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Abstract
There is a need to enhance the public health surveillance infrastructure by leveraging novel surveillance tools that can provide comprehensive, reliable, and rapid surveillance data to improve community health outcomes. Currently, the standard approach to generating public health surveillance data is by use of clinical diagnostics which severely underestimates community-level dynamics of diseases due to its dependence on community access to timely healthcare and individual health-seeking behaviors for reporting. Wastewater-based epidemiology is a novel surveillance tool that analyzes wastewater, as a pooled community sample, for biomarkers of disease to monitor and assess the overall health of a community by tracking trends, detecting outbreaks, and monitoring the genomic diversity of circulating pathogens. Wastewater-based epidemiology has the potential to meet the needs in public health surveillance to improve community health outcomes. I hypothesize that wastewater-based epidemiology can provide comprehensive, reliable, and rapid surveillance data to inform implementation of timely and effective public health actions. The objective of my study is to assess and implement novel quantitative- and genomic-based analytical methods used for wastewater-based epidemiology to better understand disease dynamics within a community. I applied these methods to two diseases that have major impacts and a high burden on human health: COVID-19 and Salmonellosis. Through field- and lab-based experiments, I aim to optimize, implement, and assess the operational capacity and effectiveness of wastewater-based epidemiology as a novel tool for monitoring community health. Wastewater-based epidemiology can provide a cost-effective complement to fill the gaps left by traditional clinical reporting for community-level disease surveillance. This project will provide two analytical tools and present a model for the implementation of wastewater-based epidemiology in communities to aid in public health surveillance.
