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

RAPID：ReCOVER：COVID-19 流行病应对的准确预测和资源分配

• 批准号: 2027007
• 负责人: Viktor Prasanna
• 金额: $ 15.86万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027007&HistoricalAwards=false
• 关键词: RAPID; ReCOVER; Accurate; Predictions; Resource

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.

近期爆发的COVID-19及其全球影响要求采取紧急措施控制疫情。预测COVID-19等传染病的速度和严重程度，并适当分配医疗资源，是应对流行病的核心。COVID-19等流行病不仅影响全球健康，还对经济和社会产生深远影响。控制疫情、提供知情预测和预防未来的疫情，对于全球民众恢复日常工作和毫无恐惧地旅行至关重要。资源短缺对医疗系统造成不必要的压力，进一步危及社区的健康。要做好准备并更好地管理现有资源，就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作出预测。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，从而对病毒的传播提供预测性了解。该项目将学习COVID-19的感染预测模型，考虑以下因素。(i)在州/县/市一级而不是国家一级进行预测，因为更细的粒度在规划和管理资源方面至关重要。(ii)一个人的传染性会随着时间而改变。通过观察到的数据学习模型将有助于理解病毒性的时间性质。(iii)在这种粒度下，旅行是传播的重要原因，需要加以考虑。(iv)现有数据需要“修正”，找出未观察到但影响流行动态的潜在未报告病例的数量。该项目还将根据预测解决资源分配问题-例如，如果下周某个州将提供一定数量的口罩，那么应该如何在该州的不同医院之间分配（每个州有哪些医院和多少）？拟议项目ReCOVER将使用一种新的细粒度、异质感染率模型来执行各种粒度（医院/机场、城市、州、国家）的预测，同时考虑到人类的流动性。ReCOVER将整合来自各种来源的数据，以建立高度准确的模型，用于在各种粒度上预测世界各地的流行病。由于能够捕捉感染率的时间异质性，该方法有可能深入了解尚未完全了解的COVID-19的传染性。该项目将通过对历史感染的时间分析和纠正数据，解决未报告病例的问题。将自动识别建模的正确粒度，例如，什么时候对一个州的城市进行建模，以权衡预测的精度和可靠性。拟议的项目还制定和解决了一个资源分配问题，可以指导控制流行病和防止未来爆发的对策。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）都有一个捕获概率响应的函数。虽然该项目获取的数据考虑了COVID-19，但该项目开发的模型和算法适用于广泛的传染病类别。该项目最终将成为一个可定制的交互式工具，可由合格的用户（如负责管理流行病应对工作的政府实体）用于进行预测和资源管理。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Shape-based Evaluation of Epidemic Forecasts

基于形状的疫情预测评估

• DOI: 10.1109/bigdata55660.2022.10020895
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Big Data (Big Data
• 作者: Srivastava, Ajitesh;Singh, Satwant;Lee, Fiona
• 通讯作者: Lee, Fiona

Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination.

• DOI: 10.7554/elife.73584
• 发表时间: 2022-06-21
• 期刊: eLife
• 影响因子: 7.7
• 作者: Truelove S;Smith CP;Qin M;Mullany LC;Borchering RK;Lessler J;Shea K;Howerton E;Contamin L;Levander J;Kerr J;Hochheiser H;Kinsey M;Tallaksen K;Wilson S;Shin L;Rainwater-Lovett K;Lemairtre JC;Dent J;Kaminsky J;Lee EC;Perez-Saez J;Hill A;Karlen D;Chinazzi M;Davis JT;Mu K;Xiong X;Pastore Y Piontti A;Vespignani A;Srivastava A;Porebski P;Venkatramanan S;Adiga A;Lewis B;Klahn B;Outten J;Orr M;Harrison G;Hurt B;Chen J;Vullikanti A;Marathe M;Hoops S;Bhattacharya P;Machi D;Chen S;Paul R;Janies D;Thill JC;Galanti M;Yamana TK;Pei S;Shaman JL;Healy JM;Slayton RB;Biggerstaff M;Johansson MA;Runge MC;Viboud C
• 通讯作者: Viboud C

The variations of SIkJalpha model for COVID-19 forecasting and scenario projections

用于 COVID-19 预测和情景预测的 SIkJalpha 模型的变化

• DOI: 10.1016/j.epidem.2023.100729
• 发表时间: 2023
• 期刊: Epidemics
• 影响因子: 3.8
• 作者: Srivastava, Ajitesh