or patterns of adverse events. The feedback loop for error correction is the intervention and prevention actions to minimize the health risks. The User requirement derived follow from the close analysis of the existing disease surveillance and notification systems in both Sri Lanka and Indian. First, we introduced the key actors, their roles, and the functionality required for the purpose of data collection, analysis, and alerting. Secondly, we introduced the minimal set of attributes required to attain the system requirements for collection of health data, analysis of outbreaks, and sharing health risk information. Based on the initial analysis, an axiomatic design framework11 was applied to identify the customer attribute, functional requirements, and design parameters of the proposed ICT system. This is an objective study aiming to ensure the time independent complexities are eliminated (see Appendix A). Following the preliminary design and analysis of the business we collaborated with the users (health workers, health officials, and decision makers) in a top-down bottom-up approach (Gadomski et al 1998) to develop the URS document (see Appendix A).

6.2 Software Requirement Specifications (SRS)
The technology developers used the URS as a guide along with further interactions with the researchers and end-users to develop the individual SRS documents for the – mobile health data collection software, biosurveillance module database with implementation Graphic User Interfaces, statistical data mining algorithms with visualization tools for event detection, and extending the Sahana Alerting Broker with Common Alerting Protocol data standard for issuing SMS, Email, and Web text alerts. Each of the SRS went through a series of revisions throughout the development life cycle. It was difficult for the users to conceptualize the software deliverables and as a result changes were requested after they had the opportunity to test the tangible software executables. The project also made some changes to the software developments after the users were operating the applications and the researchers/developers realized the unforeseen shortcoming. Appendix B carries the latest version of all the SRS documents for each of the components.

6.3 Technology Products
The sections below give a brief less technical description of the software components for data collection, data warehousing, event detection, and alerting that are referred to as the mHealthSurvey mobile application, Biosurveillance Module, T-Cube Web Interface, and Sahana Alerting Broker, throughout this document.

6.3.1

mHealthSurvey

The mHealthSurvey mobile health software is a Java 2 Micro Edition (J2ME) application that works on 11

Suh, Nam (2005). Complexity Theory and Applications (MIT Pappalardo series in Mechanical Engineering), Oxford University Press, 2nd Edition.

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any Java-enabled mobile supporting the Mobile Information Device Profile 2.0 (MIDP2.0) and Connection Limited Device Configure ration 1.1 (CLDC1.1) common Java Specification Request (JSR) stack. The application was tested on Nokia 3110c/5320c,Amoi A636, Gionee v6600, Gionee v6900,Motorola , Sony Ericsson s302c.

Members of the project team recognize that more advanced platforms (e.g., Android, Linux, iPhone) are now becoming commonplace but viewed J2ME as a powerful technology relative to cost and other constraints in this setting and for the purpose of piloting the system. For example, it is widely available on low cost GSM handsets and offers a built-in consistency across products in terms of running anywhere, anytime, over most devices.

Users must first download the mHealthSurvey Java executable from the hosted server simply by pointing the mobile phones' WAP browser to the given web address. Once it is saved on the mobile phone, the continued process will prompt the user to install the application. Prior to submitting health records, the user must configure the software to adhere to their profile. This is done through the download list function, the initial function executing any of the other functions. The download list essentially retrieves all disease, symptom, and signs values along with the predefined relationships and the health worker type, location, gender, group, and clinical status predefined selectable values. The download list function is also the initial test verifying the success of the installation and the application's ability to communicate with the server and database. The user unique health workers specific profile is registered on the mobile phone, which is used to tag the health records for accountability factors. The set of health worker assigned working area locations (clinic and hospital villages, towns, or cities) is also retrieved from the database. Thereafter, the application is ready to begin submitting digitized records.

The basic functions of the health-survey or patient record digitizing form comprises attributes such as the case date/time, health worker id, location name, gender, age-group, disease, symptoms, and signs. An additional field labeled as 'notes' is provided for feeding any other relevant information besides content in the standard attributes. The application utilizes a search and fill method for completing the disease, symptoms, and signs as well as a method that automatically populates the related symptoms and signs when the disease is selected; thus reducing the data entry time but giving the option to add to and delete from the proposed list of symptoms and signs.

