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

数学建模在评估和预测新冠肺炎疫情的影响以及为公共卫生政策提供信息方面发挥了至关重要的作用。然而，现有模型往往没有考虑到漏报的临床病例，导致对流行病学参数的不准确估计和有缺陷的预测。与此同时，废水监测已经成为一种很有前途的工具，可以从不同的人群中获取数据，包括没有症状的个人和那些没有通过临床测试获得的数据。尽管有潜力，但将废水数据与传染病的数学模型相结合在很大程度上仍未得到探索。该项目旨在通过利用废水监测数据加强动态人口一级流行病学模型的校准来弥合这一差距。通过纳入废水数据，该项目寻求改善对真实疾病流行率的估计，加强对未来病例的预测，并监测病毒变异的出现和演变。制定的数学框架对于持续监测新冠肺炎和类似疾病至关重要，使公共卫生官员能够评估干预措施的有效性并制定相应计划。这项研究积极吸引本科生，特别是来自代表性不足的背景的本科生，培养STEM领域的多样性和包容性。该项目为劳伦斯理工大学的课程和项目开发做出了贡献，建立了一个可持续和跨学科的数学生物学研究项目。该项目旨在解决用于模拟传染病传播的现有数学框架的局限性，由于无症状个人和低自我报告导致的漏报病例，该框架经常遭受不充分的校准。因此，关键的流行病学参数，如基本繁殖数量估计得很差，导致预测不准确，对推动感染传播的潜在机制了解有限。为了克服这些挑战，该项目将开发机械数学框架，以增强传统的SIR类型(易感-感染-恢复)模型，该模型通常与常微分方程组有关。这些增强的模型将纳入两个额外的数据来源：在废水中发现的病毒RNA副本和在粪便样本中发现的病毒RNA副本。废水病毒RNA拷贝的加入将在SIR模型中引入一个新的变量，控制废水中病毒浓度随时间的动态变化。代表平均感染者随时间排出的病毒量的病毒排出曲线将使用来自临床粪便样本的参数进行现象学建模。为了进一步提高脱落曲线的准确性并深入了解其潜在机制，将开发一种宿主内病毒模型，包括胃肠道内的未感染细胞、感染细胞和免疫反应。整个建模框架将扩展到考虑病毒变体，方法是将感染类别划分为不同的隔间，每个隔间具有特定于变种的参数，如传播率、疫苗抵抗力和再感染率。将使用适当的数据集对所得到的数学模型进行分析、数值模拟和参数化。将开发用户友好的计算程序包，以促进这些模型的实施及其与公共卫生数据库的接口。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.