Real-Time Biosurveillance Program Pilot - India & Sri Lanka

Presentation Title

Nuwan WaidyanathaLIRNEasia

Email: [email protected]

http://www.lirneasia.net/profiles/nuwan-waidyanathaMobile: +8613888446352 (cn) +94773710394 (lk)

Conference Theme

Month day, 2010Location

This work was carried out with the aid of a grant from the International Development Research Centre, Canada.

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Early detection and mitigation of common diseases and pandemics

Real-Time Biosurveillance Program to Revolutionize disease surveillance and notification

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INTRODUCTION TO THE RESEARCH Synergies of RTBP and Early Warning Systems Research question and specific objectives

DISEASE INFORMATION REQUIREMENTS Determinants of notifiable diseases in India and Sri Lanka Cycle of data collection, analysis, and dissemination

COMMUNICATION SYSTEM EVALUATIONS Data collection :: mHealthSurvey mobile application Event detection :: T-Cube Web Interface Disseminations :: Sahana Messaging/Alerting Module Cost effectiveness, efficiencies, and sensitivity

CONCLUSIONS & REFERENCES

Outline

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Synopsis of IDRC funded PANACeA projects

PAN Asian Collaboration for Evidence-based e-Health Adoption and Application

Initiative to generate evidence in the field of e-health within the Asian context, by forming a network of researchers and research projects from developing Asian countries.

http://www.aku.edu/CHS/panacea/about.shtml

Some PANACeA Initiatives (more :: http://tinyurl.com/39ypljm )

❏ Outbreak Management System

❏ Systematic review of ICTs in disasters

❏ e-Health system for community health care recording and reporting

❏ Mobile telemedicine system for ambulance and movable community health care

❏ Mobile telemedicine kit for disaster relief

http://www.aku.edu/CHS/panacea/about.shtml

http://tinyurl.com/39ypljm

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Panacea THIRRA (http://thirra.primacare.org.my): Portable system from telehealth and health information in rural & remote areas

Cell-Life: preventing HIV/Aids (): monitoring and intervention programs by communicating data via mobile phone GPRS connected workstations

EpiSurveyor Software developed by DataDyne (): Desktop tool to develop “forms” for handheld mobile devices; J2ME mobile phone solution, Tested on PDAs in Uganda, Tanzania; on “CDC Maternal Health Evaluation Forms”;live stock development board Sri Lanka

OpenRosa/JavaRosa (): consortium developing standards based tools for collecting data via mobile phones, analyzing data, and reporting data via mobile phones, Free software downloads – “Gather” is a project supporting the openrosa work and testing solutions in Africa

D-Tree Offers Childcare solutions ():They use javarosa code base; e-MICI project in Uganda – household questionnaire to monitor Child diseases; doing work with BRAC in Tanzania on gathering household health info

ComCare: XForms based solution for collecting community health information

InSTEDD: create and advocate open source tools (): Social network approach, Tested in Mekong river basin, Event base surveillance techniques

Click Diagnostics (): collect patient household data, stakeholders to run custom analyze , program to target disease areas and improve intervention (e.g. HIV/AIDS staging and regimen management, cervical cancer screening, malaria surveillance, TB surveillance)

Other m-Health Initiatives on disease control

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Disease Surveillance

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Sensor

Detection

Decision

Physical World

Broker

Response

Doctrine of Real-Time Biosurveillance (RTBP)

Health Providers,Relief Workers

Observe Relevant Data

RTBPm-HealthSurvey

Record and Transmit Data GSM phone

network

RTBPServer and Database

Store Data

RTBPInteractive

Visualization, Analysis and

Event Detection Software

Monitor Data

AffectedPopulation

Automated Alerts

Interactive Analytics

Internet, GSM

network

Internet

Analysts, Health Officials,Epidemiologists, Decision Makers

Manage Relief Effort

HealthDisaster

Management

RTBP?

RTBP?

RTBP?

RTBP?

RTBP?

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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?”

Evaluating a Real-Time Biosurveillance Program: Pilot

Specific Objectives

Evaluating the effectiveness of the m-Health RTBP for detecting and reporting outbreaks

Evaluating the benefits and efficiencies of communicating disease information

Contribution of community organization and gender participation

Develop a Toolkit for assessing m-Health RTBPs

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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?”

mHealthSurvey a data entry software works on any standard java mobile phone. A typical record contains the patient visitation date, location, gender, age, disease, symptoms, and signs. Data is transmitted over GPRS cellular networks.

T-Cube Web Interface (TCWI) is an Internet browser based tool to visualize and manipulate large spatio-temporal data sets. Epidemiologists can pin down a potential outbreak of, for instance, a gastrointestinal disease among children in the Sevanipatti PHC health division.

Sahana Alerting Broker (SABRO) allows for the generic dissemination of localized and standardized interoperable messages. Selected groups of recipients would receive the single-entry of the message via SMS, Email, and Web.

Data Collection Event Detection Alerting

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Black arrows: current manual paper/postal system for health data collection and reporting

Red lines: RTBP mobile phone communication system for heath data collection and reporting

Problem the RTBP is trying to solve in Sri Lanka

Reduce 07-15 day delays to Minutes

Re-engineer the limited disease activated passive surveillance to Comprehensive Active surveillance

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Black arrows: current manual paper/postal system for health data collection and reporting

Red lines: RTBP mobile phone communication system for heath data collection and reporting

Problem RTBP is trying to solve in India

Reduce 07-15 day delays to Minutes

Re-engineer the limited disease activated passive surveillance to Comprehensive Active surveillance

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RTBP high level system diagram

Skip the paper

Actors, processes, and information flow of the proposed data collection, event detection, and situational-awareness/alerting real-time program

1. Health records digitized by health workers in Thirupathur block using mobile phones.

2. Disease, symptoms, and demographic information transmitted across GSM mobile network to central database.

3. Data analysed by trained staff at the IDSP and PHC Departments.

4. Automated event detection algorithms process a daily ranked set of possible disease outbreaks, which are presented to IDSP and PHC staff.

