Application of advanced very high resolution radiometer (AVHRR)-based vegetation health indices for estimation of malaria cases

Abstract

Satellite data may be used to map climatic conditions conducive to malaria outbreaks, assisting in the targeting of public health interventions to mitigate the worldwide increase in incidence of the mosquito-transmitted disease. This work analyzes correlation between malaria cases and vegetation health (VH) indices derived from satellite remote sensing for each week over a period of 14 years for Bandarban, Bangladesh. Correlation analysis showed that years with a high summer temperature condition index (TCI) tended to be those with high malaria incidence. Principal components regression was performed on patterns of weekly TCI during each of the two annual malaria seasons to construct a model as a function of the TCI. These models reduced the malaria estimation error variance by 57% if first-peak (June-July) TCI was used as the estimator and 74% if second-peak (August-September) was used, compared with an estimation of average number of malaria cases for each year.

Figure 1.

Time series for the population, patients, and the malaria cases.

Figure 2.

Correlation coefficient of the annual malaria case index DY vs. weekly Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI).

Figure 3.

Temperature Condition Index (TCI) dynamics for the years with the lowest and largest annual malaria cases.

Figure 4.

Estimated and observed malaria cases with Temperature Condition Index (TCI) principal component (PC) in A, Weeks 33–37 and B, Weeks 22–27.