RAPID: Development of a Nonlinear Activity Response Model for Coronavirus (COVID-19) Scenario Projections Based on the Observations of the Shutdown-Reopening Cycle of China
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2030425
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$199,811Funder
National Science Foundation (NSF)Principal Investigator
Yuhang WangResearch Location
ChinaLead Research Institution
Georgia Tech Research CorporationResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Impact/ effectiveness of control measures
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Minority communities unspecified
Occupations of Interest
Unspecified
Abstract
Geosciences - This RAPID project will investigate the cycle of shutdown and reopening of businesses in China due to COVID-19, and assess the usefulness of studying this cycle for providing guidance for policymaking in the U.S., European countries, and the countries where the medical testing capability is severely limited. The response to COVID-19 has varied significantly from the epicenter (Wuhan and Hunan province) to the other 25 provinces, 4 provincial-level megacities (Beijing, Shanghai, Tianjin, and Chongqing), and 5 autonomous regions for minority ethnic groups. The proxy data for response activities includes the processed TROPOMI tropospheric vertical NO2 column data and inverse modeling of daily NOx emissions in China.
The hypotheses of this project are that (1) the COVID-19 infection data (including coronavirus positive test, hospitalization, and mortality data) and government policies largely shape the responses by the society and businesses; (2) near real-time monitoring of tropospheric column NO2 provide timely high spatiotemporal data for gauging the activity responses by the society and businesses, which are unavailable through conventional means; (3) with the large datasets of varying degrees of COVID-19 infection, governmental policy, and activity responses in different regions of China, a nonlinear response model will be developed and this model can later be corrected with data from US and other countries to provide policymaking guidance through scenario analysis.
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.
The hypotheses of this project are that (1) the COVID-19 infection data (including coronavirus positive test, hospitalization, and mortality data) and government policies largely shape the responses by the society and businesses; (2) near real-time monitoring of tropospheric column NO2 provide timely high spatiotemporal data for gauging the activity responses by the society and businesses, which are unavailable through conventional means; (3) with the large datasets of varying degrees of COVID-19 infection, governmental policy, and activity responses in different regions of China, a nonlinear response model will be developed and this model can later be corrected with data from US and other countries to provide policymaking guidance through scenario analysis.
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.