5. List of possible outbreaks examined by IDSP and PHC staff to determine likelihood of an adverse event.

6. Confirmed adverse events disseminated to Medical Officers, HIs, nurses, and other health officials, within affected geographic area.

7. Condensed version of the alert pushed through SMS to get immediate attention of the recipients.

8. More descriptive message emailed and published on the web (also accessible through mobile phone).

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Why use mobile for data collection and dissemination?

These charts with demand side statistics were taken from LIRNEasia's teleuse at BOP 3 study - http://lirneasia.net/projects/2008-2010/bop-teleuse-3/

0%

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Bangladesh Pakistan India Sri Lanka Philippines Thailand

Phone ownership (% of BOP teleusers)

Fixed only

Mobile + fixed

Mobile only

http://lirneasia.net/projects/2008-2010/bop-teleuse-3/

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Evaluation metric verticals and horizontals

Three verticals – data collection, event detection and reporting

Four layers – Institutional, content, application, Transport

Arrows showing the Interoperability between layers and verticals

Objectively assess by calculating various indicators: costs, efficiencies, error rates, etc

Subjectively assess through interviews and simulations

Talk aboutthis

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The pilot in India and Sri Lanka

24 Health Sub Center Village Nurses

4 Public Health Center Sector Health Nurses, Health Inspectors, and Data Entry Operators

1 Integrated Disease Surveillance Program Unit of the Deputy Director of Health Services

Thirupathur Block, Sivagangai District, Tamil Nadu, India

12 District/Base Hospitals and Clinics

15 Sarvodaya Suwadana Center Assistants

4 Medical Officer of Health divisions & 1 Regional Epidemiology Unit

Kurunegala District, Wayamba Province, Sri Lanka

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Why digitize the frontline health data?

❒ Activated passive surveillance

❒ Data limited to 25 infectious diseases (avg LK=70 TN=600 monthly health records per district)

❒ Only 20% of the diagnosis are confirmed, rest are probable and suspected, likely to miss signs of emerging outbreak

❒ No data on other-communicable or chronic diseases

❒ Trend analysis is based on < 0.05% of patient population

❒ Planing and resource allocation is based on expert opinion, departments are not data driven

❒ Active surveillance with situational awareness

❒ All data on diseases infectious and chronic diseases (avg 50000 monthly health records per district)

❒ Collect symptoms and signs for syndromic surveillance;

❒ A richer dataset with more attributes

❒ Statistics are on actual patient visitations; even data on snake bites and accidents

❒ Comprehensive data can be used for long term planning and move towards data driven departments

Existing RTBP

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Data collection process

(1) Patient is received by the Nurse

(2) Nurse issues a diagnosis chit to patient fills in Name, Age, Gender, and OPD No

(3) Medical Officer fills in the chit with diagnosis and treatment

(4) Patient presents chit to pharmacy to receive medication

(5) Data Entry Operator digitizes and submits the data

1

2

3

4

5

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mHealthSurvey Midlet by IIT-M

(a) Main menu

(b) Profile registration

(c) Retrieve locations

(d) Patient record screen I

(e) Patient record screen II

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mHealthSurvy software design

J2ME: Built on Java 2 Micro edition

CDC: works with CDC 1.1 and above (JSR)

MIDP: works with MIDP 2.0 or above

GPRS: transport technology

Each record is 2kb and costs INR 0.01 or LKR 0.02 (USD 0.0002) i.e < USD 10 Handset/Month

Mobile phone around US$ 100

Tested on Nokia3110c, Motorola SLVR L7, Gionee v6900. Amoi A636, Sony Ericsson s302c

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mHealthSurvey Certification Exercise

Sri Lanka India Benchmark0.00

10.00

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Part IIIPart IIPart I

Country

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Exercise Benchmark Sri Lanka IndiaPart I – installation and configuration (min) 12.00 10.75 17.48Part II – submit up to 6 records (min) 20.00 10.80 27.26Par III – standard operating procedures (points) 50.00 20.43 15.00

Outcome categorizationCertified trainers ( > 90 points) 02 of 15 NilCertified Users (90 ≥ points > 70) 13 of 15 04 of 23Uncertified (points ≤ 70) --- 19 of 23

Younger (age 18 – 35) Sri Lankan data entry assistants with no health training and no prior experience were quick to adapt to the technology

The relatively older (age 35 – 55) trained health workers in India with 10 – 25 yr experience had difficulty adapting to the software but have improved after some intervention and additional training

Average Country Scores

Exercise conducted 2 months after training and use of the mobile health software

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Quality of the digitized data

The 23% noisy data in India subsided to less than 4% after informing the consequences of false detections (SNR for sub intervals: 0.18, 0.40. 0.31. 0.04, 0.07)

Data quality = Signal to Noise Ratio (SNR); i.e. number records with errors/records submitted

Assistants in Sri Lanka with no formal health training and no affiliation to the hospitals/clinics had no incentive to correct the 45% errors (SNR for sub intervals: 0.58, 0.30, 0.53, 0.57, 0.17)

1 Low quantities of data received from Health Sub Centers2 Volume of records were better after including Primary Health Centers3 Holiday effect: no records received4 Learning curve getting medical officers to adopt to the new procedures of writing the diagnosis5 Release of mHealthSurvey v1.3 with better predictive text

INDIA

SRI LANKA

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Timeliness of data submission

Finding time to complete the records without disrupting current work flow was a significant barrier for real- time data submission (sub interval delay rates 0.28, 0.09, 0.21, 0.38, 0.44, 0.48, 0.68)

Timeliness = submitting the patient’s record the same day as the patient visitation

Data entry assistants have no other role besides digitizing records but see delays proportional to the patient visitation counts (sub interval delay rates: 0.10, 0.27, 0.25, 0.36, 0.53, 0.21).

