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RAPID: Collaborative Research: Optimizing non-pharmaceutical and pharmaceutical interventions for controlling COVID-19 at the community-level

RAPID：合作研究：优化非药物和药物干预措施以在社区层面控制 COVID-19

基本信息

批准号：
2028632
负责人：
Martial Ndeffo Mbah
金额：
$ 8.75万
依托单位：
Texas A&M University
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：
2020-05-15 至 2021-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028632&HistoricalAwards=false
关键词：
RAPID Collaborative Research Optimizing non

项目摘要

During emerging infectious disease outbreaks, such as the current novel coronavirus (COVID-19) pandemic, mathematical models are important tools to help inform public health recommendations and best utilization of limited resources. This research will develop and analyze data-driven mathematical models to predict the spread and evaluate the success of various public health intervention strategies to control COVID-19 in the US and abroad. The models will account for the characteristics of the pathogen, the variation of transmission that occur within community and in hospital settings, and geographical difference in transmission. The broader impacts from these models will provide real-time information to assist public health officials and decision-makers in making critical decisions on COVID-19 control policies and resource allocation. Standard modeling approach such as compartmental population-based approach may not be suitable for modeling the spread of COVID-19, due to the high-level of heterogeneity of such systems, disease pathways, population makeup, host interactions on different levels of organization (household, workplace/school, social activities), and adaptive features of human behavior. The investigators will employ an individual-based modeling approach (IBM) that will accommodate such local heterogeneities. The investigators will use social, demographic, and epidemiological data of COVID-19 cases in the US and Korea, as well as hospital-level and city-level contact tracing data of COVID-19 in Wuhan, China, to parameterize their models. First, they will develop an IBM hospital-based model to explore different hospital-based interventions for mitigating the risk of nosocomial transmission of COVID-19 between patients and healthcare workers. Second, they will develop an IBM community-based model to evaluate and identify optimal non-pharmaceutical and potential pharmaceutical interventions for COVID-19 control in different local communities (city-county scale). The non-pharmaceutical interventions will include, amongst others: case isolation at home or hospitals, voluntary self-quarantine, stopping mass gathering, closure of schools, universities, or workplaces, and social distancing such as reduction of contacts, wearing of protective masks, and reduction of individuals' movements. Pharmaceutical interventions will include novel vaccines and antiviral therapies.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) ActThis 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.

在新出现的传染病爆发期间，例如当前的新型冠状病毒（COVID-19）大流行，数学模型是帮助提供公共卫生建议和最佳利用有限资源的重要工具。这项研究将开发和分析数据驱动的数学模型，以预测美国和国外控制 COVID-19 的各种公共卫生干预策略的传播并评估其成功与否。这些模型将考虑病原体的特征、社区内和医院环境中发生的传播变化以及传播的地理差异。这些模型的更广泛影响将提供实时信息，以协助公共卫生官员和决策者就 COVID-19 控制政策和资源分配做出关键决策。标准建模方法（例如基于群体的方法）可能不适合对 COVID-19 的传播进行建模，因为此类系统、疾病途径、人口构成、不同组织级别（家庭、工作场所/学校、社会活动）上的宿主相互作用以及人类行为的适应性特征具有高度异质性。研究人员将采用基于个体的建模方法（IBM）来适应这种局部异质性。研究人员将使用美国和韩国的 COVID-19 病例的社会、人口和流行病学数据，以及中国武汉的医院级和城市级 COVID-19 接触者追踪数据来参数化他们的模型。首先，他们将开发一个基于 IBM 医院的模型，以探索不同的基于医院的干预措施，以减轻患者和医护人员之间 COVID-19 的医院传播风险。其次，他们将开发一个基于 IBM 社区的模型，以评估和确定不同当地社区（市县规模）控制 COVID-19 的最佳非药物和潜在药物干预措施。非药物干预措施将包括：在家或医院进行病例隔离、自愿自我隔离、停止群众聚集、关闭学校、大学或工作场所，以及保持社交距离，例如减少接触、佩戴防护口罩和减少个人活动。药物干预措施将包括新型疫苗和抗病毒疗法。该 RAPID 奖项由环境生物学部的传染病生态与进化计划使用冠状病毒援助、救济和经济安全 (CARES) 法案的资金颁发。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进行评估，被认为值得支持标准。