Award Abstract #2027306

RAPID:Comparative quantitative microbial risk assessment of COVID-19 transmission through droplets, aerosols and contaminated surfaces

See grant description on NSF site

Program Manager:

Mamadou Diallo

Active Dates:

Awarded Amount:



Sunny Jiang

Awardee Organization:

University of California-Irvine


Engineering (ENG)


The global COVID-19 pandemic caused by the virus SARS-CoV2 has caused considerable human health and economic impacts because of its rapid spread and high fatality rate. Examining the trend of COVID-19 data suggests other routes of transmission beyond direct person-to-person transmission are likely. Recent reports show that aerosols containing SARS-CoV2 could still infect an individual for up to 3 hours later, and plastic surfaces contaminated with SARS-CoV2 can remain infective for up to 3 days. Trace evidence of the virus was recovered from Diamond Princess cruise ship cabins up to 17 days after the passengers left. The goal of this project is to understand COVID-19 transmission through virus-contaminated respiratory droplets, fine solid or liquid particles in air, and contact with virus-contaminated surfaces. This study will model human behaviors effecting disease transmission, the properties of the virus, and environmental factors that may affect disease transmission to predict infection risk. If successful, this project will help describe transmission patterns of SARS-CoV2 and provide new ways to slow or halt the spread of this disease in the US. The goal of this project is to identify the infection risk of the novel coronavirus SARS-CoV2 through different exposure routes in order to prioritize measures for public health protection. The specific objectives of this work are to: 1) model the spread of SARS-CoV2 through droplets and aerosols from coughing/sneezing and through aerosols generated from toilet flushing under different environmental conditions; 2) develop exposure models through inhalation of droplets and aerosols to determine the dose of single and repeated exposure; and 3) model repeated exposure through contact with contaminated surfaces and hands-to-face transmission. The models will incorporate mechanistic understanding of aerosol generation, transport, and fate under different conditions, as well as data fitting to predict aerosol size and concentration under different test scenarios. SARS-CoV2 viral shedding rate from patients and its persistence in different environmental media will be used to model the viral load through aerosol and contact exposure. Human physiology and habits will be coupled with viral attack rates to quantify the risk using a Monte Carlo probability simulator. The sensitivity analysis will also identify data gaps for rapid data collection in collaboration with teams of researchers from different disciplines. The results of this project will contribute to the understanding of global spread of infectious disease beyond short and long-term remediation strategies on COVID-19.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.

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