The comprehensive user manual in Appendix C Section 13.1 describes the mHealthSurvey functionality.

6.3.2

Sahana Biosurveillance Module (BSM)

The mobile phone application submitted information is staged in the Sahana12 Biosurveillance Module (BSM) relational database. The database contains a set of master tables and transaction tables. The master tables hold the knowledge base of the domain specific information that is coupled with a set of procedure to ensure that data integrity is maintained. The transaction tables hold the dynamic 12

Information on Sahana Software Foundation can be found here http://www/sahanafoundation.org/

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information mainly received through the mHealthSurvey.
The relational database consists of a person, location, facility, services, diagnosis, and cases entities. The person tables hold information on health worker profiles and is able to hold patient identification details like their name, identification document numbers, vital statistics, etc.; location defines the geographical areas and administrative boundaries; facilities define the hospitals and clinics; services are for defining and procedures such as investigations, quarantining, laboratory testing; etc.; diagnosis holds the disease, symptom, signs, and causality relationship; cases are for saving the patient clinical health records. All the entities are related in some way with the case entity being central to all. The mHealthSurvey interfaces with the MySQL database through a set of PHP scripts. The BSM PHP scripting architecture conforms to a Representational State Transfer full (REST-like) architecture; where data is shared between the user interfaces and database through standard HTTP POST and GET functions.

The software requirement specification in Appendix B Section 11.3.1 describes the functionality of the Sahana Biosurveillance management system.

6.3.3

T-Cube Web Interface (TCWI)

T-Cube Web Interface (TCWI) tool is designed to efficiently visualize and manipulate large-scale datasets. These types of data sets are common in epidemiology. Besides executing various complex adhoc drill-down queries, TCWI enables a range of methods for statistical testing of multivariate temporal and spatio-temporal data. Users can manipulate and visualize data through the Time Series, Map, and Pivot Table panels.

The user may choose to apply one of the available statistical modeling and anomaly detection techniques. The list of choices includes moving average, moving sum, cumulative-sum, temporal scan, change scan, linear trend, peak analysis and range analysis. The users can interactively manipulate, navigate, summarize and visualize data at interactive speeds. That supports focused investigations, drill-downs as well as summarizing and reporting operations. The users may choose to simply execute a Massive Screening procedure, which performs an automatic and comprehensive search for anomalous patterns across large number of queries spanning multiple dimensions of data. The user could invoke this function interactively, or it could be scheduled to execute periodically to generate a set of alerts. The alerts are sorted according to statistical significance of the corresponding anomalies found in data, and they can be interactively reviewed by the Epidemiologists for the factual confirmation of their practical importance. TCWI supports these efforts by allowing focusing attention on the most surprising patterns in current data and by providing the ability to quickly drill down or roll up the data for further explanations.

TCWI also provides computationally efficient, interactive data summarizing capability. Multidimensional data of counts of events (such as the numbers of reported disease cases) can be aggregated into a multi-way matrix view - a pivot table. Multiple attributes can be selected to denote rows and columns of the table by dragging the corresponding attribute names from the attributes list. Once a table is created and automatically filled with values, the user can click on a cell to view the Waidyanatha

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corresponding time series graph, or a pie chart depicting the frequency distribution of the underlying data.
The TCWI software requirement specification in Appendix B Section 11.3 describes generic utility and the user manual in Appendix C Section 13.2 describes the user interfaces and algorithms used in RTBP.

