RAPID: Epidemiological and Phylogenetic Models for Contact-Based Control of COVID-19

当前的新冠肺炎大流行表明，有效和反应迅速的公共卫生系统对于遏制新爆发的疫情至关重要。加强病例检测、接触者追踪以及广泛的检疫和社会距离措施，对于遏制新冠肺炎疫情至关重要。数学建模和分析可以为这些“基于接触”的非药物干预措施的有效性提供重要的见解。然而，模型参数化需要详细的病例数据，这经常受到不一致、未报告和无症状病例的挑战。为了克服这些困难，研究人员将：开发具有隔离、追溯和社会距离的新冠肺炎爆发模型，并在隔离措施下得出关于最终疫情规模和繁殖数量的新结果，以平坦和缩小疫情曲线。研究人员还将整合病例和系统发育数据，以建立一个创新的框架，揭示特定地区的控制，并预测如何最好地实施以接触为基础的措施，以有效控制疫情。结果将与政策制定者以及科学和公众受众进行沟通，以便对冠状病毒大流行产生实时影响。该项目将考虑具有响应性隔离、跟踪和社会距离措施的新冠肺炎的分区爆发模式。新的配方允许根据接触者追踪和广泛的检疫干预参数来推算暴发规模。病例、追踪和检疫数据的复制数量估计和模型拟合将量化特定区域的控制特征。将评估疫情规模对接触者追踪和社会距离措施组合的敏感性，以了解平坦和缩小疫情曲线的可行战略。通过对模型的分析和模拟，将调查长期的放松和收紧检疫干预以实现可持续控制的情景。此外，通过跨学科合作，研究人员将把流行病学模型的多区域随机版本与基因组数据的系统动力学计算结合起来，以在存在可能的未知传播链和特定地区的响应干预措施的情况下改进模型预测。一种“前向模拟-后向联合”的方法将跟踪迁徙事件，并产生系统发育树签名，以便将模型模拟与系统发育数据相匹配。整体结合的系统动力学和流行病模型分析将量化不同的疾病传播和基于接触的控制效果，以告知公共卫生当局。该奖项由CARE法案补充资金提供资金分配给MP。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 current COVID-19 pandemic shows that effective and responsive public health systems are crucial for curbing emergent outbreaks. Increased case detection, contact tracing, and broad quarantine and social distancing measures, have been vital for containing COVID-19 outbreaks. Mathematical modeling and analysis can provide important insights into the efficacy of these “contact-based” non-pharmaceutical interventions. However, model parameterization requires detailed case data, which is often challenged by inconsistent, unreported and asymptomatic cases. To overcome these difficulties, the researchers will: develop outbreak models of COVID-19 with quarantine, tracing and social distancing and derive new results on final epidemic size and reproduction number under quarantine measures to flatten and shrink the epidemic curve. The researchers will also integrate case and phylogenetic data for an innovative framework that reveals region-specific control and predicts how to best implement contact-based measures for efficiently containing the outbreaks. Results will be communicated with policymakers, along with scientific and public audiences, in order to have a real-time impact on the coronavirus pandemic.This project will consider compartmental outbreak models of COVID-19 with responsive quarantine, tracing and social distancing measures. The novel formulation allows for derivation of outbreak size, dependent on contact tracing and broad quarantine intervention parameters. Reproduction number estimates and model fitting of case, tracing and quarantine data will quantify region-specific control characteristics. Sensitivity of epidemic size to combinations of contact tracing and social distancing measures will be assessed to understand viable strategies for flattening and shrinking epidemic curves. Long term scenarios of loosening and tightening of quarantine interventions for sustainable control will be investigated through analysis and simulation of the models. In addition, through interdisciplinary collaboration, the researchers will incorporate multi-region stochastic versions of the epidemiological models with phylodynamic computations of genomic data to improve model projections in the presence of possible unknown chains of transmission and area-specific responsive interventions. A “forward simulation-backward coalescent” approach will track migration events and produce phylogenetic tree signatures for matching model simulations to phylogenetic data. The overall combined phylodynamic and epidemic model analysis will quantify heterogeneous disease spread and contact-based control efficacy to inform public health authorities.This award is being funded by the CARES Act supplemental funds allocated to MPS.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.