RAPID: Understanding COVID-19 Transmission With Non-Markovian Models

有了新冠肺炎，世界经历了当代史上最重大的流行病。为了减少病毒传播，已经提出并实施了不同的缓解策略。在这种情况下，疾病传播模型已成为预测当前和未来新冠肺炎传播特征的关键工具，无论是否实施缓解战略，并指导决策。然而，如果模型建立在可靠的数据和证据支持的假设之上，那么它们就是准确的预测者。模型预测的大幅波动可以归因于没有数据或证据支持的假设，从而影响模型的可靠性。一个典型的假设是个人在不同疾病状态之间的过渡时间的指数分布(即，将个人标记为易感、暴露、感染和康复的间隔)。然而，最近对新冠肺炎数据的观察，突显了一些关键过渡时期的非指数分布，例如传染性时期。这直接影响到模型的准确性。考虑到这一点，本项目的目标是：1)开发基于网络的分区元人口模型，该模型接受个体在不同分区之间的过渡时间的任意分布；2)开发严格的方法来使用随机优化方法估计模型的未知参数；3)确定针对关注较少的地区(如农村地区)量身定做的联系网络。该项目的顺利完成将为美国公共卫生，特别是新冠肺炎的分析和监测带来好处。对缓解战略进行更准确的基于模型的测试将有助于公共卫生官员选择战略，并围绕缓解政策收集信任和支持。这样，卫生政策制定者、模型制定者和普通公众将在最终阻止新冠肺炎的问题上拥有共同的目标。在这个项目中，团队将开发非马尔科夫模型，这些模型由新冠肺炎最近的数据分析结果所建议的经验确定的过渡时间分布驱动，突出一些关键过渡时间的非指数分布。我们建议的模型的这一新方面对当前和未来的暴发特征产生了更准确的估计。例如，我们可以比假设指数分布的模型更准确地估计未检测到的感染人数。此外，由于任何模型都有已知和未知的参数，未知参数的估计对模型的精度至关重要。为了估计美国新冠肺炎大流行的未知参数，我们计划使用元人口模型和随机优化技术生成的流行病曲线。模型精确度的提高可以更好地估计疫情特征，进而更好地预测缓解政策的有效性。最后，任何基于网络的模型都需要输入代表联系人或移动的网络。虽然一些受疫情影响的城市可以使用这些联系网络，但农村地区得到的分析较少，尽管农村地区偶尔是热点地区。该团队将致力于为某些感兴趣的农村地区确定数据驱动的联系网络，我们将为此应用并测试我们的建模方法。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。

With COVID-19, the world has experienced the most significant pandemic of contemporary history. In an effort to reduce the virus transmission, different mitigation strategies have been proposed and implemented. In this situation, models of disease transmission have emerged as a key tool to predict current and future characteristics of COVID-19 spreading, with or without the implementation of mitigation strategies, and to guide policymaking decisions. Models however are accurate predictors if they are built upon reliable data and evidence-supported assumptions. Large swings in model predictions can be imputed to assumptions not supported by data or evidence, with consequences on the model reliability. One typical assumption is the exponential distribution of the transition times of individuals between different states of disease (i.e., compartments that mark individuals as susceptible, exposed, infected, and recovered). However, recent observations of COVID-19 data, highlight non-exponential distributions for some critical transition times, such as the infectious period. This directly impacts the accuracy of the models. With this in mind, the goals of this project are to: 1) develop network-based compartmental meta-population models that accept arbitrary distributions for the transition times of the individual between different compartments; 2) develop rigorous methodologies to estimate unknown parameters of the model using stochastic optimization methods; 3) determine contact networks tailored for regions receiving lower attention, such as rural areas. Successful completion of this project will provide benefits to the USA public health, in particular to the analysis and monitoring of COVID-19. More accurate model-based testing of mitigation strategies will help public health officials to select strategies and to gather trust and support around mitigation policies. This way, health policymakers, modelers, and the general public will share common goals toward eventually stopping COVID-19. In this project, the team will develop non-Markovian models that are driven by empirically determined distributions of transition times as suggested by recent results from analyzing data for COVID-19, highlighting the non-exponential distributions for some critical transition times. This novel aspect of our proposed model produces more accurate estimates of the current and future outbreak characteristics. For example, we can estimate the number of undetected infected people more accurately than models assuming the exponential distribution. Furthermore, as any model has known and unknown parameters, the estimation of the unknown parameters is critical to the model accuracy. To estimate the unknown parameters for this COVID-19 pandemic in the USA, we plan to use epidemic curves generated using the meta-population model and stochastic optimization techniques. The increased model accuracy produces better estimates of the outbreak characteristics and in turn better predictions of the mitigation policies effectiveness. Finally, any network-based model requires to input of a network representing contacts or movements. While these contact networks are available for some cities affected by the pandemic, rural regions have been less analyzed, despite occasionally being hot spots. The team will work to determine data-driven contact networks for certain rural areas of interest, for which we will apply and test our modeling approaches.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.