RAPID: COVID-19 Transmission Network Reconstruction from Time-Series Data

自从在湖北省中国身上发现以来，新冠肺炎迅速传播开来。为了估计新冠肺炎的潜在影响，研究人员使用模型来预测感染人数和病毒导致的潜在发病率。重要的是，模型的结果指导着控制新冠肺炎病毒传播的政策，人们也认识到，根据所使用的模型，得出的结论有很大的不同。在这里，为了有效地缓解和预测病毒的传播，重要的是建设新冠肺炎的传播网络，该网络告知病毒在将感染传入或重新引入不同地区和人群时所采取的路线。对传输网络的准确估计将有助于开发更高保真度和准确性的模型，并有助于制定有效的缓解策略。该项目将为新冠肺炎传输网络重建开发一种数据驱动的方法，以补充和辅助基于模型的方法。这里，将使用仅根据数据历史估计的一个地区的感染与其他地区的其他感染的相对相互依存性和独立性。这种数据驱动的方法有可能演变出重要的互补性见解并指导新冠肺炎缓解策略。有许多参数模型被用于分析/预测基于新冠肺炎的病毒感染的演变。在这个项目中，重点是揭开从数据中推断的感染传播网络的演变。一种主要的方法是基于滤波和多元最优估计。该方法的第一步是确定一种制剂影响另一种制剂的途径，在该途径中识别所有促进感染从一种制剂传播到另一种制剂的中间制剂。在第二步中，重点是估计传播的动力学，借此解开一些方面，如从病原体遇到受感染病原体的时间到感染表达的延迟。从图形模型及其关系到网络过滤的各种工具将被用来解决这个问题。数据驱动的算法不依赖于模型，从目前采用的基于模型的方法中带来了一套关于新冠肺炎传播的补充见解。基于过滤/优化的方法将用于标准流行病学模型产生的数据，例如易感-感染-移除模型。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。

Covid-19 has spread rapidly since it was detected in Hubei province in China. To estimate potential impact of Covid-19, researchers have employed models to predict numbers of people infected, and the potential morbidity caused by the virus. Importantly, results from models guide policies for controlling the spread of the COVID-19 virus, where it is also recognized that conclusions reached vary considerably based on the model being employed. Here, for effective mitigation and prediction of the spread of the virus it is important to construct the transmission network of COVID-19 which informs the routes the virus takes in introducing or re-introducing infections to different regions and populations. An accurate estimate of the transmission network will help in developing models with higher fidelity and accuracy and can help in effective mitigation strategies. The project will develop a data-driven approach for reconstruction of transmission network of COVID-19, to complement and aid model-based approaches. Here, relative interdependence and independence of infection in a region from other infections in other regions estimated solely from data history will be employed. Such a data-driven approach has the potential to evolve significant complementary insights and guide strategies for COVID-19 mitigation.There are numerous parametric models being employed to analyze/predict evolution of the Covid-19 based viral infection. In this project, the focus is to unravel the evolution of the transmission network of infections as inferred from data. A primary approach is based on filtering and multivariate optimal estimation. The first step of the methodology is to identify the pathway by which an agent can affect another agent where all intermediate agents that facilitate transmission of infection from one agent to another are identified. In the second step the focus is on estimating the dynamics of the transmission whereby aspects such as the delay in expression of infection from the time the agent encounters an infected agent are unraveled. Tools from graphical models and their relationship to filtering over networks will be brought to bear on the problem. The data-driven algorithms are agnostic to models bringing complimentary set of insights into the Covid-19 transmission from the model-based approaches currently being employed. The filtering/optimization-based methods will be used on data generated by standard epidemiological models such as the Susceptible-Infected-Removed models.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.