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Modeling and Forecasting Malaria in Tripura, INDIA using NOAA/AVHRR-Based Vegetation Health Indices

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    Title: Modeling and Forecasting Malaria in Tripura, INDIA using NOAA/AVHRR-Based Vegetation Health Indices
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    Date: 2016
    Publication type: Technical report
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    1. SEP C
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    Keywords
    1. AVHRR
    2. Malaria
    3. Principal Component Regression

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    Venue
    NOAA‐CREST, City College, New York, NY, USA

    Abstract

    Improved forecasting, prevention and control of epidemics are the key technical elements for malaria eradication program. The objective is to use NOAA/AVHRR environmental satellite data to produce weather seasonal forecasts for using as a proxy for predicting malaria epidemics in Tripura state, India which has one of the highest endemic of malaria cases in the country. An algorithm has been reported that uses Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) computed from Advance Very High Resolution Radiometer (AVHRR) data flown on NOAA afternoon polar orbiting satellite. A significant relationship between satellite data and annual malaria incidences is found at least three months before the major malaria transmission period. Principal component regression (PCR) method was used to develop a model to predict malaria as a function of the TCI. The simulated results were compared with observed malaria statistics showing that the error of estimations of malaria is insignificant. Optical remote sensing therefore is a valuable tool to estimate malaria well in advance so that preventive measures can be taken.