6.3.4

Sahana Alerting Broker (SABRO)

The overarching Messaging/Alerting Module is a Sahana Module that is used for the sending and receiving of various messages ranging from polling functions to alerting functions. At present, the module allows for the generic sending and receiving of messages in the form of SMS, sending messages as Email, conducting SMS based surveys and issuing Common Alerting Protocol (CAP) alerts. A sub-module of the Messaging/Alerting Module is the Sahana Alerting Broker (SABRO). CAP is an accepted standard of the International Telecommunications Union (ITU) labeled as ITU-T X.1313. The Organization for the Advancement of Standardization of Incidence Systems (OASIS) developed and published the XML files and carries the references to the document definition URL (specifically the reference schema). CAP adopts a Document Type Definition (DTD) Extensible Markup Language (XML) data structure that consist of a main element and sub elements , , and .

CAP was integrated into this project because it is an open source, XML-based protocol with clearly defined elements, is capable of supporting data interchange across multiple dissemination channels; with CAP, one input at the central information hub can be translated into multiple outputs for downstream alerting; CAP provides a standardized template for submitting observations to the central authorities (upstream) and thereby supports situational awareness to improve overall management of a critical incident; A CAP-enabled system will more easily integrate with other national and international information systems.

SABRO is a server application that provides an intermediary point of interconnection between the RTBP health departments and the relay network to facilitate interconnection of all ICTs and passage of CAP-compliant messages through a single software application. The sub components of server software consist of five interconnected subsystems: Message creation and validation, Message distribution, Message delivery, Message acknowledgement, Message system administration. First step is preparing the message templates. The intention of the templates is to pre-populate the CAP content with values that are common across all messages within that category of alerts. This would ease the data entry burden on the message issuers and increase the message generation times. These templates are saved in the database of templates, using easy to select neutral language. Each message carries a unique identifier, a set of attributes that identifies the source and sender for audits. The scope is set as restricted meaning the message is for those targeted recipients only (i.e. health workers and health officials in the case of RTBP). Category is naturally set to “Health” with the event described as a “disease outbreak.” Priority defines the response action or inaction that should be taken by the receiving health workers or health officials. If the priority was set to “urgent” then recipients may be required to take prompt action; while a “low” priority may mean being vigilant and observe the situation. The description section contains a full synopsis of the alert. Waidyanatha

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Once the attributes are populated, the sender can select or type in the list of individual recipients or recipient groups in the Contacts section. Thereafter, select the delivery types (or transport methods), i.e. email, SMS, web, for the message to be disseminated to the prescribed recipients via those channels The Sahana Alerting Broker requirement specifications in Appendix B Section 11.4.3 describe the design requirements and Appendix C section 12.3 describe the user interfaces and functionality.

6.4 User Manuals and Standard Operating Procedures
The early stages of the technology release, comprehensive user manuals were produced. These manuals described all aspect of the technology and their functions (see Appendix C Section 12). In addition to the comprehensive user manuals, a set of quick reference guides were provided to the users (Appendix C Sections 12.12 – 12.14).

The Standard Operating Procedures were an additional set of guidelines outlining the processes that involved other non-technology related functions. Along with the newest release of the improved versions of the each technology, a quick references guide was giving minimal instructions to operate the software. All these documents are provided in Appendix C Sections 12.9 – 12.11 for mHealthSurvey, TCWI, and SABRO.

6.5 Evaluation Toolkit and Guide
The eval-toolkit-n-guide was developed to outline the evaluation methodology for assessing the upstream communication: data collection, data processing: event detection, and downstream communication: alerting/reporting functions. The purpose of the document was to share the research questions and methodology with investigators and researchers alike. It also served as means to document the in-depth knowledge of the RTBP evaluation process.

6.6 Training and awareness workshops

Partner Planning meeting, 04 – 05 August 2008, IITM's Rural Technology and Business Incubator, Chennai, India, report - http://lirneasia.net/2008/08/rtbp-partner-meeting-repot/

Healthcare workers and official planning meeting, 07-08 October 2008, Medical Officer of Health Office, Kuliyapitiya, Sri Lanka, report - http://lirneasia.net/2008/10/rtbp-lk-planworkshop/

Healthcare worker and official planning meeting, 23-24 November 2008, Thirukostiyur Primary Health Center, Sivaganga District, India, report - http://lirneasia.net/2009/02/vhn-mobile-voice/

