Satellite-observed sensitivity of weather condition for predicting malaria vector distribution in Bandarban district, Bangladesh

Mohammad Nizamuddin; Atiqur Rahman; Leonid Roytman; Felix Kogan; Al Powell; Mitch Goldberg

doi:10.1117/12.818316

30 April 2009 Satellite-observed sensitivity of weather condition for predicting malaria vector distribution in Bandarban district, Bangladesh

Mohammad Nizamuddin, Atiqur Rahman, Leonid Roytman, Felix Kogan, Al Powell, Mitch Goldberg

Author Affiliations +

Proceedings Volume 7312, Advanced Environmental, Chemical, and Biological Sensing Technologies VI; 73120T (2009) https://doi.org/10.1117/12.818316
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

A better understanding of the relationship between malaria epidemics, satellite data and the climatic anomalies could help mitigate the world-wide increase in incidence of the mosquitotransmitted diseases. This paper analyzes correlation between malaria cases and vegetation health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) computed for each week over a period of 14 years (1992-2005). Following the results of correlation analysis the principal components regression (PCR) method was performed on weather components (TCI, VCI) of satellite data and climate variability during each of the two annual malaria seasons to construct a model to predict malaria as a function of the VH. A statistically significant relation was found between malaria cases and TCI during the month of June-July and September-October. Furthermore the simulated results found from PCR model were compared with observed malaria statistics showing that the error of the estimates of malaria is 5%.

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Mohammad Nizamuddin, Atiqur Rahman, Leonid Roytman, Felix Kogan, Al Powell, and Mitch Goldberg "Satellite-observed sensitivity of weather condition for predicting malaria vector distribution in Bandarban district, Bangladesh", Proc. SPIE 7312, Advanced Environmental, Chemical, and Biological Sensing Technologies VI, 73120T (30 April 2009); https://doi.org/10.1117/12.818316

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KEYWORDS

Satellites

Error analysis

Dysprosium

Vegetation

Climatology

Meteorological satellites

Statistical analysis

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