Environmental surveillance (ES) is an effective epidemiological tool that has successfully been used to monitor poliovirus circulation over the past three decades. ES has also emerged as a low-cost method to detect and estimate incidence of other diseases, such as typhoid fever and COVID-19. A limitation of ES in its current capacity stems from its reliance on sound working knowledge of the sewerage and sanitary system in the setting where it is being used. Without this knowledge, positive detection of a target pathogen in ES samples cannot be traced upstream to the likely source population. Additionally, selecting sites to collect samples for ES is speculative when this knowledge is absent, and samples may not capture the subpopulation of interest and/or may capture overlapping subpopulations. Advances in digital elevation models (DEMs) captured through remote sensing, and their use in hydrologic modeling in ArcGIS, have expanded the potential for ES to be applied to areas where the sewage drainage systems are: 1) not well understood; 2) open to the environment; or 3) gravity-fed. Although a useful tool, historically this approach is less accurate when derived from free, open-access DEM datasets and applied to settings with hydraulic structures. This thesis demonstrates an innovative mixed-methods mapping approach that addresses the current limitations of ES and the use of catchment area delineation for ES using free, open-access data. DEM Reconditioning, a tool in ArcHydro, was applied to improve the accuracy of free DEMs. This methodology was applied to two case studies in unique settings where ES research is underway. The first case study is in Kolkata, India, where ES samples were collected and tested for Salmonella (S.) Typhi and S. Paratyphi A to supplement clinical surveillance of typhoid and paratyphoid fever. A hybrid model was developed that integrates information about hydraulic structures and hydrologic processes that comprise the sewage drainage system in Kolkata. This model was used to estimate the geographic area and population size represented by sewage samples collected from thirteen pumping stations. In the second case study, a catchment area model was developed to identify optimal sampling locations for ES of COVID-19 in Accra, Ghana. This thesis confirmed that a mixed-methods approach can unify free, open-access information and be used to map sewage drainage systems in settings where ES has historically been limited. The Kolkata model demonstrated how this methodology can be used retroactively to inform interpretation of results from ES samples, and it catalyzed discussion about the integrity of the results for settings with flat terrain and extensive artificial canals. The Accra model demonstrated that this process can be used proactively to identify strategic sampling points that capture subpopulations of interest, prevent catchment population overlap, and maximize coverage. When the estimates from the Kolkata and Accra models were compared to watershed-based estimates from a consulting firm for these sites, we concluded that the methodology described in this thesis likely led to more accurate catchment size estimates.
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About this Master's Thesis
|Committee Chair / Thesis Advisor
|A Mixed-Methods Catchment Area Modelling Approach using Free DEMs to Inform Environmental Surveillance: A Double Case Study Analysis ()
|2021-05-02 17:18:47 -0400