Modelling the effect of public health interventions on COVID-19 case counts in a closed population through wastewater surveillance: implications in infectious disease outbreak response and epidemiology

Wastewater surveillance is a rapidly advancing infectious disease management strategy that allows public health organizations to monitor community spread of a disease. Consistent wastewater monitoring can allow for early outbreak detection and inform rapid public health interventions accordingly. Although implementation of wastewater surveillance for SARS-CoV-2 has begun, several challenges remain regarding how to optimally use wastewater data to model community disease dynamics. There is a need to develop frameworks that more accurately translate information on viral wastewater concentration and community infection levels into measurable public health outcomes. This project aims to establish a framework that links wastewater surveillance data to community spread of SARS-CoV-2, and to integrate this data into mathematical models that account for public health responses. We hypothesize that SARS-CoV-2 concentrations in wastewater will be a highly accurate indicator of the effective reproduction number of the virus within the population of interest and that wastewater data will be highly sensitive to the implementation of public health measures in the community. The proposed model will provide insights on expected outbreak size and duration, while also allowing for an exploration of the impact that public health measures had on controlling outbreaks. This model framework will serve as an adaptable solution that can be applied to other pathogens in the future and will ultimately inform other public health response plans. This research fills a critical gap in using novel surveillance technologies to inform public health strategy and will serve as a valuable resource for preventing and controlling communicable disease in the future.