Detection Analysis, Introduction to T-Cube Web Interface, 04 April 2009, Samana Thetha – Lanka Jathika Sarvodaya Shramadana Society, Moratuwa, Sri Lanka, report http://lirneasia.net/2009/04/analytics-training-rtbp/

Mobile application training, 25-26 May 2010, Lanka Jathika Shramdana Society District Office, Kuliyapitiya, Sri Lanka, report - http://lirneasia.net/2009/05/lk-healthworker-trainin/

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Mobile application training, 29-30 May 2009, Hotel Subha Lakshmi, Karraikudi, Sivaganga District, India, report - http://lirneasia.net/2009/05/vhn-training/

Event detection and Alerting Training, 29-September-01-October-2010, Deputy Director of Health Services office, Sivaganga District, India, report - http://lirneasia.net/wpcontent/uploads/2009/10/RTBP-Field-visit-report-Oct-2009.pdf

Event detection and Alerting Training, 06-07 October 2010, Medical Officer of Health office, Kuliyapitiya, Sri Lanka, report - http://lirneasia.net/wp-content/uploads/2009/10/Kuru-Fieldreport.pdf

Evaluation planning, Hotel Subha Lakshmi,17-18 December 2010 Karraikudi, Sivagangai District, Sri Lanka, report - http://lirneasia.net/2010/01/eval-plan-workshop-beauty-of-rtbp/

Evaluation planning, 17-18, 22 December 2010, Blue Sky Hotel, Kurunegala, Sri Lanka, report - http://lirneasia.net/2010/01/eval-plan-workshop-beauty-of-rtbp/

Interim findings, IITM's Research Park, 07 July 2010, Chennai, India, report http://www.lirneasia.net/wp-content/uploads/2010/08/RTBP-FindingsWorkshop-REPORT.pdf

Interim findings, 12 July 2010, Hotel Blue Sky, Kurunegala, Sri Lanka, report http://lirneasia.net/2010/07/rtbp-lk-findings/

Final meeting, Hotel Blue Ocean, Negambo, Sri Lanka, report – this final technical report.

6.7 Policy Briefs

Two policy briefs were produced – one for India and second for Sri Lanka. Copies of both are in Appendix D.

6.8 Book Chapters

Gow, G. and Waidyanatha, N. (2010). Using Common Alerting Protocol to Support a Real-Time Biosurveillance Program in Sri Lanka and India, Kass-Hout, T. & Zhang, X. (Eds.). Biosurveillance: Methods and Case Studies. Boca Raton, FL: Taylor & Francis, Chapter 14, p. 268-288.

Prashant, S. and Waidyanatha, N. (2010). User requirements towards a biosurveillance program, Kass-Hout, T. & Zhang, X. (Eds.). Biosurveillance: Methods and Case Studies. Boca Raton, FL: Taylor & Francis, Chapter 13, p .240-263.

6.9 Journal articles

Nuwan Waidyanatha and Sabrina Dekker (2011), The RTBP – Collective Intelligence Driving Health for the Users, International Journal of user Driven Healthcare, IGI Global Publications. (In Press).

Nuwan Waidyanatha, Artur Dubrawski, Ganesan M., and Gordon Gow (2011). Affordable

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System for Rapid Detection and Mitigation of Emerging Diseases , International Journal of eHealth and Medical Communications, IGI Global Dissemination of Knowledge (In Press) •

M. Ganesan, Suma Prashant, N. Janakiraman and Nuwan Waidyanatha. Real-Time BioSurveillance Program: Field Experiences from Tamil Nadu, India. Health, Poverty and Human Development, Indian Association for Social Sciences and Health (IASSH) (in press)

Ariyaratne, V., Ratnayake, R., Hemachandra, P., and Edirisinghe, E, Sampath, W., Waidyanatha, N.(2010). Real-Time Biosurveillance Pilot Program, Sri Lanka journal of Bio-Medical Informatics 2010;1(3):139-154