1 Users with dysfunctional phones where sharing and were sending data on the weekends or when friends phone was available for borrowing

INDIA

SRI LANKA

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Digitizing problems that affect the categorical data

SNOMED-CT

LOINC

UMLS

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Fix the data collection shortcomings: noisy and off-time

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here

From: 01-Sep-2009 To: 30-May-2010

Case Records:

220000+

From: 01-Jun-2009 To: 30-May-2010

Case Records:

81000+

Noisy vs Cleandata

Real-Time vs Off-Timedata

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Observations of the data digitizing uncertainties

No observations for India in this quadrant → Data submitted by health workers in India is consistent

Diseases with higher counts but occurring only in a single location; hence suspected of possible mis-coding by Sri Lankan assistants The likelihood of a measles

outbreak emerging only in a single location without spreading to other areas, given that it is a viral disease, is highly unlikely.

The assistant entering the data had submitted data for “Toxide vaccine” as Tetanus.

These diseases occurred only once in one location

Uniformity of geographic distribution of disease cases ( low: concentrated in a few locations, high: spread over)

Fever greater than 7 days concentrated in February and March of 2010, mainly from a single location, during the non rainy seasonH

isto

rical a

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Data digitization: Some Feedback

“Integrated Disease Surveillance Program Data Entry Operator and Data Manager fear they will lose their jobs if mHealthSurvey and TCWI are introduced. At present these staff members receive phone calls from all health facilities and enter the data in spreadsheets of tabulation of weekly aggregates.” - Senior Project Officer, RTBI, India (19.08.10).

“Data digitizing nurses in India and assistants in Sri Lanka invest their own resources to repair and replace ill-fated mobile phones.” - Field Coordinator, Rural Technology and Business Incubator, India, consulted 18-December-2010 and Field Coordinator, Sarvodaya, Sri Lanka, consulted (26.04.10).

“In the present day setup in Sri Lanka, most of the surveillance data comes from Inward admissions and it is important that the data is collection is expanded to the Outpatient Departments as in the case of this project.” - Wayamba Provincial Director of Health Services, Sri Lanka, consulted (05.04.10).

“Sarvodaya Suwadana Center (primary health center) assistants in Sri Lanka have formed a social network to keep each other informed of escalating health situations in their communities” - Field Coordinator, Kurunegala District, Sri Lanka, consulted (06.10.10).

“For notifiable disease cases, digitizing the patient’s name and address is important for house investigations.” - Village Health Nurse (Keelsevalipatty), workshop report, Sivaganga District, India, consulted (01.10.10).

“It was easier for central officials in Chennai and New Delhi to monitor our individual statistics and performance opposed to scanning through paper or aggregated for the same; therefore, we are afraid to digitize data.” - Village Health Nurses (Nerkupai), Sivaganaga District, India, consulted (29.09.10).

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mHealth dala collection lessons

Nurses sending data

Near zero noise because impacts their work

No time to enter data patient care and routine work comes first

Under reporting to avoid extra work

Improvise mHealthSurvey for collection and reporting of other

Older slow to learn but will catchup

No prior experience beyond voice

Resolve technical problems on their own relative to PCs

Replaced handsets on their own

Outsourced data entry clerks

No incentive because 1) lack of knowledge 2) not direct impact

Data entry is their only job

No strings attached with reporting quantity

Nothing like that

Young were quick to learn

Knew all capabilities of mobile

Resolve technical problems on their own relative to PCs

Replaced handsets on their own

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Four layers – social, content, application, Transport




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T-Cube Web Interface (TCWI) by Auton Lab

AD Tree data structure

Trained Bayesian Networks

Fast response to queries

Statistical estimations techniques

Data visualization over temporal and spatial dimensions

Automated alerts

of 24 Copyright © 2009 by Artur Dubrawski

Bio-surveillance

Interactive analyticsAstrophysics

Food safety

Nuclear threat detection

Learning Locomotion

Safety of agriculture

Fleet prognostics

United Nations CTBTO

Saving sea turtles

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Pre-Screening using Massive Tempotal Scan

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T-Cube Web Interface – Spatio – Temporal Presentation

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Overview of the T-Cube data structure and computations

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Evaluation of TCWI

Replication study :: Sri Lankan Weekly Epidemiological Return (WER) reports published at www.epid.gov.lk notifiable disease counts tabulated by District was semi synthesized by distributing the weekly counts as daily counts taking day-of-week effect, gender distribution, and age representations.

Study the reliability and effectiveness :: significant events detected by T-Cube is compared with the ground truth and also weighed on the response actions or inaction

Competency exercise :: injected fake data over a period of 5 days and the subjects, unaware of the prefabricated events, were asked to detect most significant events

T-Cube Acceptance :: a questionnaire was designed based on the Technology Assessment Methodology (TAM) and was subject to TCWI users as well as health official associated with T-Cube who make decisions on whether or not to take action

Cost analysis :: compare the economic efficiencies and cost effectiveness between present detection/analyses system and T-Cube

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Replication study using synthesized WER data

We took 2007 – 2009 Weekly Epidemiological Returns publicly available data - http://www.epid.gov.lk/

Synthesized the data to match that similar to the RTBP dimensions by distributing the district weekly aggregates

- day-of-week visitation densities (M - F)

- female to male ration

- age-groups (0-5, 6-14, 15-20, 21-45, 46-65, above 65)

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Food poison spike as detected by spatial scan around Feb 15,2007 in Nuwara_Eliya, which was reported as outbreak by health department.

