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University of Oklahoma (2021)

Earth observation and mosquito-borne diseases : assessing environmental risk factors for disease transmission via remote sensing data

McMahon, Andrea

Titre : Earth observation and mosquito-borne diseases : assessing environmental risk factors for disease transmission via remote sensing data

Auteur : McMahon, Andrea

Université de soutenance : University of Oklahoma

Grade : Doctor of Philosophy (PhD) 2021

Résumé partiel
Despite global intervention efforts, mosquito-borne diseases remain a major public health concern in many parts of the world. New strategies to target interventions rely on spatially explicit information about disease transmission risk. Because the transmission of mosquito borne diseases is influenced by environmental conditions, environmental data are often used to predict disease risk. However, the relationships between environmental conditions and such diseases are not homogeneous across different landscapes and requires a context-dependent understanding. The research presented in this dissertation consists of three case studies that used remote sensing data to identify environmental risk factors for mosquito-borne diseases in different geographic settings. In the first project, the distribution of malaria cases in two study areas in the Amhara region of Ethiopia was analyzed with the help of remote sensing data on land surface temperature, precipitation, spectral indices, as well as land cover and water availability. Environmental variables were derived from remote sensing data and their relationships with spatial and temporal patterns of malaria occurrence were investigated. Settlement structure played an important role in malaria occurrence in both study areas. Climatic factors were also important, with relative risk following a precipitation gradient in the area by lake Tana and a temperature gradient along the Blue Nile River escarpment. This research suggests that studies aiming to understand malaria-environmental relationships should be geographically context specific so they can account for such differences. Second, the spatial distribution of West Nile virus (WNV) risk in South Dakota was studied via different geospatial environmental datasets. We compared the effectiveness of 1) land cover and physiography data, 2) climate data, and 3) spectral data for mapping the risk of WNV transmission. The combination of all data sources resulted in the most accurate predictions. Elevation, late season (July/August) humidity, and early-season (May/June) surface moisture were the most important predictors of disease distribution. Indices that quantified inter-annual variability of climatic conditions and land surface moisture were better predictors than inter-annual means. These results suggest that combining measures of inter-annual environmental variability with static land cover and physiography variables can help to improve spatial predictions of arbovirus transmission risk. Third, mosquito populations in Norman, Oklahoma, were analyzed to investigate the influences of land cover and microclimate on the abundance of vector mosquitoes in a heterogeneous urban environment. Remotely-sensed variables, microclimate measurements, and weather station data were used to study patterns of mosquito abundances. Spatial distributions of the two vector species Ae. albopictus and Cx. quinquefasciatus were strongly associated with land cover variables. Impervious surface area positively affected the abundance of both species.

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