6.10 Research papers (Conference/Symposium proceedings)





Sampath, C., Ganesan, M., Waidyanatha, N. (2010). mHealth Revolutionizing public health: an economic , 5th Communication Policy Research south (CPRsouth 2010), December 11-13, 2010, Xi'an, China
CPRsouth5: http://www.cprsouth.org/cprsouth5/cprsouth5-papers-presentations-and-policybriefs-xian-china/ Gordon Gow, Chamindu Sampath, Ganesan M., Janakiraman N., Mifan Careem, Damendra Pradeeper, and Mahesh Kaluarachchchi (2010). Sahana Alerting Software for Real-Time Biosurveillance in India and Sri Lanka, Proceedings of the 1st IEEE International Conference on Computer and Information Applications (ICCIA 2010), Dec 03-05, 2010, Tianjin, China, p 37373. IEEE-ICCIA 2010: http://lirneasia.net/2010/12/rtbp-iccia-2010/ Lujie Chen, Artur Dubrawski, Chamindu Sampath, and Nuwan Waidyanatha (2010). Automated Detection of Data Entry Errors in a Real-Time Surveillance System, Proceedings from the 9th International Society for Disease Surveillance (ISDS 2010), Nov 30 – Dec 02, Park City, USA.

Artur Dubrawski, Michael Baysek, Chamindu Sampath, and Nuwan Waidyanatha (2010). Challenges of Introducing Disease Surveillance Technology in Developing Countries: Experiences from India and Sri Lanka, Proceedings from the 9 th International Society for Disease Surveillance (ISDS 2010), Nov 30 – Dec 02, Park City, USA. ISDS 2010 Blog: http://lirneasia.net/2010/12/rtbp-isds-201/

Artur Dubrawski and Nuwan Waidyanatha (2010). Real-Time Disease Surveillance using Affordable Technology, 2010 mHealthSummit, 08-10 November, 2010, Washington D.C., USA
mHeath Summit 2010: http://lirneasia.net/2010/11/rtbp-mhealth-summit-2010/ Sampath, C., Dubrawski, A., Chen, L., Sabhnani, M, Ganesan, M., Vincy, P. and Waidyanatha, N (2010). T-Cube as a tool for detecting disease outbreaks in real-time, Proceedings of the13 th IEEE International Conference on Computing and Communication Technology – Research Innovation and Vision for the Future (IEEE-RIVF 2010), November 02-04, 2010, Hanoi Vietnam

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IEEE-RIVF 2010: http://lirneasia.net/2010/11/rtbp-rivf2010/

Chamindu Sampath, Artur Dubrawski, Nuwan Waidyanatha, and Ganesan M. (2010). T-Cube Web Tool for rapid detection of disease outbreaks in India and Sri Lanka: lessons learned, 2nd eHealth Sri Lanka Conference, Health Informatics Society of Sri Lanka, 15 – 16 September 2010.

Chamindu Sampath, Nuwan Waidyanatha, Gordon Gow, Ganesan M., Mifan Careem, Pradeeper Damendra, and Mahes Kaluarachchi (2010). Sahana Alerting Module for Real-Time Biosurveillance in India and Sri Lanka: lessons learned, 2nd eHealth Sri Lanka Conference, Health Informatics Society of Sri Lanka, 15 – 16 September 2010. HISSL 2010: http://lirneasia.net/2010/09/rtbp-hissl-ehealth2010/






Anderson, P., Careem, M., Damendra, P., Gow, G., Samarajiva, R., and Waidyanatha, N. (2010). Common Alerting Protocol All-hazards All-media for saving lives: two case studies, Symposium on Disaster Impact and Assessment in Asia – Centre for Research on the Epidemiology of Disasters, August 25 – 27, 2010, Hue City, Vietnam. Dubrawski, A, Chen, L., Beysek, M., Prashant, S., Ganesan, M., (2010) Real-Time Biosurveillance Pilot in India and Sri Lanka, IEEE-HealthCom 2010, proceedings of the 12 th International Conference on e-Health Networking, Applications, and Services, July 04 – 06, 2010, Lyon, France.