In addition TCWI detected spikes in Kandy and Vauvniya areas

Spatial scan is run by 7 days windows size.

Replication study using Sri Lanka WER data 2007 - 2009

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Another Food poison spike as detected by spatial scan around June 17,2009 in Nuwara_Eliya, the same location.

Spatial scan is run by 7 days windows size.

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Dengue Fever Seasonal and spatial pattern

May 1,2007

Aug 30,2007

May 21,2008 April 15,2009 May 28,2009

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First day an elevated global score noted, lead by region Kandy

4/14

Progression of Dengue Fever outbreak in April – June 2009

Spatial Scan global Score

4/15

Situation in Kandy intensified, together other regions

4/24

Southern Regions began to see increased cases

5/28

Southern region continue to see progression, while other region subsides

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A recent outbreak of Acute Diarrheal Disease in Thirukostiyur area

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Most frequently occurring wide spreading infectious disease outbreaks

Cough, Kurnegala District – Sri Lanka, 11 outbreak episodes to date with over 12,100 cases.

Respiratory Tract Infection, Kurnegala District – Sri Lanka, 09 outbreak episodes to date with over 18,547 cases.

Tonsilitis, Kurnegala District – Sri Lanka, 07 outbreak episodes to date with over 5.086 cases

Respiratory infectious diseases, a correlated with environmental factors, are the most common

These findings are from TCWI's spatial scan algorithms

Common cold is the most popular but gastrointestinal infectious are, relatively, the most visible

Common Cold, Sivaganga District – India, 18 outbreak episodes to date with over 23,188 cases.

Worm Infestation, Sivaganga District – India, 13 outbreak episodes to date with over 1,236 cases.

Dysentery, Sivaganga District – India, 5 outbreak episodes to date with over 1,541 cases.

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Trends in selected Chronic disease

Hypertension (High Blood Pressure) has a linearly increasing trend over the one year period in both countries with Females and Males over 45 years of age showing to be the most vulnerable. The dtrend in India shows an unusual increase between March and May 2010; while the reported cases are consistent throughout the year in Sri Lanka.

Diabetes-Mellitus has a linearly increasing trend over the one year period in both countries with Indians over 40 years of age and Sri Lankan over 45 years of age to be the most vulnerable groups.

Given that the Male to Female ratios, approximately, in Tamil Nadu, India and Kurunegala, Sri Lanka are both 1 : 1; statistics to date show females to be more susceptible to the above mentioned life style diseases.

These findings are from TCWI's statistical estimation and pivot table analysis methods

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Trends in selected Chronic disease

Arthritis and Rheumatoid-Arthritis has a linearly stagnate trend over the one year period in both countries with Males over 45 years of age and Females over 35 years of age to be the most susceptible in India; similarly Males over 45 and Females over 31 years of age to be the most vulnerable groups.

Asthma has a linearly decreasing trend over the one year period in both countries; the dtrend shows the counts to increase during the rainy season, India: Sept'09-Jan'10 and Sri Lanka: Nov '09-Jan '10. In India, only males over 45 years of age are affected but females in all age groups are affected. Both Male and Female over 31 years of age are in Sri Lanka are equally vulnerable.

Given that the Male to Female ratios, approximately, in Tamil Nadu, India and Kurunegala, Sri Lanka are both 1 : 1; statistics to date show females to be more susceptible to the above mentioned life style diseases.

These findings are from TCWI's statistical estimation and pivot table analysis methods

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TCWI Competency Assessments with Injected Synthetic data

Susceptible Exposed Infected Recovered

With a Network Flow

Injected 3 sets of data1) Notifiable disease :: Dysentery2) Other-Communicable disease :: ADD3) Syndrome :: Fever, Pain, RTI

Used “Epigrass” to generate synthetic data with a SEIR model

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TCWI Simulation results

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TCWI Actual Usage by Health Departments

Day-of-Week TCWI is Utilized Time spent each time

3 of 14 potential users spend less than 30 minutes each time once a week on detection analysis; remaining 9 did claim to be too busy to use TCWI

75% of the 9 Sri Lankan users spend more than 30 minutes each time every day of the week on detection analysis.

Day-of-Week TCWI is Utilized Time spent each time

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TCWI Preferred functions

India health officials' primary preferences are screening for fever, other-communicable diseases, and using the pivot table.

Sri Lanka health officials' primary preferences are screening the notifiable, fever, and other-communicable diseases.

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TCWI Technology Assessment Model scores

Technology Acceptance Model was applied to obtain these results on perceived ease of use, perceived usefulness, behavioral interaction, attitude towards using, and psychological attachment

SRI LANKN

The personal feeling is such that, all things considered, TCWI in the job is - quite a good idea, slightly beneficial, quite a wise idea, and slightly positive

INDIAN

This part of the questionnaire was not completed.

Users attitude towards using TCWI

The TAM questionnaire was conducted with 14 Indian and 09 Sri Lankan users (health officials and health workers)

STRONGLY AGREE

AGREE

IMPARTIAL

DISAGREE

STRONGLY DISAGREE

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T-Cube Web Interface: Some Feedback

“We can use this rich and comprehensive dataset and analysis tools for our annual planning,now our planning relies on professional perception and not necessary data.”

- Deputy Director Planning, Kurunegala District, Sri Lanka, Consulted (06.10.09)

“Epidemiologists want TCWI to facilitate the old ways of monitoring outbreaks based on thresholds opposed to statistical significance. For example, a single case of Malaria is regarded as anoutbreak in India, which requires response actions.”

- Deputy Director of Health Services, Sivaganga District, India, Consulted (19.12.09).

“It is important to monitor escalating fever cases, notifiable disease cases, and common clusters of symptoms.”

- Regional Epidemiologist, Kurunegala District, Sri Lanka, consulted (19.12.09).

