Development of an Early Warning System for the Outbreak of Japanese Encephalitis using Remote Sensing and GIS

1. Development of an early warning system for the outbreak of Japanese encephalitis with the help of Remote Sensing and GIS in conjunction with the epidemiological studies in Assam Bijoy Krishna Handique, Scientist/Engineer-SD bkhandique@gmail.com North Eastern Space Applications Centre, Dept. of Space, Umiam 793103, Meghalaya, India

2. Background • Japanese Encephalitis (JE), commonly known as ‘brain fever’ is a mosquito borne viral disease. • The epidemiology of JE is complex due to a complex transmission cycle of the virus involving a variety of vertebrate and invertebrate hosts. • Remote Sensing serves as a tool for surveillance of vector habitats • Geographic Information System (GIS) as a powerful tool helps in integrating multiple layers of information with spatial analysis for identifying the risk prone areas

3. Average mortality and morbidly indicators (average per year) of JE in Assam (1980-2007) • Cases - 295.5 : Mortality - 114.9 : Case Fatality Ratio - 40.9% • Incidence per million - 12.5 : Mortality rate per million - 4.7 • Stratification of JE shows four clear zones in Assam according to endemicity: • Highly endemic districts: Dibrugarh, Lakhimpur, Sivasagar, Jorhat and Sonitpur • Moderately endemic: Kamrup, Darrang, Karbi-Anglong, Golaghat and Nagoan • Low endemic: Cachar, Dhubri, Goalpara, Kokrajhar, Nalbari and Borpeta • Non endemic: North Cachar Hills and Karimganj

4. Objectives: • To evaluate the role of ecological factors that may be modulating the abundance of vector mosquitoes with the help of Remote Sensing data in GIS domain along with the ground data on vector characteristics. • To monitor the seasonal density of the potential JE vectors and to describe temporal changes in their abundance. • To formulate a model to forecast JE outbreaks 2 to 3 months ahead, to enable health personnel tackle the outbreak with efficient use of available man and material • Develop a decision support system towards development of an early disease warning system in the GIS domain.

5. FORECASTING OUTBREAK OF JE WHEN ?WHERE ?HOW SEVERE ?

6. Methodology Flowchart

8. Dominant types of vector habitats were selected for collecting adult and immature mosquitoes at monthly intervals • Vector abundance is measured in terms of Man Hour Density (MHD) and species diversity

9. Species richness of mosquito vectors were measured with Shannon Weiner Diversity Index, given by Where is the index value ‘ni’ number of species ‘N’ is the total number of species in that habitat type.

10. Shannon Weiner Diversity Index of Mosquito Species at Different Sample Locations

11. Vector habitat Characterization HIGH MOSQUITO DENSITY (ADULTS) VECTOR ABUNDANCE REMOTE SENSING BASED VECTOR HABITAT CLASSIFICATION MEDIUM MOSQUITO DIVERSITY (IMMATURES) LOW

12. Forecasting of onset of JE WEATHER PARAMETERS - Rainfall, Temp (Max), Temp (Min), RH(M), RH(E) correlated with JE onset data since 1985 EARLY April-May REGRESSION MODEL AND DISCRIMINANT ANALYSIS NORMAL June-JULY WHEN ? 9th and 11thSMW WEATHER PARAMETERS FOUND TO HAVE SGNIFICANT EFFECT ON ONSET OF JE LATE AUG-SEP

13. Observation of Discriminant analysis: Canonical Correlation and Eigen values of these categories have been found significant and shows that discrimination is possible among these categories. Percent of variance and cumulative percentage of group variables have been worked out. Cannonical correlation co-efficients for first two discriminant functions have been found to be 0.867 and 0.432 Eigen values worked out are 3.028 and 0.229 where, 83.3% of original grouped cases have been classified correctly.

14. Forecasting onset of the disease: Weather parameters of 9th and 11th standard meteorological week (SMW) exhibit best fit in regression model It also gives highest classification accuracy (83.3%) in the discriminant analysis Discriminant analysis carried out taking the weather parameters as the dependent variable and taking occurrence of JE cases as the group variable. Three categories are made as – Early - If onset of disease is reported in the months of May - June Normal - If onset of disease is reported in the months of June - July Late - If onset of disease is reported in the months of Aug. – Sept A early onset of JE cases (May-June) was predicted for the year 2008 for the Dibrugarh district.

15. Sero-conversion study initiated Sero-conversion study initiated • Relation between vector density, JE cases and • human and pig sero-positivity Relation between vector densities with JE virus activity in Khowang PHC Relation between vector densities with JE virus activity in Naharani PHC

16. MAPPING OF VILLAGES WITH JE RISK MAPPING AREAS OF VECTOR ABUNDANCE VERY HIGH YEAR-WISE DISTRIBUTION OF JE CASES HIGH WHERE ? CATEGORIES OF PIG REARING COMMUNITIES LOW

18. 21 YEARS’ CASE DATA LOG TRANSFORMED TIME SERIES ADJUSTED FOR SEASONAL VARIATION IN A PHC CASE INCIDENCE PREDICTED FOR EACH PHC HOW SEVERE ?

19. PHC wise predicted JE cases for the year 2005

20. PHC wise Observed JE cases during 2005

21. PHC wise predicted JE cases for the year 2006 adjusting long term and cyclic trend

22. Accuracy of forecast during 2005 • Prediction of the 6 PHCs of Dibrugarh district: 55 cases • Observed cases: 45 • Onset forecast: Early (May) • Observed onset: May • JE prone areas: 85% cases from high prone areas • Accuracy of forecast during 2006 • Prediction of the 6 PHCs of Dibrugarh district: 46 cases • Observed cases: 39 (21 MAC ELISA positive cases) • Onset forecast: Normal (June) • Observed onset: June • JE prone areas: 90% cases from high prone areas

23. Decision Support System • List of JE prone villages along with forecasted onset of the disease is supplied to Joint Director of health services of Dibrugarh District and Assam Medical College, Dibrugarh in the month of March. • These information are provided to the Villages Resource Centres (VRC) of the district. VRCs have telemedicine connectivity • Regional Medical Research Centre (ICMR) provide feed back and accuracy of forecast each year.

24. Thank You