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.

当Covid-19通过世界各地，尤其是在美国的社区传播时，许多问题仍然没有解决。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/或死亡的数量？随着社区开始在不同程度上放松当前的“锁定”命令，这个问题将变得更加细微。此外，天气（温度，降水和湿度）的空间和临时变化以及紫外线辐射会在多大程度上调节疾病的演变？通过独特的数据收集，改进和科学研究的结合，这项研究可以对这些问题提供宝贵的见解。 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 interesting stakeholders, such as county public health departments, the CDC, and DoD agencies.This investment 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动力学。 DICE是一种独特的工具，可以帮助揭示不同遏制和非药物缓解策略以及气候强迫对Covid-19的传播的影响。独特的是，这是一个任意缩放的混合空间跨种群模型，在该模型中，单个社区经历了确定性疾病动态，但是在一个社区之间在另一个社区中播种爆发的过程是随机的。骰子可以在县，州，地区或国家一级进行，或者可以根据可用的数据耦合这些子单元的各种组合。 DICE解决了SE1…ENI1…IMRX方程的系统，该方程产生了建模的事件曲线，并估算了繁殖数，这是时间，R（t）的函数，爆发的严重程度以及量化干预效率的参数。 DICE已经具有合并的学校度假数据的能力，并使用了NASA和NOAA的气候数据，尤其是特定的湿度，这在预测影响力的演变中很重要。最近，使用完整的单个构图原型工具（草稿）对合并干预措施的一系列方法进行了测试和探索，这些方法已被测试和探索，这些工具（草稿）是专门为快速探索可以掺入骰子中的改进而开发的。骰子既可以模拟未来的情况，又可以适合可用数据以估计不同干预措施的效率，还可以捕获严重程度（SEV）和传播（R（t））的关节估计。随着Covid-19通过美国传播，可以在R-SEV-Space中评估社区传播，这将提供至关重要的战略信息，以帮助决策者做出更明智的决定。通过使用Monte Carlo Markov链（MCMC）方法，骰子可以对项目中的不确定性产生强大的估计。此外，DICE是一种多模型算法，允许32多个模型变体的森林产生，这不仅提供了各种因素可能发挥的影响（例如气候）的估计，而且还产生了模型实现的超浓度，这反过来又提供了额外的不确定性估计。该快速奖是由环境生物学系的生态学和传染病计划的进化，使用冠状病毒援助，救济和经济安全（CARES）ACT的资金。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响来审查Criteria，通过评估诚实地将其视为诚实的支持。