“Medical Officers, Nurses, Health Educators, etc, who are interested in learning of outbreakssee the benefit and are happy with TCWI detection analysis methods but the staff at the Integrated Disease Surveillance Program are not ready to accept change and want to stick to thetraditional system unless state or national level Authorities mandate it.”

- Senior Project Officer, RTBI, India, consulted (19.08.10).

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T-Cube Web Interface: Some Feedback

“Pharmacists’ perceptions are such that a separate computer shouldbe given for detection analysis and they do not want to share their computers, which are usedfor medicine and birth information.”

- Senior Project Officer, RTBI, India, Consulted (08.07.2010).

“RTBP’s real-time biosurveillance capabilities will enhance the present day passive or non-active passive surveillance to an active surveillance system.”

- Wayamba Provincial Director of Health Services, consulted (07.07.10).

“All cases can be viewed in TCWI in real-time for detecting outbreaks swiftly, which other-wise would take several days before the hospitals/clinics send the notification paper forms, bywhich time the patient may be dead or discharged.”

- Public Health Inspector, Wariyapola, Sri Lanka, consulted (26.04.10).

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TCWI some early lessons

Health departments unfamiliar with statistical estimation methods for detection analysis

Requirements were to observe :: Integrated Disease Surveillance Program (IDSP) P/S disease list

No incentives to use and usage is almost nil

Health departments unfamiliar with statistical estimation methods for detection analysis

Requirements were to observe :: set of Notifiable disease and Weekly Epidemiological Returns report

Have incentives and using due to known delays in present system

Studying the TCWI Acceptance through TAM

RESULTS TO BE ANNOUNCED

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Four layers – social, content, application, Transport




Talk aboutthis

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Existing methods of receiving health alertsSurvey responses from 28 health workers from June 2009 to March 2010

At present health workers learn of adverse health events through MEDIA and WORD-OF-MOUTH, in some cases from PEERS

No formal Government method for sharing health risk information with health workers

Survey responses from 15 health workers from June 2009 to March 2010

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How do we integrate the subscribers and publishers?

How do we deliver early warnings in local language?

How do we use existing market available technologies?

How do we disseminate alerts over multiple channels?

How do we inter-operate between incompatible systems?

How do we effectively communicate the optimal content?

How do we address the communication strategy?

How do we accommodate upstream-downstream alerting?

Problem to solve

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Common Alerting Protocol Overview

□ All you want to know in “CAP Cookbook”

□ XML Schema and Document Object Model

□ Interoperable Emergency Communication Standard

□ Specifically geared for Communicating Complete Alerts

□ Capability for Digital encryption and signature X.509

□ Developed by OASIS for “all-hazards” communication

□ Adopted by ITU-T for Recommendations X.1303

□ Incubated by W3C Emergency Information Interoperability Framework

□ Used by USA, USGS, WMO, Gov of CA

□ Can be used as a guide for structuring alerts

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CAP Document Object Model

Bold elements are mandatory

Bold elements in <Alert> segment are qualifiers

Others elements are optional

Profile may specify other mandatory elements from optional list

Single <Alert> segment

Multiple <Info> segments inside <Alert> segment

Multiple <Area> and <Resource> segments inside a <info> segment

(*) indicates multiple instances are permitted

AlertidentifiersenderSentStatusmsgTypeSourceScopeRestrictionAddressCode (handling code)NoteReferences (Ref ID)Incidents (Incident ID)

InfoLanguageCategoryEvent*responseTypeUrgencySeverityCertaintyAudienceeventCode*Effective (datetime)Onset (datetime)Expires (datetime)senderNameHeadlineDescriptionInstructionWeb (InformationURL)Contact (contact details)Parameter*

ResourceresourceDescmimeTypeSizeURIderefURIdigest

AreaareaDescPolygon*Circle*Geocode*AltitudeCeiling

*

*

*

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Predefined values

CAP Element Predefined Values

<Status> Actual, Exercise, System, Test, Draft

<msgType> Alert, Update, Cancel, Ack, Error

<Scope> Public, Restricted, Private

<Language> en, fr, si, tm, …| codes ISO 639-1

<Category> Geo, Met, Safety, Security, Rescue, Fire, Health, Env, Transport, Infra, CNRNE, Other

<responseType> Shelter, Evacuate, Prepare, Execute, Monitor, Assess, None

<Urgency> Immediate, Expected, Future, Past, unknown

<Severity> Extreme, Sever, Moderate, Minor, Unknown

<Certainty> Observed, Likely, Possible, Unlikely, Unknown

<Area> b-WGS 84

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Prioritizing Messages in CAP

Priority <urgency> <severity> <certainty>

Urgent Immediate Extreme Observed

High Expected Severe Observed

Medium Expected Moderate Observed

Low Expected Unknown Likely

Select value

Auto populate

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Sahana Alerting Broker (SABRO) Subsytems

❏ Inputs can be manual or automated

❏Message creation & validation uses CAP v1.1 and EDXL 1.0 data standards

❏Access control (permissions) and user rules are governed through the Organization Resource Manager (ORM)

❏Direct alerts are sent to end user recipients and Cascade alerts are a system-to-system communication determined by the message distribution method

❏Long-text, Short-text, and Voice-text are different forms of full CAP message for the ease of message delivery to various end-user terminal devices

❏Message acknowledgement logs the recipient messages confirming receipt

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Overview of Sahana

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Sahana Messaging/Alerting CAP/EDXL Broker by Respere

Single input multiple output engine; channeled through multiple technologies

Manage publisher /subscribers and SOP

Adopt PHIN Communication and Alerting Guidelines for EDXL/CAP

Relating the template editor with the SMS/Email Messaging module

Do direct and cascading alert from a regional jurisdictional prospective

Designing short, long, and voice text messages

Addressing in multi languages

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Example of template for SMS alert

<headline> : <status><msgType> for <areaDesc> area with

<priority> priority <event> issued by <senderName>.

