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RUI: Applications of a Generalized Differential Equation Compartmental Model of Infectious Disease Transmission

RUI：传染病传播广义微分方程房室模型的应用

基本信息

批准号：
2052592
负责人：
Scott Greenhalgh
金额：
$ 22.53万
依托单位：
Siena College
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052592&HistoricalAwards=false
关键词：
RUI Applications Generalized Differential Equation

项目摘要

This project concerns a novel class of mathematical models that quantify disease outbreaks by the total number of days people are infected with a disease. The goal of the project is to apply these models to answer open questions in public health and evolutionary biology. The models can provide a simple means to study how the differences in peoples’ recovery time from infection affects the evaluation of public health policies, disease interventions, and disease evolution. As proof of concept, the project will model historical disease outbreaks, such as measles, whooping cough, and influenza, and compare predictions to classical modeling approaches that are based on tracking the number of infected people. The results of the research are expected to contribute to both the information needed by public health officials to make informed decisions and the state of knowledge on disease evolution.This project will develop and apply a new class of differential equation compartmental models. This class of models is the first to consider an alternative assumption as to what quantifies the amount of disease in a population. By considering the quantity "person-days of infection" over "disease incidence," the new class of models enables the incorporation of higher statistical moments from latent and infectious period distributions into the rates that govern the dynamics of the model. The work aims to illustrate the straightforward, accurate, and readily interpretable properties of this new class of compartmental model and its application. The project will analyze questions such as 1) the interplay between health disparities and virulence evolution, 2) the impact of infectious period outliers on the cost-effectiveness of disease interventions, and 3) how host variation affects the evolutionary and epidemiology dynamics of infectious disease transmission. Essential in tackling these issues will be the use of mean-residual life theory and analysis techniques that include stability, cost-effectiveness, and evolutionary invasion analyses. The research will illustrate the utility of this new class of models, contribute to the information needed by public health officials to mitigate disease outbreaks, and contribute to the state of knowledge on disease evolution.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.

这个项目涉及一类新的数学模型，通过人们感染某种疾病的总天数来量化疾病爆发。该项目的目标是应用这些模型来回答公共卫生和进化生物学中的公开问题。这些模型可以提供一种简单的方法来研究人们从感染中恢复的时间的差异如何影响公共卫生政策、疾病干预和疾病演变的评估。作为概念验证，该项目将对麻疹、百日咳和流感等历史疾病暴发进行建模，并将预测与基于跟踪感染人数的经典建模方法进行比较。这项研究的结果有望为公共卫生官员做出明智决策所需的信息和疾病进化的知识状况做出贡献。该项目将开发和应用一类新的微分方程式分区模型。这类模型首次考虑了另一种假设，即用什么来量化人群中的疾病数量。通过考虑“人-天感染”的数量而不是“疾病发病率”，这类新的模型能够将潜伏期和感染期分布的更高统计量结合到控制模型动态的比率中。这项工作旨在说明这类新的隔室模型及其应用的简单、准确和易于解释的性质。该项目将分析以下问题：1)健康差异和毒力进化之间的相互作用；2)感染期离群值对疾病干预措施成本效益的影响；3)宿主变异如何影响传染病传播的进化和流行病学动态。解决这些问题的关键将是使用平均剩余寿命理论和分析技术，包括稳定性、成本效益和进化入侵分析。这项研究将说明这类新模型的实用性，有助于公共卫生官员减少疾病爆发所需的信息，并有助于了解疾病演变的状况。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。