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

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，以及确定对这些比率最负责任的因素，对于合理管理正在进行的新冠肺炎大流行至关重要。 在缺乏有效的治疗方法和疫苗的情况下，更深入地了解社会措施（保持距离、追踪接触者、预防接种等）的影响，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，需要了解当地新冠肺炎疫情的情况。 该提案有两个目的。首先，使用最先进的统计模型来揭示最影响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，用于评估不同政策措施的影响，包括实施和放宽社交距离行为、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平台将适用于培训公共卫生和相关项目的本科生和研究生，并为开展流行病学研究的学生提供分析工具。支持Numerus Model Builder DASA Covid-19平台的流行病学模型包括对标准SEIR的修改（易感、暴露/潜伏、感染、潜伏）制剂，纳入明确接触（C）类别，以及将传染性个体分为前/无症状（A）和有症状的传染性（I）疾病状态以产生SCLAIV模型（其中V是指自然接种/恢复类）。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）病原体入侵。 该公式还包括一系列平行的Sr、Cr、Lr、Ar、Ir和Vr类别，这些类别对应于按照已实施的Covid-19政策措施的驱动行动确定的速度进入这些减少暴露的SCLAIV反应类别的个人。SCLAIV+响应模型参数的值受各种因素的影响，这些因素将使用统计机器学习方法进行识别。 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，将用于使用从全球网络上收集的适当数据，识别不同区域Covid-19疫情中观察到的时空模式的因素。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。

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.