Msg: <identifier> sent on <sent>Desc: <description> More detailsWeb: <web>Call: <contact>

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Examaple of Automated Standard Message

Escalating mumps in Kurunegala district for Wariyapola-PHI area

A low priority notifiable disease outbreak issued by Dr Hemachandra.

Msg : nwpdhs-1281246871 sent on 2010-08-08 11:08:57.

Desc : 2 cases of Mumps for 15-20 age group and all genders were reported in Munamaldeniya.

More DetailsWeb www.scdmc.lkCall 2395521

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CAP XML → XSL → delivery method

Single Input Multiple Output Mass Messaging; towards a publisher subscriber model

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CAP short/long text Message delivery methods

Single Input Multiple Output Mass Messaging; towards a publisher subscriber model

Community Suwadana Health Centers

Government Regional Epidemiology and Medical Officer of Health

departments

Government Regional Epidemiology and Medical Officer of Health

departments

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Steps for setting up a CAP Profile

Audience <Scope>Alert First Responders only (i.e. closed user group)

Example: police, health workers, civil society, public servants

Alert Public (entire population)

Combination of First Responders and Public

step 1: alert First-Responders to give them time to prepare

Step 2: warn public

Geographical Descriptions <Area>Country wide

Province or State

District

Other – Geocodes or GPS polygons

National <Languages>English only or Chinese only or Malay onlyEnglish, Hindi, Chinese, and Malay

Communications Technology?Mobile phones – SMS, Cell Broadcast, Email, AppletTV – Text, Audio, VisualAM/FM Radio - Text, AudioVHF/UHF Radio - AudioInternet – HTTP, Email, Webservices

Audience

Geography

Language

Technology

- determining the policy and procedures -

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Downstream messaging structure - INDIA

IDSP PHC

Message Creator – IDSP staff member Message Creator – PHC staff member

Message Issuer - DE Message Issuer - MO

Action alert

Mode of delivery *1 SMS2 Short Email

3 Long Email

Awareness message

Awareness Message

Mode of delivery*1 SMS2 Short Email3 Long Email

Action Alert

RecipientsBMOMOHISHNVHN

RecipientsBMOMOHISHNVHNOther health officials at IDSP

RecipientsBMOMOHI

SHNVHN

Other health officials at IDSP

RecipientsMOSHNHI

VHN

Event Detection

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Downstream messaging structure – SRI LANKA

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Messaging exercises with Sahana Alerting Broker3 users in India and 5 users in Sri Lanka participated in the message dissemination exercises. Each user was presented with four varying scenarios in relation to escalating cases of diseases identified through TCWI and other sources.

Percentage of messages sent on-time (benchmark time-to-completion was 5 minutes)

The security policy of the software, by default, is set to expire the session after 5 minutes to prevent unauthorized use, which forced the user to restart.

Accuracy of creating the messages with populating the common alerting protocol attributes of the software

Templates with pre-populated values and a clear structure helped the users with creating the messages

Correctly selecting the appropriate delivery channels targeting the intended recipients

It was easier to comprehend issuing of alerts but not the the same with issuing situational awareness messages such as the weekly top 5 diseases reports.

INDIA Exercises were incomplete; no results to discuss

35%

65%

30%10%

10%

50%

35%

10% 55%

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3 2 1 0Error0

50

100

Msg received via?

Affected locations?

Event?

Who issued?

Msg Identifier?

Msg priority?

Response actions?

Get more info?

Points

No

of s

ub

ject

s ga

ve a

nsw

er

Que

stio

ns

3 2 1 0Error0

50

100

Msg received via?

Affected locations?

Event?

Who issued?

Msg Identifier?

Msg priority?

Response actions?

Get more info?

Points

No

of s

ub

ject

s ga

ve a

nsw

er

Que

stio

ns

CAP SMS Alert/Situ-aware comprehension exercises

Outcomes

Everyone did quite well in the exercises except for 1 or 2 exceptional cases

Both India and Sri Lanka having trouble with msg-identifier; could be because msg-identifier getting truncated by the 160 char SMS constraint

Recommendation :: put msg- identifier in subject header (but may cutoff rest due to 160 char SMS); use the term “reference number” instead or both

Assessment design

Participants receive 4 SMS text with varying values of the CAP attributes

India = 23 and Sri Lanka = 19 health workers participated in the exercise

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Credibility, Persuasiveness, Validity

Counts, disease, locations, response

CAP Short-text message over SMS, 84 responses for 4 different messages

CAP Short-text message over SMS, 76 responses for 4 different messages

32%

14%8%

28%

18%

Message Authenticity

Call MOHCall IssuerRefer In-ternet

OtherNo Ans

45%

8% 14%

18%

14%

Verify Authenticity?

Call MOHCall IssuerRefer In-ternet

OtherNo Ans

72%

11%

7%11%

Summarize message:

Disease locations sender

Disease locations sender re-sponse

Other

No Ans

17%

7%

7%

70%

Recommendations:

Mention patient de-tails

AdequateOther

No Ans

25%

3%

14%9%

49%

Other Delivery

EmailEmail & Web

Voice

OtherNo Ans

Email, Web, VoiceSender NamelocalizeUse Web/Contact

Expected response

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Some assumptions

❒ The health facilities serve, on average, 100 patients a day; hence, the digitizing capacity of the a single data-entry assistant or single nurse is constrained by this number

❒ Calculations for the existing programs are based on least case and the costs for the RTBP are based on the worst case; e.g. accounted for annual training in RTBP but not in existing system

❒ Indian Primary Health Center (PHC) is both a health facility and a health department; Sri Lanka Hospitals/Clinics and MOH are separate

❒ Reporting is confined to PS List and Morbidity (India); H544, H399, H499, WER (Sri Lanka)

❒ No software licensing fees included such as for the T-Cube Web Interface but that is less than 5% of TCO

❒ Considered only total cost of ownership for the economic analysis, did not incorporate impact related costs such as quality adjusted life years, productivity improved, lives saved, etc

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TCO macro-costs and the marginal differences

Explanation of marginal difference of RTBP macro cost > 20% than existing system

System delivery :: unable to get actual program design, development, and implementation cost, most likely funded by INGO, however, the per district monthly cost is very small.

