RAPID: Epidemic control strategies for COVID-19 in age-structured populations: A multi-model approach

最近的新冠肺炎大流行导致人们依赖流行病学模型来预测与各种缓解战略相关的潜在好处，以控制这种新疾病的传播。关注新冠肺炎的数学模型的复杂性各不相同，既有相对简单的模型，也有跟踪个体及其在疫情过程中运动情况的更复杂的模型。模型性能取决于模型结构和可用的基础数据。在疫情开始时，当可获得的数据有限时，简单的模型应该最有效。随着疫情的发展，更复杂的模型可能会表现得更好。然而，大多数研究都集中在单一模型上，而没有考虑跨多个模型进行比较。这项研究将通过使用多模型方法来提出关于流行病强度(例如，预计的新冠肺炎病例数量)以及缓解战略对这些动态的影响的问题，从而解决流行病过程中的直接社会关切。虽然这项研究将开发专注于新冠肺炎的模型，但这些模型以及将模型与数据相匹配的统计方法将适用于未来新疾病的爆发。此外，该项目还为研究生和博士后研究员提供了培训机会。总体而言，这项研究将使用多模型方法，该方法考虑了一系列模型，从相对简单的区隔模型到包含基于社会联系的年龄和/或网络结构的更复杂的模型。所考虑的模型将提供对重要的流行病学参数(例如，R0)及其相关不确定性的估计。了解这种不确定性，并确定流行病模型的复杂性是否以及何时是估计流行病参数和流行病轨迹的障碍或工具是一个关键问题。本研究将结合关于新冠肺炎大流行的多个流行病模型的数据来：1.了解传统分区方法与基于网络的年龄结构模型在持续流行过程中的相对性能；2.量化最关键的变异来源，以估计阈值疫苗接种标准、流行病轨迹和潜在的缓解策略；3.随着疫情的发展和数据可用性的变化，确定更复杂的基于网络的模型是否以及何时优于更传统的方法。为了实现上述目标，所进行的分析将使用贝叶斯方法将模型与数据进行拟合。贝叶斯方法将允许量化模型和参数的不确定性，以及评估预测的流行病动态和缓解策略的不确定性。该项目由传染病生态学和进化计划(EEID)、环境生物学部门、生物科学理事会和既定的刺激竞争研究计划(EPSCoR)联合资助。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。

The recent COVID-19 pandemic has led to the reliance on epidemiological models to forecast the potential benefits associated with various mitigation strategies to control the spread of this novel disease. The mathematical models focused on COVID-19 vary in their complexity from the relatively simple to more elaborate models that follow individuals and their movement over the course of the epidemic. Model performance depends both on model structure and the underlying data available. During the beginning of an epidemic when limited data are available, simple models should work best. As the epidemic progresses, more complex models may then perform better. Yet, most research focuses on a single model and does not consider comparing across multiple models. This research will address immediate societal concerns over the course of the epidemic by using a multi-model approach to ask questions regarding epidemic intensity (for example, the number of expected COVID-19 cases) and the impacts of mitigation strategies on these dynamics. While the research will develop models focused on COVID-19, the models along with the statistical approaches to fit models to data will be applicable for future outbreaks of novel diseases. Additionally, this project provides training opportunities for a graduate student and a post-doctoral researcher. In general, this research will use a multi-model approach that considers a range of models from relatively simple compartmental models to more complex models that incorporate age- and/or network-structure based on social contacts. The models considered will provide estimates of important epidemiological parameters (for example, R0) along with their associated uncertainty. Understanding this uncertainty and establishing if and when epidemic model complexity is a hindrance or utility for estimating epidemic parameters and epidemic trajectories is a key issue.This research will combine data with multiple epidemic models on the COVID-19 pandemic to:1. understand the relative performance of traditional compartmental approaches to network-based age-structured models during the course of an on-going epidemic; 2. quantify the most crucial sources of variation for estimating threshold vaccination criteria, epidemic trajectories, and potential mitigation strategies; and, 3. identify if and when more complex network-based models outperform more traditional approaches as the epidemic progress over time and data availability changes.To meet the above goals, the analyses conducted will use a Bayesian approach of fitting models to data. A Bayesian approach will allow for the quantification of model and parameter uncertainty as well as assess the uncertainty of forecasted epidemic dynamics and mitigation strategies.This project is jointly funded by the Ecology and Evolution of Infectious Diseases Program (EEID), Division of Environmental Biology, Directorate of Biological Sciences and the Established Program to Stimulate Competitive Research (EPSCoR).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.