NSF Award
2030307
Nontechnical Abstract: <br/>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.<br/><br/>Technical Abstract:<br/>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.<br/><br/>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.<br/><br/>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.
