RAPID: Modeling the Severity and Transmissibility of COVID-19 in the USA with Intrinsic Behavior Change

RAPID：通过内在行为变化对美国 COVID-19 的严重性和传播性进行建模

• 批准号: 2031536
• 负责人: Peter Riley
• 金额: $ 19.96万
• 依托单位: Predictive Science Incorporated
• 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: 2020-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031536&HistoricalAwards=false
• 关键词: RAPID; Modeling; Severity; Transmissibility; COVID

As COVID-19 spreads through communities across the world, and particularly within the USA, a number of questions remain unanswered. Of particular importance is to what extent different mitigation and containment strategies affect the resulting number of ICU cases and/or deaths? This question will become ever more nuanced as communities begin to relax current “lockdown” orders to varying degrees. Additionally, to what extent do spatial and temporal changes in weather (temperature, precipitation, and humidity) as well as UV radiation modulate the disease’s evolution? Through a combination of unique data collection, model refinement, and scientific investigation, this study can shed valuable insight on these questions. The codes and derived data will be made available to the scientific community through GitHub repositories, CRAN packages, and web portals, and informal training will be provided for potentially interested stakeholders, such as county public health departments, the CDC, and DoD agencies.This investigation will use an existing state-of-the-art modeling and forecasting framework, Dynamics of Interacting Community Epidemics (DICE), to examine the human ecology of COVID-19 dynamics. DICE is a unique tool that can help reveal the impact of different containment and non-pharmaceutical mitigation strategies, as well as climate forcing, on the transmission of COVID-19. Uniquely, it is an arbitrarily scaled hybrid spatial metapopulation model in which individual communities experience deterministic disease dynamics, but between which the process of one community seeding an outbreak in another community is stochastic. DICE can be run at the county, state, region, or national level, or, various combinations of these sub-units can be coupled, depending on what data are available. DICE solves the system of SE1…EnI1…ImRX equations producing a modeled incidence profile and estimates of the reproduction number as a function of time, R(t), the severity of the outbreak, and parameters quantifying the efficacy of interventions. DICE already has the capability of incorporating school vacation data, and uses climate data from NASA and NOAA, and specific humidity, in particular, which has been shown to be important in forecasting the evolution of influenza. A range of methodologies for incorporating interventions, such as school closures, social distancing, and shelter-in-place orders have been recently tested and explored using a complementary single-population prototype tool (DRAFT), specifically developed to rapidly explore refinements that can be incorporated into DICE. DICE can both simulate possible future scenarios as well as fit to available data to estimate the efficacy of different intervention profiles, and also captures joint estimates of severity (Sev) and transmissibility (R(t)). As COVID-19 spreads across the U.S., community transmission can be evaluated in R-Sev space, which will provide crucial and strategic information to assist policymakers in making more informed decisions. Through the use of a Monte Carlo Markov Chain (MCMC) approach, DICE produces robust estimates of the uncertainties in the projections. Additionally, DICE is a multi-model algorithm, allowing the generation of forecasts for more than 32 model variants, which provides not only an estimate of the impact that various factors may play (e.g., climate), but also produces hyper-ensembles of model realizations, which, in turn provide additional estimates of uncertainty. This RAPID award is made by the Ecology and Evolution of Infectious Disease 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.

随着COVID-19在世界各地的社区传播，特别是在美国，许多问题仍然没有答案。特别重要的是，不同的缓解和遏制策略在多大程度上影响了ICU病例和/或死亡人数？随着社区开始在不同程度上放松当前的“封锁”命令，这个问题将变得越来越微妙。此外，天气（温度、降水和湿度）以及紫外线辐射的时空变化在多大程度上调节了疾病的演变？通过独特的数据收集，模型改进和科学调查的结合，这项研究可以对这些问题提供有价值的见解。 这些代码和衍生数据将通过GitHub存储库、CRAN包和门户网站提供给科学界，并将为潜在的利益相关者提供非正式培训，如县公共卫生部门、CDC和国防部机构。这项调查将使用现有的最先进的建模和预测框架，互动社区流行病动态（DICE），来研究COVID-19的人类生态学动态。DICE是一种独特的工具，可以帮助揭示不同的遏制和非药物缓解策略以及气候强迫对COVID-19传播的影响。独特的是，它是一个任意尺度的混合空间集合种群模型，其中单个社区经历确定性疾病动态，但其中一个社区在另一个社区传播疾病的过程是随机的。DICE可以在县、州、地区或国家一级运行，或者根据可用的数据，可以耦合这些子单元的各种组合。DICE求解SE1.EnI1.ImRX方程系统，生成建模的发病率曲线和作为时间、R（t）、爆发严重程度和量化干预效果的参数的函数的繁殖数量估计值。DICE已经有能力整合学校假期数据，并使用NASA和NOAA的气候数据，特别是特定湿度，这在预测流感演变方面非常重要。一系列纳入干预措施的方法，如学校关闭，社交距离，和庇护场所的订单，最近进行了测试和探索，使用一个互补的单一人口原型工具（草案），专门开发，以快速探索可以纳入DICE的改进。DICE既可以模拟可能的未来情景，也可以拟合现有数据，以估计不同干预方案的有效性，还可以捕获严重程度（Sev）和可传播性（R（t））的联合估计值。随着COVID-19在美国蔓延，可在R-Sev空间中评估社区传播情况，这将提供关键的战略信息，协助决策者作出更知情的决定。通过使用蒙特卡罗马尔可夫链（MCMC）方法，DICE对预测中的不确定性进行了稳健的估计。此外，DICE是一种多模型算法，允许为超过32个模型变量生成预测，这不仅提供了对各种因素可能发挥的影响的估计（例如，气候），但也产生超集合的模型实现，这反过来又提供了额外的估计不确定性。该奖项由环境生物学部门的传染病生态学和进化计划颁发，使用冠状病毒援助，救济和经济安全（CARES）法案的资金。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。