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
North Eastern Space Applications Centre, Dept. of Space,
Umiam , 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 : Mortality : Case Fatality Ratio %
Incidence per million : 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 and 0.432
Eigen values worked out are and 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. Relation between vector density, JE cases and
human and pig sero-positivity
Sero-conversion study initiated
Relation between vector densities with JE virus activity in Khowang PHC
Sero-conversion study initiated
Relation between vector densities with JE virus activity in Naharani PHC

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

18. HOW SEVERE ? 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