Using remote sensing and modeling techniques to investigate the annual parasite incidence of malaria in Loreto, Peru

Between 2001 and 2010 significant progress was made towards reducing the number of malaria cases in Peru; however, the country saw an increase between 2011 and 2015. This work attempts to uncover the associations among various climatic and environmental variables and the annual malaria parasite incidence in the Peruvian region of Loreto. A Multilevel Mixed-effects Poisson Regression model is employed, focusing on the 2009-2013 period, when trends in malaria incidence shifted from decreasing to increasing. The results indicate that variations 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) is associated with higher annual parasite incidence, whereas an increase in temperature (β = -0.0043; 95% CI, − 0.0044-− 0.0041) is associated with a lower annual parasite incidence. The results from this study are particularly useful for healthcare workers in Loreto and have the potential of being integrated within malaria elimination plans.