IEEE-HealthCom 2010: http://lirneasia.net/2010/07/rtbp-ieee-healthcomm-2010/ Gow, G., Vincy, P. and Waidyanatha, N. (2010). Using Mobile Phones in a Real-time Biosurveillance Program: Lessons from the frontlines in Sri Lanka and India, IEEE International Symposium on Technology and Society (ISTAS 10), June 07 – 09, 2010, Wollongong, Australia, p 366 – 374.

Kannan, T., Sheebha, R., Vincy, P., and Waidyanatha, N. (2010). Robustness of the mHealthSurvey Midlet for Real-Time Biosurveillance, Intelligent Mobile Computing for Better Medical Services, 4th International Symposium on Medical Information and Communication Technology (ISMICT 10), March 22 – 25, 2010, Taipei, Taiwan. ISMICT 2010 blog: http://lirneasia.net/2010/04/mobile2-0-ismict2010-rtbp/ Ganesan, M., Prashant, S., Janakiraman, N., and Waidyanatha, N. (2009), Real-Time BioSurveillance Program: Field Experience from Tamil Nadu India, 7th Indian Association for Social Science and Health (IASSH) Conference, February 13, Varanasi, India IASSH 2010 blog: http://lirneasia.net/2010/03/rtbp-at-iassh/ Dubrawski, A. Sabhnani, M, and Waidyanatha, N. (2009). T-Cube Web Interface for Real-Time Biosurveillance Program in Sri Lanka, 8 th International Society for Disease Surveillance (ISDS) Conference, December 03 – 04, Las Vegas, USA.

ISDS 2009 Blog - http://lirneasia.net/2009/12/m-health-real-time-biosurveillance-pilotshowcased-in-las-vegas/ Dubrawski, A., Ganesan, M., Gow, G., Sabhnani, M., Waidyanatha, N., and Weerakoon, P. (2009). Real-Time Biosurveillance Pilot in India and Sri Lanka, eAsia 2009, December 02 – 04, 2009, Colombo, Sri Lanka

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eAsia 2010: http://lirneasia.net/2009/11/rtb-e-asia2009/

Dubrawski, A. Sabhnani, M. Knight, M. Baysek, M. Neill, D. Ray, S. Michalska, A. Waidyanatha, N. (2009). T-Cube Web Interface in support of real-time bio-surveillance program, proceedings of the International Communication and Technologies and development (ICTD 2009), 17 – 19 April 2009., Doha, Qatar, p 495-495

ICTD 2009: http://lirneasia.net/2...

