LEAPS-MPS: Enhancing Dynamic Population-Level Epidemiological Models by Incorporating Wastewater Surveillance Data

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Mathematical modeling has played a crucial role in assessing and forecasting the impact of the COVID-19 pandemic and informing public health policies. However, existing models often fail to consider underreported clinical cases, resulting in inaccurate estimates of epidemiological parameters and flawed forecasts. Meanwhile, wastewater surveillance has emerged as a promising tool for capturing data from a diverse population, including asymptomatic individuals and those not captured by clinical testing. Despite its potential, integrating wastewater data with mathematical models of infectious diseases remains largely unexplored. This project aims to bridge this gap by leveraging wastewater surveillance data to enhance the calibration of dynamic population-level epidemiological models. By incorporating wastewater data, the project seeks to improve the estimation of true disease prevalence, enhance forecasting of future cases, and monitor the emergence and evolution of viral variants. The developed mathematical frameworks will be vital for the ongoing monitoring of COVID-19 and similar diseases, enabling public health officials to assess the effectiveness of interventions and plan accordingly. This research actively engages undergraduate students, particularly from underrepresented backgrounds, fostering diversity and inclusivity in STEM fields. The project contributes to the curriculum and program development at Lawrence Technological University, establishing a sustainable and interdisciplinary research program in mathematical biology.This project aims to address the limitations of existing mathematical frameworks used to model infectious disease spread, which often suffers from inadequate calibration due to underreported cases resulting from asymptomatic individuals and low self-reporting. As a consequence, critical epidemiological parameters, such as the basic reproduction number, are poorly estimated, leading to inaccurate forecasts and a limited understanding of the underlying mechanisms driving infection transmission. To overcome these challenges, the project will develop mechanistic mathematical frameworks that enhance traditional SIR-type (Susceptible-Infectious-Recovered) models, commonly associated with a system of ordinary differential equations. These enhanced models will incorporate two additional sources of data: viral RNA copies found in wastewater and viral RNA copies found in stool samples. The incorporation of wastewater viral RNA copies will introduce a new variable into the SIR-type model, governing the dynamics of viral concentration in wastewater over time. The viral shedding curve, representing the amount of virus shed by an average infected person over time, will be modeled phenomenologically using parameters derived from clinical stool samples. To further improve the accuracy of the shedding curve and gain insights into its underlying mechanisms, a within-host virus model will be developed, incorporating uninfected cells, infected cells, and immune responses within the gastrointestinal tract. The overall modeling framework will be extended to account for virus variants by dividing the infectious class into distinct compartments, each with variant-specific parameters such as transmissibility, vaccine resistance and reinfection rate. The resulting mathematical models will be analyzed, numerically simulated, and parameterized using appropriate datasets. User-friendly computational packages will be developed to facilitate the implementation of these models and their interface with public health databases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.