COVID-19: RAPID: Networked Compartmental Modeling and Analysis for Spread of COVID-19

正在进行的COVID-19大流行是由2019年12月才发现的一种新型冠状病毒引起的。由于该病毒的新颖性及其目前席卷全球的速度，不仅对该病毒知之甚少，而且数据也很少。尽管缺乏数据，但仍有许多重要问题需要回答。保持社交距离是否有效地“拉平了曲线”？在遏制疫情蔓延方面，强制性的就地庇护会更有效多少？这样的社会成本值得吗？10%的人无视这些协议会有什么影响？与实施成本相比，实施隔离的边际效益是什么？今天，重要的问题似乎与缓解有关，因为最大的担忧是眼前的问题：由于COVID-19即将到来的住院高峰。然而，也有必要展望这种病毒可能卷土重来的可能性，并提出一系列新问题。在人们仍在从COVID-19中恢复的情况下，异步“开放”该国不同地区将产生什么影响？我们怎么知道我们没有在成熟之前解除限制？为了做出明智的政策决定，需要对这些问题给出准确的答案，这需要对COVID-19有深刻的理解和准确的模型，而这些模型目前根本无法获得。不幸的是，在需要采取行动减轻已经在社会、经济甚至生命损失方面造成的巨大损害之前，没有时间了解这种病毒。相反，必须将新数据快速合并到模型中，并且必须在不断的基础上重新访问这些问题，以便能够快速提供至少对上述重要问题的合理理解。该项目解决了COVID-19快速发展的建模问题。从系统的角度来看，该项目旨在调查COVID-19的各种被忽视的人为因素对当今用于决策的主要模型的影响。数值方法和数学模型可以为世界各地的流行病学家了解病毒如何传播提供重要的补充支持。该项目的结果将是新颖的随机和确定性网络元人口模型，而不是常见的集中人口模型。开发的模型将扩展简单的易感-感染-移除（SIR）模型，通过添加更多的区室来捕获COVID-19特有的许多不同属性。这些模型将对当前大流行的网络效应提供更严格的分析，这可能特别有用，因为该国不同地区甚至世界各地在不同时间实施/取消了不同程度的流动限制。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。

The ongoing COVID-19 pandemic is caused by a novel coronavirus, which was only identified in December 2019. Due to the novelty of the virus and the speed at which it is currently sweeping the world, not only is there very little known about the virus but there is also very little data. Despite the lack of data, there are many important questions that need to be answered. Is social distancing working effectively in `flattening the curve’? How much more effective would mandated shelter-in-place be in containing the spread? Is it worth the social cost? What is the effect of 10% of the population ignoring these protocols? What is the marginal benefit of enforcing quarantines versus implementation cost? Today the important questions seem to be related to mitigation as the biggest concern is the immediate matter at hand: the impending peak of hospitalizations due to COVID-19. However, it is also necessary to be looking ahead to a potential resurgence of this virus with a new set of questions. What will be the effect of asynchronously `opening up' different parts of the country as people are still recovering from COVID-19? How will we know we are not lifting restrictions pre-maturely? Precise answers to these questions are needed in order to make informed policy decisions, and this requires a deep understanding and accurate models of COVID-19 which are simply not available today. Unfortunately, there is no time to learn about this virus before needing to act to mitigate the tremendous damage that is already being incurred socially, economically, and even in terms of lost lives. Instead, new data must be rapidly incorporated into models and these questions must be re-visited on a constant basis to be able to quickly provide at least a reasonable understanding of the important questions above.This project addresses the rapidly evolving modeling problem for COVID-19. Taking a systems point of view, this project seeks to investigate the effects of various overlooked artifacts of COVID-19 in the leading models used to inform policy decisions today. The numerical methods and mathematical models can provide significant complementary support to the epidemiologists worldwide on understanding how the virus spreads. The outcomes of this project will be novel stochastic and deterministic networked meta-population models as opposed to the commonly seen lumped population models. The models developed will expand simple Susceptible-Infected-Removed (SIR) models to capture a number of different properties specific to COVID-19 by adding more compartments. These models will provide a more rigorous analysis of the network effects of the ongoing pandemic which may prove especially useful as different parts of the country, or even the world, are imposing/lifting various levels of mobility restrictions at different times.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.