Using Remote Sensing and Modeling Techniques to Investigate Malaria Prevalence in Loreto, Peru
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Mousam, Aneela
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Abstract
Peru is a country still working toward completely eliminating malaria. Between 2001 and 2010 significant progress was made towards reducing the number of malaria cases, but the country saw an increase between 2011 and 2012. This work attempts to uncover the associations among various climate and environmental variables and malaria prevalence in the Peruvian region of Loreto, which is located in the Amazon basin. A Multilevel Mixed-effects Poisson Regression model is employed to investigate the relationship between malaria prevalence and climate and environmental conditions during 2009-2013. The results indicate that increase in elevation (β=0.78; 95% confidence interval (CI) 0.75-0.81), soil moisture (β=0.0021; 95% CI 0.0019-0.0022), rainfall (β=0.59; 95% CI 0.56-0.61) and normalized difference vegetation index (β=2.13; 95% CI 1.83-2.43) are associated with higher malaria prevalence, while increase in temperature (β=-0.0043; 95% CI -0.0044, -0.0041) is associated with a lower malaria prevalence. The results from this study are especially useful for healthcare workers in Loreto and have the potential of being integrated within malaria elimination plans.
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Malaria, Peru, Climate, Remote sensing