Department of Mathematics, Purdue University, West Lafayette, Indiana, United States.
Division of Applied Mathematics and School of Engineering, Brown University, Providence, Rhode Island, United States.
PLoS Comput Biol. 2021 Sep 8;17(9):e1009334. doi: 10.1371/journal.pcbi.1009334. eCollection 2021 Sep.
Epidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and projection with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible-exposed-infectious-recovered (SEIR) model, including new compartments and model vaccination in order to project the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately project the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC's government's website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.
流行病学模型可以提供疫情的动态演变，但它们基于许多假设和参数，这些假设和参数必须随着疫情持续时间进行调整。然而，通常情况下，可用数据不足以确定模型参数，因此无法推断出未观察到的动态。在这里，我们开发了一个用于构建可靠数据驱动的流行病学模型的通用框架，该框架由一个工作流程组成，该流程集成了数据采集和事件时间表、模型开发、可识别性分析、敏感性分析、模型校准、模型稳健性分析以及在不同场景下的不确定性预测。特别是，我们应用该框架提出了一个改进的易感-暴露-感染-恢复（SEIR）模型，包括新的隔室和疫苗接种模型，以预测纽约市（NYC）的 COVID-19 传播动态。我们发现，我们可以唯一地估计模型参数，并准确地预测每日新增感染病例、住院和死亡人数，与 NYC 政府网站上的可用数据一致。此外，我们还使用校准后的数据驱动模型研究了纽约市接种疫苗和重新开放室内用餐的时间对疫情的影响。
