RAPID: ReCOVER: Accurate Predictions and Resource Allocation for COVID-19 Epidemic Response

最近新冠肺炎的爆发及其全球影响要求采取紧急措施遏制疫情。预测像新冠肺炎这样的传染病的速度和严重程度，并适当分配医疗资源，是应对疫情的核心。像新冠肺炎这样的流行病不仅影响全世界的健康，而且还会产生深远的经济和社会影响。控制疫情、提供知情的预测和预防未来的流行病，对于全球人民恢复日常工作和旅行而不感到恐惧至关重要。资源短缺使医疗系统承受了过大的压力，进一步危及社区的健康。备灾和更好地管理现有资源需要在世界各地的城市和县一级作出具体预测，而不仅仅是在国家一级。该项目将通过开发基于机器学习的计算模型来研究病毒的传播并评估各种干预措施对疾病传播的影响，从而提供对病毒传播的预测性了解。该项目将学习新冠肺炎的感染预测模型，考虑到以下几点。(I)在州/县/市一级而不是国家一级进行预测，因为更精细的粒度对规划和管理资源至关重要。(Ii)一个人的传染性程度随着时间的推移而变化。通过观察数据学习模型将有助于理解病毒的时间性质。(3)在这样的粒度下，旅行是传播的一个重要原因，需要加以考虑。(4)需要对现有数据进行“更正”，找出未观察到但对疫情动态有影响的潜在未报告病例的数量。该项目还将解决基于预测的资源分配问题-例如，如果某个州下周将有一定数量的口罩可供使用，它们应该如何在该州的不同医院(哪些医院和每个州有多少)之间分配？拟议的项目Recover将使用一种新颖的细粒度、异质感染率模型来执行不同粒度(医院/机场、城市、州、国家)的预测，同时考虑到人类的流动性。Recover将整合来自不同来源的数据，以建立高度准确的模型，以在不同的粒度预测世界各地的疫情。由于能够捕获感染率的时间异质性，该方法有可能提供对新冠肺炎传染性本质的洞察，这些本质尚不完全被理解。该项目将通过对历史感染的时间分析来解决未报告病例的问题，并更正数据。正确的建模粒度将被自动识别，例如，何时对一个州的城市进行建模，以权衡精度以获得更高的预测可靠性。拟议的项目还制定和解决了资源分配问题，可以指导应对措施遏制疫情，防止未来爆发。这是通过网络上的资源分配的最佳解决方案来提供的，其中每个节点(代表一个区域)具有捕获概率响应的功能。虽然该项目考虑到与新冠肺炎一起获得数据，但该项目下开发的模型和算法适用于广泛类别的传染病。该项目最终将成为一种可定制的交互式工具，可供合格用户(如负责管理疫情应对工作的政府实体)进行预测和资源管理。数据和代码还将与研究社区共享。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 recent outbreak of COVID-19 and its world-wide impact calls for urgent measures to contain the epidemic. Predicting the speed and severity of infectious diseases like COVID-19 and allocating medical resources appropriately is central to dealing with epidemics. Epidemics like COVID-19 not only affect world-wide health, but also have profound economic and social impact. Containing the epidemic, providing informed predictions and preventing future epidemics is essential for the global population to resume their day-to-day work and travel without fear. Shortage of resources puts undue stress on healthcare system further risking health of the community. Preparedness and better management of available resources would require specific predictions at the level of cities and counties around the world rather than solely at the level of countries. The project will provide a predictive understanding of the spread of the virus by developing machine learning based computational models to study the transmission of the virus and evaluate the impact of various interventions on disease spread. The project will learn infection prediction models for COVID-19 considering the following. (i) Predicting at state/county/city-level rather than country-level as finer granularity is essential in planning and managing resources. (ii) How infectious a person is changes over time. Learning the model through observed data will help in understanding of the temporal nature of the virality. (iii) At such granularity travel is a significant reason for the spread and needs to be accounted for. (iv) Available data needs to be “corrected” by finding the number of underlying unreported cases that are not observed and yet influence the epidemic dynamics. The project will also solve the resource allocation problem based on the prediction – for instance if a certain number of masks will be available next week in a certain state, how should they be distributed across different hospitals in the state (which hospitals and how many in each state)?Proposed project ReCOVER will use a novel fine-grained, heterogeneous infection rate model to perform predictions at various granularities (hospital/airports, city, state, country) while accounting for human mobility. ReCOVER will integrate data from various sources to build highly accurate models for prediction of the epidemic across the world at various granularity. Due to the ability to capture temporal heterogeneity in infection rate, the approach has the potential to provide insights into infectious nature of COVID-19 which are not fully understood yet. The project will address the issue of unreported cases through temporal analysis of historical infections and correct the data. The right granularities of modeling will be automatically identified, e.g., when to model a state over its cities to trade-off precision for higher reliability in predictions. The proposed project also formulates and solves a resource allocation problem that can guide the response to contain the epidemic and prevent future outbreaks. This is provided by optimal solutions to resource allocation over a network where each node (representing a region) has a function that captures probabilistic response. While the project obtains data with COVID-19 in consideration, the model and algorithms developed under the project are applicable to a wide class of contagious diseases. The project will culminate into an interactive customizable tool that can be used to perform predictions and resource management by a qualified user such as a government entity tasked with managing the epidemic response. The data and code will also be shared with research community.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.