RAPID: An Ensemble Approach to Combine Predictions from COVID-19 Simulations

RAPID：结合 COVID-19 模拟预测的集成方法

• 批准号: 2030685
• 负责人: Taylor Anderson
• 金额: $ 20万
• 依托单位: George Mason University
• 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: 2020-05-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030685&HistoricalAwards=false
• 关键词: RAPID; Ensemble; Approach; Combine; Predictions

Decision-makers and the general public rely on models to simulate the SARS-CoV-2 virus spread and predict the number of infections and fatalities. Model predictions are critical to rapidly develop policy interventions that mitigate COVID-19, to anticipate impacts on health care resources, and to strategize how best to impose and lift public health guidelines. Existing COVID-19 models produce radically different predictions, thus creating confusion and mistrust over their use. Therefore, there is an urgent need to compare between the wide-range of existing COVID-19 models and their predictions. The goal of this project is to find a consensus among various model predictions and to make the different model assumptions and uncertainty transparent. An interactive web-based dashboard will serve as an open and accessible tool to inform the public, fellow researchers, and decision-makers where existing models agree and disagree on predictions. The predictions that are agreed upon by existing models can then be used with greater confidence and trust as the basis for effective decision-making to save lives and resources. Apart from the practical importance for the implementation of effective pandemic control measures and public health strategies, other broader impacts are professional development opportunities for early career researchers and training opportunities for a post-doctoral scholar. To find a consensus among various model predictions, this project will develop a novel ensemble prediction approach that (1) aligns different COVID-19 simulation models and (2) uses a time series clustering technique to unify model predictions. In the model alignment stage, a range of open-source and publicly available COVID-19 simulation models will be selected, aligned based on their parameters, and run as needed. A broad set of predictions will be obtained from the model results, where each prediction represents a possible world with a corresponding number of new cases, fatalities, and other quantities of interest. The time series clustering stage will project possible worlds into a feature space and apply clustering algorithms to find similar possible worlds. For each cluster, a representative possible world will be selected, enriched with measures of uncertainty, and visualized using the dashboard. This project advances the theoretical knowledge base for model alignment approaches and representative uncertain clustering for simulation model predictions. The unification of model predictions into a scientific consensus can be used to inform decision-makers better so that they can develop life-saving interventions.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.

决策者和公众依靠模型来模拟SARS-CoV-2病毒的传播，并预测感染和死亡人数。模型预测对于快速制定缓解COVID-19的政策干预措施、预测对医疗资源的影响以及制定如何最好地实施和提升公共卫生指导方针的战略至关重要。现有的COVID-19模型产生了完全不同的预测，因此对其使用造成了混乱和不信任。因此，迫切需要对现有的各种COVID-19模型及其预测进行比较。该项目的目标是在各种模型预测之间找到共识，并使不同的模型假设和不确定性透明化。一个基于网络的交互式仪表板将作为一个开放和可访问的工具，告知公众，研究人员和决策者现有模型对预测的一致性和不一致性。然后，可以更有信心和信任地使用现有模型商定的预测，作为有效决策的基础，以拯救生命和资源。除了对实施有效的流行病控制措施和公共卫生战略的实际重要性外，其他更广泛的影响是为早期职业研究人员提供专业发展机会和为博士后学者提供培训机会。为了在各种模型预测之间找到共识，该项目将开发一种新的集合预测方法，（1）对齐不同的COVID-19模拟模型，（2）使用时间序列聚类技术来统一模型预测。在模型调整阶段，将选择一系列开源和公开可用的COVID-19模拟模型，根据其参数进行调整，并根据需要运行。将从模型结果中获得一组广泛的预测，其中每个预测代表一个可能的世界，具有相应数量的新病例，死亡人数和其他感兴趣的数量。时间序列聚类阶段将把可能的世界投影到特征空间中，并应用聚类算法来找到相似的可能世界。对于每个集群，将选择一个代表性的可能世界，用不确定性的度量来丰富，并使用仪表板进行可视化。该项目推进了模型对齐方法的理论知识基础和模拟模型预测的代表性不确定聚类。将模型预测统一为科学共识可以用来更好地为决策者提供信息，以便他们能够制定拯救生命的干预措施。这个RAPID奖由环境生物学部传染病生态学和进化计划颁发，使用冠状病毒援助，救济，该奖项反映了NSF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。

项目成果

Spatiotemporal prediction of foot traffic

• DOI: 10.1145/3486183.3490997
• 发表时间: 2021-11
• 期刊: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising
• 作者: Samiul Islam;Dhruv Gandhi;Justin Elarde;T. Anderson;A. Roess;Timothy F. Leslie;H. Kavak;Andreas Z