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RAPID: Predicting Coronavirus Disease (COVID-19) Impact with Multiscale Contact and Transmission Mitigation

RAPID：通过多尺度接触和传播缓解来预测冠状病毒病 (COVID-19) 的影响

基本信息

批准号：
2030307
负责人：
Arshad Kudrolli
金额：
$ 20万
依托单位：
Clark 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 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030307&HistoricalAwards=false
关键词：
RAPID Predicting Coronavirus Disease COVID

项目摘要

Nontechnical Abstract: The rapid spread of new coronavirus SARS-CoV-2, which causes Coronavirus Disease (covid-19), requires a multidisciplinary mitigation strategy from the clinical to physical host-to-host transmission modelling. Data is required on the transmission of pathogen carrying airborne mucosalivary droplets and aerosols generated during normal breathing, talking, sneezing, and coughing. Synthetic exhalations will be measured leveraging advanced prototyping to obtain data needed to model the spread of covid-19, and the efficacy of personal protection devices and face coverings fabricated with various weaves and materials will be tested. Physical data related to temperature, humidity, and airflow on survival and dispersion of exhalations will be obtained. The data will be integrated using supervised machine learning methods, mathematical network simulations, and epidemiological data to develop an individual-based method that can give pandemic management results. Physical data will be published on transmission rates, including wearing of personal protective equipment and face coverings with various weaves, to inform mitigation strategies to alleviate covid-19 pandemic. Interactive Web based resources will be used for immediate broad dissemination of data and learning outcomes on covid-19 to the public, in addition to peer reviewed publications and training post-doctoral and undergraduate researchers in methods leading to pandemic mitigation.Technical Abstract:The mode of transmission and extent of environmental contaminations on the outbreak of the Coronavirus Disease 2019 (covid-19), while sharing features with severe acute respiratory syndrome and other infectious diseases, remains unknown. This project will address fundamental rheology-matched metrics of transport and survival of airborne exhalation droplets and aerosols that carry coronavirus and on surfaces needed as input parameters for modeling mitigation. Impact of personal protective equipment on individual prognosis, with physical data related to temperature, humidity, and airflow-dependent dispersion distance of pathogen bearing viscoelastic droplets corresponding to breathing, sneezing, and coughing, will be obtained. The impact of the measured transmission rates on the spread and recurrence will be investigated with epidemiological data integrated with deep learning to implement a scalable, individual-based, stochastic, spatial model. Resulting peer-reviewed publications will serve as trusted source for calculation of covid-19 transmissibility and personal protection strategies. Post-doctoral and undergraduate researchers versed in fluid dynamics, soft matter physics, and network simulations will be trained toward mitigating infectious disease spread.This Rapid Response Research (RAPID) grant supports research that will result in spatiotemporal mucosalivary droplet transmission range data required to develop covid-19 mitigation network methods with funding from the CARES Act managed by the Condensed Matter Physics Program in the Division of Materials Research of the Mathematical and Physical Sciences Directorate.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.

非技术摘要：新型冠状病毒SARS-CoV-2的快速传播导致冠状病毒病（covid-19），需要从临床到物理宿主间传播模型的多学科缓解策略。需要关于正常呼吸、说话、打喷嚏和咳嗽过程中产生的携带空气中粘膜唾液飞沫和气溶胶的病原体传播的数据。将利用先进的原型设计来测量合成呼气，以获得模拟Covid-19传播所需的数据，并将测试用各种编织和材料制成的个人防护装置和面罩的功效。将获得与温度、湿度和气流有关的生存和呼气分散的物理数据。这些数据将使用监督机器学习方法、数学网络模拟和流行病学数据进行整合，以开发一种基于个人的方法，从而得出流行病管理结果。将公布有关传播率的物理数据，包括个人防护设备的穿戴和各种编织的面罩，以告知缓解COVID-19大流行的缓解策略。基于网络的交互式资源将用于向公众立即广泛传播有关COVID-19的数据和学习成果，以及同行评审的出版物和培训博士后和本科生研究人员，以减轻大流行的方法。技术摘要：2019冠状病毒病（COVID-19）爆发的传播模式和环境污染程度，尽管与严重急性呼吸系统综合征和其他传染病有共同的特征，但仍然未知。该项目将解决携带冠状病毒的空气中呼出液滴和气溶胶的运输和存活的基本流变学匹配指标，以及作为建模缓解输入参数所需的表面。将获得个人防护装备对个人预后的影响，以及与呼吸、打喷嚏和咳嗽相对应的携带粘弹性液滴的病原体的温度、湿度和取决于气流的散布距离相关的物理数据。测量的传播率对传播和复发的影响将通过与深度学习相结合的流行病学数据进行研究，以实现可扩展的，基于个人的，随机的空间模型。由此产生的同行评审出版物将作为计算新冠病毒传播率和个人保护策略的可信来源。博士后和本科研究人员精通流体动力学，软物质物理，和网络模拟将被训练以减轻传染病传播。这项快速反应研究（RAPID）资助支持的研究将产生开发新冠病毒所需的时空粘膜唾液液滴传播范围数据，19种缓解网络方法，由数学和物理科学材料研究部的凝聚态物理计划管理的CARES法案提供资金该奖项反映了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。