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

LEAPS-MPS：通过纳入废水监测数据来增强动态人口水平流行病学模型

• 批准号: 2316809
• 负责人: Bruce Pell
• 金额: $ 24.93万
• 依托单位: Lawrence Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316809&HistoricalAwards=false
• 关键词: LEAPS; MPS; Enhancing; Dynamic; Population

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.

数学建模在评估和预测Covid-19大流行的影响并告知公共卫生政策的影响方面发挥了至关重要的作用。但是，现有模型通常未能考虑不足的临床病例，从而导致对流行病学参数和有缺陷的预测的估计不准确。同时，废水监视已成为一种有前途的工具，用于捕获来自不同人群的数据，包括无症状的个体以及未经临床测试未捕获的人。尽管具有潜力，但将废水数据与传染病的数学模型集成在一起仍然很大程度上没有探索。该项目旨在通过利用废水监视数据来弥合这一差距，以增强动态人群级流行病学模型的校准。通过纳入废水数据，该项目旨在提高对真正疾病患病率的估计，增强对未来病例的预测，并监测病毒变异的出现和演变。开发的数学框架对于正在进行的COVID-19和类似疾病的监测至关重要，使公共卫生官员能够评估干预措施的有效性并相应地计划。这项研究积极参与本科生，特别是从代表性不足的背景中，促进了STEM领域的多样性和包容性。该项目为劳伦斯技术大学的课程和计划开发做出了贡献，建立了数学生物学的可持续和跨学科研究计划。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，这些局限性通常因不足而导致的校准不足而导致不足的人，因此受到了不足的校准，因此受到了较低的自我验证和低型自我的自我预期。结果，关键的流行病学参数（例如基本繁殖数量）的估计很差，导致预测不准确，并且对驱动感染传播的基本机制的理解有限。为了克服这些挑战，该项目将开发机械性数学框架，以增强传统的Sir型（易感性反复恢复）模型，通常与普通微分方程系统有关。这些增强的模型将结合两个其他数据源：在粪便样品中发现的废水和病毒RNA拷贝中发现的病毒RNA副本。废水病毒RNA副本的掺入将在SIR型模型中引入一个新的变量，从而管理废水中病毒浓度的动力学随着时间的流逝。随着时间的推移，将使用临床粪便样品得出的参数对现象学对代表普通感染者脱落的病毒量的病毒曲线进行建模。为了进一步提高脱落曲线的准确性并获得对其潜在机制的见解，将开发出宿主内病毒模型，并在胃肠道内纳入未感染的细胞，感染细胞和免疫反应。整体建模框架将扩展到通过将传染性类别分为不同的隔室来考虑病毒变体，每个隔间都有特定于变异的参数，例如透射性，疫苗电阻和再感染率。将使用适当的数据集分析，数值模拟和参数化所得的数学模型。将开发用户友好的计算软件包，以促进这些模型的实施及其与公共卫生数据库的界面。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。