Background: Malaria remains one of the world's most devastating public health threats. In Peru, 75% of malaria occurs in the northern Amazon region of Loreto where 80% of cases are concentrated in just 10 districts. Loreto is the least densely populated region of Peru and also the largest. To maintain the declining malaria rates currently seen, better knowledge of where, when and why people are infected is needed. The primary factors affecting malaria endemicity in Loreto are vector habitat expansion from land use change, and social and ecological processes that increase human exposure. Coupled with this, changes related to climate, including rainfall and flooding, temperature, humidity and soil moisture are all also linked to the growth and survival of both the parasite (Plasmodium sp.) and the dominant mosquito vector in the Amazon,Anopheles darlingi. It remains unclear in this region where prevention efforts should be targeted based on the complex suite of factors involved. To refine and focus prevention strategies, spatially explicit risk estimates are necessary. Aims: In this study, we investigate how malaria risk varies across time and space in Loreto by modeling the relationship among climate, land use, and malaria from 2001 to 2012. Methods: Using a poison random effects model, we incorporate annual measures of land use, spatial ecology, and weekly climate variables with weekly epidemiological data reported from 356 government health posts in Loreto over twelve years. Results: Initial models indicate increased malaria risk for lagged rainfall and soil moisture as well as land areas prone to flood. Conclusions: These models will be compared against current forecasting methods to determine if more efficient prevention and control efforts can be implemented.
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