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，现有的模型往往没有考虑到漏报的临床病例，导致流行病学参数的估计不准确和有缺陷的预测。与此同时，废水监测已成为一种很有前途的工具，可用于从不同人群中获取数据，包括无症状个体和未通过临床检测获取的个体。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来加强动态人群水平流行病学模型的校准，从而弥补这一差距。通过纳入废水数据，该项目旨在改善对真实疾病流行率的估计，加强对未来病例的预测，并监测病毒变体的出现和演变。所开发的数学框架对于持续监测COVID-19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的计划。这项研究积极吸引本科生，特别是来自代表性不足的背景，促进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，建立了一个可持续的跨学科数学生物学研究项目。该项目旨在解决现有用于模拟传染病传播的数学框架的局限性，由于无症状个体导致的病例报告不足和自我报告率低，这些数学框架经常受到校准不足的影响。因此，对基本繁殖数等关键流行病学参数的估计很差，导致预测不准确，对驱动感染传播的潜在机制的了解有限。为了克服这些挑战，该项目将开发机械的数学框架，以增强传统的SIR型（易感-传染-传染）模型，通常与常微分方程系统相关联。这些增强的模型将纳入两个额外的数据来源：废水中发现的病毒RNA拷贝和粪便样本中发现的病毒RNA拷贝。废水病毒RNA拷贝的掺入将引入一个新的变量到SIR型模型中，控制废水中病毒浓度随时间的动态变化。病毒脱落曲线代表平均感染者随时间脱落的病毒量，将使用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，将未感染的细胞、感染的细胞和胃肠道内的免疫反应结合起来。将扩展整体建模框架，通过将传染性类别划分为不同的分区来考虑病毒变体，每个分区具有变体特异性参数，例如传播率，疫苗耐药性和再感染率。将使用适当的数据集分析、数值模拟和参数化由此产生的数学模型。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接口。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。