Giardiasis is a waterborne disease that may be influenced by environmental conditions associated with flooding, stormwater runoff, sewage overflow, and contamination of recreational or drinking water systems. Public health surveillance systems provide important information on reported giardiasis cases; however, due to multiple reasons, these data are often available only at broad geographic scales, limiting their usefulness for localized surveillance planning. This thesis applies a geographic information systems (GIS)-based spatial downscaling framework to estimate how reported statewide giardiasis cases may be distributed across storm–floodplain risk zones in Florida using modeling assumptions.

This study integrated 2022 Florida storm-event data from the National Oceanic and Atmospheric Administration (NOAA), floodplain data from the Federal Emergency Management Agency (FEMA), LandScan population estimates, and reported statewide giardiasis case counts from the National Notifiable Diseases Surveillance System (NNDSS). Storm–floodplain risk zones were created by intersecting 1-kilometer storm-event buffers with FEMA floodplain areas. Exposed population proxy was then summarized within each risk zone and used as the allocation weight for spatial downscaling. The 2022 statewide Florida giardiasis case counts were proportionally redistributed across risk zones based on each zone’s share of the total exposed population proxies. Global Moran’s I and Local Moran’s I were used to evaluate whether the modeled downscaled case estimates were spatially clustered.

The final analytic dataset included 855 storm–floodplain risk zones with a total exposed population of 442,537 individuals. A total of 1,117 reported Florida giardiasis cases were used for downscaling. The downscaled proxy estimates for cases were highly right-skewed, with most risk zones receiving small modeled estimates and a small number of zones receiving much higher estimates. Global Moran’s I test provided statistically significant evidence of positive spatial autocorrelation, suggesting that similar modeled proxy case estimates were geographically clustered. Local Moran’s I identified significant High–High clusters, primarily concentrated in South Florida. Although these results do not represent observed individual case locations or confirmed outbreak areas, they demonstrate how spatial downscaling and spatial autocorrelation analysis can support public health surveillance planning via proxy estimates of cases when disease data are available only at aggregated geographic scale.