Linking Environmental Factors To Vector Abundance And Malaria Incidence: A Multi-Model Investigation Of Disease Ecology.

This dissertation examined the complex interactions between environmental, climatic, and entomological factors influencing malaria transmission in Mozambique. Malaria remains a leading cause of morbidity and mortality across the country, where transmission dynamics are shaped by ecological heterogeneity, vector behavior, and changing environmental conditions. By integrating epidemiological, entomological, environmental, and climate data, this work aimed to strengthen the evidence base for targeted, data-driven malaria control strategies suited to Mozambique’s diverse ecological settings.Manuscript 1 investigated the impact of environmental and housing structure on malaria infection, as measured by rapid diagnostic tests, in Sussundenga Village, Mozambique. Results demonstrated that microenvironmental conditions such as landcover type (grassland and cropland) and housing characteristics such as whether there were holes in the wall, were significant predictors of malaria infection risk. Manuscript 2 provided one of the first national-scale analyses of Anopheles mosquito abundance in Mozambique, integrating five years of entomological surveillance with satellite-derived environmental data. Results demonstrated distinct spatial and temporal heterogeneity in mosquito captures, characterized by frequent zero counts punctuated by occasional high-density events. Land surface temperature was found to be a strong positive predictor of Anopheles abundance, confirming that warmer conditions promote mosquito proliferation. Landcover composition was also shown to have important effects. Higher proportions of urban or vegetated areas were associated with increased vector abundance, while greater water coverage was unexpectedly associated with lower mosquito counts. Manuscript 3 expanded upon these findings by examining environmental and climatic variables, together with mosquito abundance, as predictors of malaria infection at the national scale. The final integrated model identified LST, NDVI, and Anopheles abundance as the principal predictors of malaria incidence, providing a robust, ecologically grounded framework for understanding malaria transmission dynamics. Taken together, these studies provide a multidimensional understanding of malaria transmission in Mozambique.

Author dc:creator

• Steiber, Alexa

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/11299/279279

OAI identifier oai:identifier

oai:conservancy.umn.edu:11299/279279