LEAPS-MPS: Incorporating Stratification by Vaccination Status and Virus Variants in Mathematical Models of Infectious Disease Spread

LEAPS-MPS：将按疫苗接种状态和病毒变种进行的分层纳入传染病传播的数学模型

• 批准号: 2213390
• 负责人: Matthew Johnston
• 金额: $ 24.22万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213390&HistoricalAwards=false
• 关键词: LEAPS; MPS; Incorporating; Stratification; Vaccination

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Since the beginning of the COVID-19 pandemic, mathematical modeling has played a significant role in assessing and forecasting the impacts of the disease and guiding public health policy. Existing mathematical frameworks, however, have been slow to adapt to sudden changes in disease spread dynamics resulting from the waning vaccine immunity and emergence of COVID-19 variants such as delta and omicron. This project will address these challenges by developing data-driven mathematical modeling tools which divide populations according to factors that have distinct characteristics, such as those due to differences in vaccination status and the spread of virus variants. As COVID-19 evolves and becomes endemic in the global population, the developed frameworks will guide public health officials in evaluating the effectiveness of potential vaccination strategies and assessing the capacity of variants to alter the course of disease spread. This will facilitate targeted and impactful policies rather than disruptive population-wide restrictions and lockdowns. The project will engage undergraduate students in topical applied mathematics research and support underrepresented students in STEM with a particular focus on the African American community in Metro Detroit. The project will additionally advance curricular and program development at Lawrence Technological University, which will enhance the institution's research environment and further the principal investigator’s professional goal of establishing a sustained, student-focused, and interdisciplinary research program in mathematical biology at Lawrence Technological University.Traditional mathematical modeling frameworks of infectious disease spread often ignore factors of heterogeneous spread within a population. This can lead to poor estimates of epidemiological parameters (such as the basic reproduction number and herd immunity threshold), mistaken assessments of the mechanisms of disease spread, and inaccurate forecasts. This project will develop the theory and application of compartmental SIR-type (Susceptible-Infectious-Recovered) models, which are associated with a system of ordinary differential equations, to incorporate variances in a population's vaccination coverage level, differing waning immunity periods, and competition between virus variants with distinct epidemiological characteristics. Waning immunity will be incorporated through a gamma-distributed delay on return to susceptibility after vaccination or previous infection. The resulting distributed delay differential equations will be analyzed and numerically simulated using the linear chain trick, which reduces gamma-distributed delays to a linear chain of exponential delays. Case data from The Michigan Department of Health and Human Services will be used to parametrize and validate the models with the goal of providing insightful forecasts for the spread of COVID-19 under different immunization schedules. Virus variants will be incorporated by dividing the infectious class into distinct compartments with variant-specific parameters, such as variances in transmissibility, severity, vaccine resistance, reinfection rate, and diagnostic detection. The goal will be to establish novel critical thresholds for when a virus variant can persist or become dominant in a population as well as address the inverse question of estimating a variant's epidemiological parameters from its early-stage growth. By controlling a population's vaccination coverage level, the developed models will be able to cut through the complexity of case incidence data to provide critical insights into the primary factors driving disease spread. User-friendly computational packages capable of implementing the models and interfacing with public health databases will be developed.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.

该奖项是根据2021年《美国救援计划法》的全部或部分资助（公共法第117-2）。由于19921年的开始，数学建模在评估和预测疾病的影响和指导公共卫生政策方面发挥了重要作用。然而，现有的数学框架的适应性很慢，无法适应疾病的突然变化，这是由于疫苗免疫学的减弱和COVID-19变体的出现（例如三角洲和Omicron）的出现。该项目将通过开发数据驱动的数学建模工具来应对这些挑战，该工具根据具有不同特征的因素（例如疫苗接种状态差异和病毒变体的传播）对种群进行划分。随着Covid-19-19的发展并成为全球人口中的内在人群，开发的框架将指导公共卫生官员评估潜在疫苗策略的有效性，并评估变体的能力改变疾病蔓延的过程。这将有助于有针对性的和有影响力的政策，而不是破坏人口范围的限制和封锁。该项目将吸引本科生参与局部应用数学研究，并支持STEM中代表性不足的学生，特别关注底特律大都会的非裔美国人社区。该项目将进一步推进劳伦斯技术大学的课程和课程开发，该大学将增强机构的研究环境，并进一步发展劳伦斯技术大学数学生物学数学生物学的持续，以学生为中心和跨学科研究计划。这可能导致对流行病学参数的估计不佳（例如基本的繁殖数量和牛群免疫史阈值），对疾病扩散机制的错误评估以及不准确的森林。该项目将开发与普通微分方程系统相关的室内sir型（易感性抗疫苗的）模型的理论和应用，以在人群的疫苗覆盖水平中纳入方差，从而区分免疫组织时期的疫苗覆盖率，以及具有不同不同流行病学特征的病毒变体之间的竞争。疫苗接种或先前感染后的易感性恢复易感性时，将通过伽马分布的延迟来纳入免疫学。将使用线性链技巧分析所得的分布式延迟微分方程，并在数值上模拟，该方程将伽马分布的延迟减少到指数延迟的线性链中。密歇根州卫生和公共服务部的案例数据将用于参数化和验证模型，目的是为在不同的免疫计划下提供有见地的森林，以使Covid-19的传播。病毒变体将通过将传染性类别分为具有特异性参数的不同隔室，例如传播，严重程度，疫苗抵抗，再感染率和诊断检测方面的差异。目的是建立新的关键阈值，以便何时病毒变体可以在人群中持续或占主导地位，并解决估计变体从其早期生长中估算流行病学参数的反应问题。通过控制人群的疫苗覆盖水平，开发的模型将能够削减病例事件数据的复杂性，从而对驱动疾病扩散的主要因素进行关键见解。将开发能够实施模型并与公共卫生数据库进行接口的用户友好的计算套件。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，通过评估来诚实地获得支持。

项目成果

A two-strain model of infectious disease spread with asymmetric temporary immunity periods and partial cross-immunity

• DOI: 10.3934/mbe.2023718
• 发表时间: 2023-01-01
• 期刊: MATHEMATICAL BIOSCIENCES AND ENGINEERING
• 影响因子: 2.6
• 作者: Johnston，Matthew D.;Pell，Bruce;Rubel，David. A.
• 通讯作者: Rubel，David. A.