RAPID: COVID-19 comparative modeling and analyses of outbreaks using mechanistic and ensemble machine learning and the development of a platform for projection and management

RAPID：使用机械和集成机器学习对 COVID-19 疫情进行比较建模和分析，并开发预测和管理平台

• 批准号: 2032264
• 负责人: Wayne Getz
• 金额: $ 20万
• 依托单位: University of California-Berkeley
• 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=2032264&HistoricalAwards=false
• 关键词: RAPID; COVID; 19; comparative; modeling

Numerical projections of reliable infection and disease mortality rates within cities, counties, states, and countries, as well as identification of the factors most responsible for these rates are critical to rational management of the ongoing Covid-19 pandemic. In the absence of effective therapeutics and vaccines, a deeper understanding of the impact of societal measures (distancing, contact tracing, quarantining, etc.) on local Covid-19 outbreaks are needed by administrators and healthcare professionals in making decisions that affect the tradeoff between the physical health of individuals and the economic health of communities. The aims of the proposal are twofold. First, to use cutting-edge statistical models to uncover the factors that most affect SARS-CoV-2 transmission and mortality rates. Second, to provide decisions makers with a simple-to-use, extensive instruction supported, data and scenario analysis (DASA) platform for evaluating the implications of different policy measures, including the implementation and relaxation of social distancing behavior, surveillance, contact tracing, patient isolation, and vaccination (once suitable vaccines are available). Additionally, this DASA platform will be suitable for training students at the undergraduate and graduate levels in public health and allied programs, as well as providing an analytical tool for students carrying out epidemiological research.The epidemiological model that underpins the Numerus Model Builder DASA Covid-19 platform includes modifications of the standard SEIR (Susceptible, Exposed/Latent, Infectious, Recovered) formulation to incorporate an explicit contact (C) class, as well as dividing infectious individuals into pre/asymptomatic (A) and symptomatic infectious (I) disease states to yield a SCLAIV model (where V refers to naturally vaccinated/recovered class). Individuals in the C class can either thwart (return to the S class) or succumb to (move onto the L=E class) pathogen invasion after making contact with the SARS-CoV-2 pathogen. The formulation also includes a parallel series of Sr, Cr, Lr, Ar, Ir and Vr classes that correspond to individuals moving into these reduced-exposure SCLAIV-response classes at rates determined by the driving actions of Covid-19 policy measures that have been put in place. The values of the SCLAIV+reponse model parameters are influenced by various factors that will be identified using statistical machine learning methods. In particular, “superlearners” that are a mix of parametric and nonparametric ensemble machine learning methods will be used to identify the factors responsible for observed spatio-temporal patterns across different, regional Covid-19 outbreaks, using appropriate data scraped from the worldwide web. This RAPID award is made by the Ecology and Evolution of Infectious Disease Program in the Division of Environmental Bioloy, 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.

对城市、县、州和国家内可靠的感染率和疾病死亡率进行数字预测，并确定造成这些比率的主要因素，对于合理管理当前的Covid-19大流行至关重要。在缺乏有效治疗方法和疫苗的情况下，管理人员和医疗保健专业人员在做出影响个人身体健康和社区经济健康之间权衡的决策时，需要更深入地了解社会措施（保持距离、接触者追踪、隔离等）对当地Covid-19疫情的影响。这项提议的目的是双重的。首先，利用尖端的统计模型，揭示最影响SARS-CoV-2传播和死亡率的因素。第二，为决策者提供一个简单易用、有广泛指导支持的数据和情景分析（DASA）平台，以评估不同政策措施的影响，包括实施和放松社交距离行为、监测、接触者追踪、患者隔离和疫苗接种（一旦有合适的疫苗）。此外，该DASA平台将适用于公共卫生和相关项目的本科生和研究生培训，并为学生进行流行病学研究提供分析工具。支撑Numerus model Builder DASA Covid-19平台的流行病学模型包括对标准SEIR（易感、暴露/潜伏、感染、恢复）配方的修改，以纳入明确的接触(C)类别，并将感染个体分为前/无症状(A)和症状性感染(I)疾病状态，以产生SCLAIV模型（其中V指自然接种疫苗/恢复类别）。C类个体在接触SARS-CoV-2病原体后，可以阻止（返回S类）或屈服于（进入L=E类）病原体的入侵。该公式还包括一系列平行的Sr、Cr、Lr、Ar、Ir和Vr等级，对应于以已实施的Covid-19政策措施的驱动作用确定的速率进入这些低暴露sclv反应等级的个人。SCLAIV+响应模型参数的值受到各种因素的影响，这些因素将使用统计机器学习方法识别。特别是，“超级学习者”是参数和非参数集成机器学习方法的混合体，将使用从全球网络中抓取的适当数据，来识别导致不同地区Covid-19疫情中观察到的时空模式的因素。该RAPID奖由环境生物学部传染病生态和进化项目提供，资金来自《冠状病毒援助、救济和经济安全法案》。该奖项反映了美国国家科学基金会的法定使命，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。