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（尤其是在美国）社区传播，许多问题仍未得到解答，特别重要的是不同的缓解和遏制策略会在多大程度上影响 ICU 病例和/或死亡人数？随着社区开始不同程度地放松当前的“封锁”命令，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？的组合通过独特的数据收集、模型完善和科学研究，这项研究可以为这些问题提供有价值的见解，代码和派生数据将通过 GitHub 存储库、CRAN 包和门户网站提供给科学界，并且将提供非正式培训。为潜在感兴趣的利益相关者提供，例如县公共卫生部门、疾病预防控制中心和国防部机构。这项调查将使用现有的最先进的建模和预测框架——社区流行病相互作用动态（DICE）来检查COVID-19 动态的人类生态学是一种独特的工具，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。混合空间集合种群模型，其中各个社区经历确定性疾病动态，但一个社区在另一个社区传播疾病爆发的过程是随机的，DICE 可以在县、州、地区或国家级别运行，或者在不同级别运行。这些子单元的组合可以耦合，具体取决于可用的数据，DICE 求解 SE1…EnI1…ImRX 方程组，生成建模的发生率曲线和作为时间函数 R(t) 的繁殖数估计值。 DICE 已经能够整合学校假期数据，并使用 NASA 和 NOAA 的气候数据，特别是比湿度，这已被证明在预测中很重要。的演变最近测试了一系列纳入干预措施的方法，例如关闭学校、保持社交距离和就地避难令，并使用了专门为快速探索可改进的补充单一人群原型工具（DRAFT）。纳入 DICE 中。DICE 既可以模拟未来可能出现的情况，也可以拟合现有数据来估计不同干预措施的效果，还可以捕获严重程度 (Sev) 和传播率 (R(t)) 的联合估计。 19 传播在美国，可以在 R-Sev 空间中评估社区传播，这将提供重要的战略信息，以帮助决策者通过使用蒙特卡罗马尔可夫链 (MCMC) 方法做出更明智的决策。此外，DICE 是一种多模型算法，可以生成超过 32 个模型变体的预测，不仅可以估计各种因素（例如气候）可能产生的影响，还可以生成预测结果。该 RAPID 奖项由环境生物学部门的传染病生态学和进化项目使用来自冠状病毒援助、救济和经济安全的资金颁发。 (CARES) 法案。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。