Investigating the potential of wastewater surveillance data to improve SARS-CoV-2 dynamical modeling and forecasting

ABSTRACT Policy makers, researchers and public health officials rely on forecasts from infectious disease models to anticipate resource use, assess risk, and plan public health interventions. In order to effectively and accurately predict future cases and hospitalization rates of COVID-19, these models depend on high quality data on infections in the population. However, as clinical testing becomes less reliable with the ubiquity of at-home antigen tests and the frequency of case reporting is decreased in many states, there is a critical need to identify alternative data sources. Wastewater-based surveillance is a promising supplement to traditional data sources for monitoring infection at the population level: it is anonymous, cost-effective, and unaffected by variation in clinical testing and reporting. It also reflects infections in the population days or weeks sooner than they would be captured by reported cases or hospitalizations. While there is ample evidence that wastewater concentrations of SARS-CoV-2 correlate with COVID-19 cases, wastewater data have rarely been used to inform modeling and forecasting, and the benefit of incorporating wastewater surveillance data into data-driven dynamical models for forecasting has not been rigorously assessed. The goal of this project is to leverage a rich dataset from the New York State Wastewater Surveillance Network to evaluate the potential for wastewater surveillance data to improve inference of SARS-CoV-2 transmission dynamics. Partnering with the New York State Wastewater Surveillance Network, which includes over 150 wastewater treatment plants across 61 counties of New York State and represents a population of more than 15 million, provides a unique opportunity to investigate the value of wastewater surveillance for modeling at a larger scale. We will build on an existing metapopulation model developed by the Shaman Lab to test how inference and forecasting of transmission dynamics can be improved by integrating wastewater data. In other words, we will use retrospective data from New York State to rigorously assess: can we predict future cases more accurately by using wastewater data to inform our models? This study would be the first that we know of to use a metapopulation structure, which considers population movement, to model SARS-CoV-2 with wastewater surveillance data. By more realistically modeling how human populations move and mix, this model structure can better capture real-world transmission dynamics. This will be crucial to realistically evaluate whether wastewater data can be relied on in the future to forecast COVID-19 incidence, which will be particularly important as clinical testing fluctuates and case reporting becomes sparser. It may also motivate the application of wastewater-based modeling to other pathogens detectable in feces, facilitating the development of prompt, cost-effective forecasts of other communicable diseases in the future.