This dissertation shows the importance of understanding the influence of large-scale environmental

This dissertation shows the importance of understanding the influence of large-scale environmental, human, and pathogen factors on specific public health issues in coastal and non-coastal areas. Results show that these interactions are complex, and that there is a combined effect of environmental factors, thus looking at them separately might not provide a complete understanding. Therefore, the combination of these factors should be taken into consideration in future work, as well as those other factors that were not included due to data limitations (discussed below). Nevertheless, this study contributes to the understanding of environmental and demographic factors that should be included for early warning systems and to improve mitigation and management strategies.
Predictive models used for Mexico and Puerto Rico looking at dengue fever occurrences and FIB exceedances showed high predictive capabilities. Models can be further improved by including data that was not considered in this dissertation. For example, for dengue fever predictions, seroprevalence and human population movement should be considered to better understand occurrences and peaks in dengue fever. Likewise, different populations segments (i.e., age groups) were considered for this study, but these age groups could be either expanded or divided differently for better predictions, according to information available on the limitations to their immune system. In terms of the FIB, models can be improved by including sanitation infrastructure, river and stormwater discharge, and wastewater treatment plant outflows. These FIB can also be found in sediments/sand and vegetation, which should also be considered in the future. Lastly, time series length can influence outcomes due to lack of data, overfitting, and underfitting. Those Puerto Rico models used 19 years (dengue) and 11 years (fecal indicator bacteria) of data, while Mexico models used years of data (dengue). Nevertheless, these models yielded high predictive capabilities, and future studies should consider expanding time series to better predict specific health-related occurrences.
The application of remote sensing data should be considered in future efforts to better understand phenology of vector-borne diseases and recreational water quality. Results of this work provide managers and public health practitioners the data needed to better model and understand public-health related issues in coastal areas. Also, this dissertation provides specific limitations such as epidemiological, demographic, and environmental data not being available to further improve management, targeted sampling, and early warning systems.

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