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

正在进行的新冠肺炎大流行是由一种新型冠状病毒引起的，这种病毒直到2019年12月才被发现。由于该病毒的新颖性和目前席卷全球的速度，不仅人们对该病毒知之甚少，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。社交距离在“拉平曲线”方面有效吗？强制就地避难所在控制传播方面会有多大的效果？这值得付出社会成本吗？10%的人口忽略这些协议会有什么影响？与实施成本相比，实施隔离的边际收益是多少？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事情：由于新冠肺炎而即将到来的住院高峰。然而，也有必要用一系列新的问题来展望这种病毒的潜在死灰复燃。在人们仍在从新冠肺炎中恢复的情况下，中国不同地区的异步式“开放”将会产生什么影响？我们怎么知道我们不是在过早地取消限制？为了做出明智的政策决策，需要对这些问题做出准确的答案，这需要对新冠肺炎有深刻的理解和准确的模型，而这是今天根本无法获得的。不幸的是，在需要采取行动减轻已经造成的社会、经济甚至生命损失的巨大破坏之前，没有时间了解这种病毒。相反，必须将新数据迅速纳入模型中，并且必须不断重新访问这些问题，以便能够快速至少合理地理解上述重要问题。此项目解决了新冠肺炎快速演变的建模问题。从系统的角度来看，这个项目试图调查新冠肺炎在当今用于为政策决策提供信息的领先模型中各种被忽视的人工制品的影响。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。这个项目的结果将是新的随机和确定性的网络元人口模型，而不是通常看到的集中人口模型。开发的模型将扩展简单的易感感染移除(SIR)模型，通过添加更多的隔间来捕捉新冠肺炎特有的一些不同属性。这些模型将对正在进行的大流行的网络影响提供更严格的分析，这可能被证明特别有用，因为国家的不同地区，甚至世界，正在不同的时间施加/取消不同水平的流动限制。这一奖项反映了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 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.