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12.6.3 12.6.3.1 12.6.3.2 12.6.3.3 12.6.4 12.6.4.1 12.7 12.7.1.1 12.7.1.2 12.7.2 12.8 12.8.1 12.8.2 12.8.3 12.8.4 12.8.5 12.8.6 12.8.7 12.8.8 12.8.9 12.9 120 121 122 123 123456 124 125 126 127 128 129 13 13.1 13.2 130 131 132 132717 133 134 135 136 137 138 139 14 140 141 142 143 144 145 146 147 148 149 15 150 151 152 153 154 155 156 157 158 159 16 160 161 162 163 164 165 166 167 168 169 17 170 171 172 173 174 175 176 177 178 179 18 180 181 182 183 184 185 186 187 188 189 1890 19 190 1909 191 192 193 194 195 196 1963 197 1973 198 199 1996 1998 19th 1e 1st 2 2.0 2.1 2.1.3 2.2 2.2.2 2.2.3 2.3 2.3.1 2.3.2 2.4.5 2.5 2.6.12 2.6.8 20 200 2000 2002 2003 2004 2005 2006 2007 2008 2008aug 2008nov 2008oct 2008sep 2009 200907240001 20090810104000 2009apr 2009aug 2009dec 2009mar 2009may 2009nov 201 2010 2010apr 2010aug 2010dec 2010june 2010mar 2010may 2010sep 2011 202 203 204 205 206 207 208 209 20t23 21 210 211 212 213 214 215 216 217 218 219 22 220 221 222 223 224 225 2255 226 227 228 229 23 23.1122 230 231 232 233 234 235 236 237 238 239 24 24/7 24/7/365 240 241 242 243 244 245 246 247 248 249 24th 25 250 251 252 253 254 255 256 257 258 259 26 260 261 262 263 264 265 266 267 268 269 27 270 271 272 273 274 275 276 277 278 279 28 280 281 282 283 284 285 286 287 288 289 29 290 291 292 293 294 295 296 297 298 299 2nd 2x2 3 3.0 3.1 3.2 3.4123 3.5 30 300 301 302 303 304 305 306 307 308 309 31 311 3110c 3110c/5320c 3116 312 313 314 316 317 32 32/64-bit 322 323 325 326 327 328 329 33 330 331 34 34.1234 35 36 366 37 37.20 37373 374 38 39 390 4 4.0 4.1 4.2 4.3 40 41 411 411a 42 43 44 45 450 46 47 479 48 49 495 49pm 4th 5 5.1 50 500 507 51 52 53 54 55 550 56 57 58 59 5c 5th 6 6.1 6.10 6.11 6.12 6.2 6.3 6.3.1 6.3.2 6.3.3 6.3.4 6.4 6.5 6.6 6.7 6.8 6.9 60 61 62 63 64 65 66 67 68 69 7 7.0987 7.1 7.1.1 7.1.2 7.1.3 7.1.4 7.2 7.2.1 7.2.2 7.2.2.3 7.2.3 7.2.3.1 7.2.3.2 7.2.4 7.2.4.1 7.2.4.2 7.2.4.3 7.2.4.4 7.2.4.5 7.3 7.3.1 7.3.2 7.3.3 7.4 70 71 72 73 74 75 76 77 78 79 7th 8 8.1 8.10 8.11 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www.epid.gov.lk/public/alert/cap/ www.epid.gov.lk/restricted/alert/c www.epid.gov.lk/restricted/alert/ca www.epid.gov.lk/restricted/alert/cap www.epid.gov.lk/wer.htm. www.epid.gov.lk/wer/ www.epid.gov.lk/wer/). www.ibm.com www.ibm.com/developerworks/xml/library/x-tipsms1.html www.ics.uci.edu www.ics.uci.edu/~fielding/pubs/dissertation/fielding_dissertation.pdf www.idrc.ca www.idrc.ca/en/ev-117799-201_104161-1- www.incident.com www.incident.com/cookbook/index.php/main_page www.javaworld.com www.javaworld.com/javaworld/jw-12-2002/jw-1220-wireless.html www.linuxhelp.net www.linuxhelp.net/guides/lamp/) www.lirneasia.net www.lirneasia.net/cap-guidelines-hazinfo www.lirneasia.net/wp-content/uploads/2010/08/rtbp-findingsworkshop-report.pdf www.ncbs.res.in www.promedmail.org www.promedmail.org/) www.rtbi.in www.sahana.lk www.sahana.lk/. www.sahana.lk/ds/gis/wer www.sakana.lk www.sakana.lk/docu/ www.sarvodaya.org www.sarvodaya.org/healthalert/ www.scdmc.lk www.scdmc.lk/alerts/cap/recentalerts.xml) www.scdmc.lk/cap/alert/ www.scdmc.lk/docs/rtbp_cap_user_guide_v1_3.pdf www.scdmc.lk/index.php? www.scdmc.lk/mhslk/ www.ualberta.ca www.w3schools.com www.wampserver.com www.wampserver.com/en/download.php) www.way2sms.com www.whoindia.org www.whoindia.org/en/section3/section108.htm www/sahanafoundation.org wyatt x x.1313 x1 x2 xi xm xml xml-base xn xslt y y1 y2 yasnoff year yellow yersinia yes yet yn youth z zainudeen zero zhang zone zoom ž α