System Admin/Support :: no established budget, each department spends money for repairs as and when needed. RTBP accounts for it.

Data Center :: India – DPH&PM system is one component of several managed by the National Information Center, in comparison to decentralizing the data centers to be managed at districts

Health Facilities :: major portion of the cost is the new human resource bundled with technology for health record digitization

System delivery, system support, and data center costs are < 7% of overall cost; hence the focus of the economic analysis is on the bulk: health facilities, health departments, and health workers

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Total Cost of Ownership

India and Sri Lanka invest very little or no resources on real-time event detection and alerting, ~ 88% in data collections

RTBP can reduce TCO > 35%, moreover, increase timeliness, and introduce rapid detection and alerting

Existing trend analysis is for long term planning only; dual data-entry at departments.

[ Existing (IN) = present system in India (Integrated Disease Surveillance Program); Existing (LK) = present system in Sri Lanka (Disease Surveillance and Notification Program); RTBP (IN), RTBP (LK) = Real-Time Biosurveillance Program in India and Sri Lanka, respectively]

Comparison of expenses in relation to the data collection, event detection, and alerting components

Digitizing data at the point of care removes the bulk health department expenses of labor intensive data aggregation and consolidation.

Worst case scenario of bundling frontline data digitizing with new resource person increases the health facility investment.

Health facility cost increase < health department money saved; India: 61% < 86%, Sri Lanka: 72% < 87%

Comparison of expenses in relation to the health facility, health department, and health workers

Comparison of Entity Costs in India and Sri Lanka- existing paper-based vs introduced RTBP

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Incremental Cost Effectiveness Ratios (ICER)

Existing ExistingRTBP RTBP

Going from Existing system to RTBP

Data collection – cheaper, more data, more attributes, data available same day (No further analysis required)

Event Detection – cheaper in Sri Lanka, almost same in India, real-time/automated event detection, reports at the touch of a button, globally accessible, no humans needed to feed data, better visualization and analystcs (no further analysis required)

Alerting – new investment and new concept but health workers will be better informed of the regional health status for preparedness and early response (needs further analysis)

Relative RTBP Cost

Relative RTBP

benefits

Expensive & ineffective→

resource unacceptable

Existing

Expensive & effective→

needs further analysis

Cheaper & ineffective→ needs further

analysis

Cheaper & effective→ no

further analysis

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ICT Sy

stem de

livery

Syste

m Adm

in/S

uppo

rt

Data ce

nter

Health

facil

ity

Health

depart

ment

Health

wor

ker

0.00

5,000.00

10,000.00

Alerting (USD)Detection (USD)Collection (USD)

Internal EntitiesCos

t (U

SD

) / D

istr

ict

/ Mon

th

NIC ID

SP Syste

m deliv

ery

Syste

m Adm

in/S

uppo

rt

Data ce

nter

Health

facil

ity

Health

depa

rtmen

t

Health

wor

ker0.00

10,000.00

20,000.00

Alerting / situ-awareEvent DetectionDate Collection


t (U

SD

) / D

istr

ict

/ Mon

th

ICD/W

ER Syste

m deliv

ery

Syste

m Admin

/Sup

port

Data ce

nter

Health

facil

ity

Health

depa

rtmen

t

Health

work

er0.00

10,000.00

20,000.00

Alerting / Situ-awareEvent DetectionData Collection


t (U

SD

) / D

istr

ict

/ Mon

thRTBP can fix the imbalance

Ideally health facilities should be powered for data collection, health departments for detection and alerting, with health workers fully on response

RTBP

Sri Lanka – health departments consume bulk of the resources

India/Sri Lanka – almost zero resources on detection and mitigation

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Economic Efficiencies :: India (annual cost per district)

Present IDSP RTBP

Report LIMITED morbidity/PS-list disease: 77

US$ 3,650.00 / disease

Report LIMITED morbidity/PS-List avg cases::600

470.00 / case

Program for District pop: 1150753US$ 0.24 / inhabitant

Report ALL diseases: 117US$ 800.00 / disease

Report ALL cases: 6175 avg per month15.00 / case

Program for District pop:1150753US$ 0.08 / inhabitant

Data Collection

Event Detection

Monitor LIMITED PS-list disease: 25US$ 402.00 / disease


Monitor ALL disease: 117US$ 135.00 / disease


Alerting

Disseminate LIMITED PS List disease: 25650.00 / disease


Disseminate Communicable disease: 43US$ 800.00 / disease


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Economic Efficiencies :: Sri Lanka (annual cost per district)

Present SNS RTBP

Report LIMITED Notifiable disease: 25US$ 10,800.00 / disease

Report LIMITED WER cases::70321.00 / case


Report ALL diseases: 179US$ 570.00 / disease

Report ALL cases: 22835 avg per month4.50 / case

Program for District pop:1550000US$ 0.07 / inhabitant

Data Collection

Event Detection

Monitor LIMITED Notifiable disease: 25US$ 311.00 / disease


Monitor ALL disease: 179US$ 54.00 / disease


Alerting

Disseminate LIMITED PS List disease: 25285.00 / disease


Disseminate Communicable disease: 49US$ 630.00 / disease


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One-way Sensitivity Analysis

Parameter: Typical Value India Sri Lanka

(I) Health Districts per State 32 2(II) Health Facilities per District 47 44(III) Health Departments per District 48 21(IV) Health Workers per Facility 10 21(V) Currency exchange rate to US$ 45.00 110.00

Mobilizing a Health Worker in the existing system with the infrastructure: paper forms,books, archiving cupboards, workspace and operational expenses: travel costs is much greater than using RTBP technology: mobile phones and simple server with affordable network infrastructure

RTBP technology reduces labor, duplicate data entry, consolidation/aggregation, and manual analyses required by health departments

Digitizing of all patient records in RTBP system may require introducing a new staff member at the health facility

Sensitivity was applied to only parameters II – IV that affect individual components differently. Parameters I and V are direct proportionality that affects the entire cost and not individual components.

