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

新冠病毒自在中国湖北省被发现以来，传播迅速。为了估计Covid-19的潜在影响，研究人员使用模型来预测感染人数以及由该病毒引起的潜在发病率。重要的是，模型的结果指导了控制COVID-19病毒传播的政策，人们还认识到，所采用的模型不同，得出的结论差异很大。因此，为有效缓解和预测病毒的传播，构建COVID-19传播网络至关重要，该网络告知病毒在不同地区和人群中引入或再引入感染的途径。对输电网络的准确估计将有助于开发具有更高保真度和准确性的模型，并有助于制定有效的缓解战略。该项目将开发一种数据驱动的方法来重建COVID-19传播网络，以补充和辅助基于模型的方法。在这里，一个地区的感染与仅根据历史数据估计的其他地区的感染的相对相互依赖和独立性将被采用。这种数据驱动的方法有可能形成重要的互补见解，并指导缓解COVID-19的战略。有许多参数模型被用来分析/预测基于Covid-19的病毒感染的演变。在这个项目中，重点是从数据中推断出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媒介影响另一种媒介的途径，其中确定了促进感染从一种媒介传播到另一种媒介的所有中间媒介。在第二步中，重点是估计传播的动力学，从而揭示诸如从病原体遇到受感染病原体时开始的感染表达延迟等方面。从图形模型及其与网络过滤的关系的工具将被用于解决这个问题。数据驱动的算法与模型无关，这些模型从目前采用的基于模型的方法中获得了对Covid-19传播的一套补充见解。基于过滤/优化的方法将用于标准流行病学模型（如易感-感染-去除模型）生成的数据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.