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Conclusions mhealthSurvey is a worthy candidate for patient disease/syndrome digitization;

however, must be robust to minimize the noise and delays; need a better GUI if Medical Officers are to enter high volume real-time data opposed to a data entry clerk

Need a complete and comprehensive standardized disease syndrome ONTOLOGY perhaps a hybrid of SNOMED-CT and LOINC

T-Cube false alarm rates must be minimized through the iterative enhancement and machine learning

Sahana CAP Broker SMS, Email, and Web messaging has proven to be a winner for real-time adverse health event information dissemination but need Voice as well

Although the value is seen in T-Cube Web Interface and CAP/EDXL Messaging The policies must be reformed to go beyond the century old paper based system to using ICT based event detection and alerting/situational-awareness

There should not be any institutional fears arising from the cost reductions instigated by the introduction of ICTs (e.g. RTBP) as is will still require the same work force

Before the cost benefits can take affect the laws and regulations must be changed to remove the paper and the storage cupboards that are government mandates

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Conclusions

RTBP costs are less, benefits are better, and efficiency gains are higher than the existing disease surveillance and notification systems

The laws and regulations must be changed to replace the legal forms and registers with electronic health records.

Health record security, privacy, and unique identifiers must be addressed before national implementation

Be ready to accept change; especially the paradigm of comprehensive disease/syndromic active surveillance without paper

There is a severe cost associated with false alarms (false positives) and missed alarms (true negatives), which needs further study and rectifying

Project Partners:

International Development Research Center, Canada - www.idrc.ca

Lanka Jathika Sarovodaya Shramadana Society, Sri Lanka - www.sarvodaya.org

IIT-Madras's Rural Technology and Business Incubator, India - www.rtbi.in

Carnegie Mellon University Auton Lab, USA - www.autonlab.org

University of Alberta, Canada - www.ualberta.ca

National Center for Biological Sciences, India - www.ncbs.in

Respere Lanka (Pvt) Limited, Sri Lanka - www.respere.com

LIRNEasia, Sri Lanka - www.lirneasia.net

Ministry of Health and Nutrition, Wayamba Privince, Sri Lanka - http://www.health.gov.lk/

Health and Family Welfare Department, Tamil Nadu, India - http://www.tnhealth.org/dphpm.htm

Listed in alphabetical order

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References related to RTBP[1] S. Prashant and N. Waidyanatha (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, pp .240-263.[2] Kannan T., Sheebha R., Vincy A., and Nuwan Waidyanatha (2010). Robustness of the

mHealthSurvey midlet for Real-Time Biosurveillance, Proceedings of the 4th IEEE International

Symposium on Medical Informatics and Communication Technology (ISMICT '10), Taipei, Taiwan.[3] M. Sabhnani, A. Moore, and A. Dubrawski (2007). Rapid processing of ad-hoc queries against large sets of time series. Advances in Disease Surveillance, Advances in Disease Surveillance, Vol 2, 2007.[4] S. Ray, A. Michalska, M. Sabhnani, A. Dubrawski, M. Baysek, L. Chen, J, and Ostlund (2008). T-Cube Web Interface: A Tool for Immediate Visualization, Interactive Manipulation and Analysis of Large Sets of Multivariate Time Series, AMIA Annual Symposium, 2008:1106, Washington, DC, 2008.[5] A. Dubrawski, M. Sabhnani, S. Ray, J. Roure, and M. Baysek (2007). T-Cube as an Enabling Technology in Surveillance Applications. Advances in Disease Surveillance 4:6, 2007.[6] G. Gow and N. Waidyanatha (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, pp 268-288.[7] G. Gow, Vincy. P.., and N. Waidyanatha (2010). Using mobile phones in a Real-Time Biosurveillance Program: Lessons from the frontlines in Sri Lanka and India. Proceedings of the 2010 IEEE International Symposium on Technology and Society (ISTAS '10), New South Wales, Australia.[8] Ganesan M., S. Prashant, Janakiraman N., and N. Waidayanatha (2010), Real-time Biosurviellance Program: Field Experiences from Tamil Nadu, India. IASSH conference paper. Varanasi, Uttarpradesh, India.[9] A. Dubrawski (2009). Detection of Events in Multiple Streams of Surveillance Data. In Infectious Disease Informatics: Public Health and Biodefense, Eds. C. Castillo-Chavez, H. Chen, W. Lober, M. Thurmond, and D. Zeng. Springer-Verlag (in press).[10] D. Neill, and G. Cooper (2009). A Multivariate Bayesian Scan Statistic for Early Event Detection and Characterization. Machine Learning (in press).[11] M. Wagner (2008). Methods for testing Biosurveillance systems, Handbook of Biosurveillance (eds. Wagner, M., Moore, M., and Aryel, R.), pp 507-515, Elsevier academic press.

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Real-Time Biosurveillance Program Pilot - India & Sri Lanka

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Transcript of Real-Time Biosurveillance Program Pilot - India & Sri Lanka