RAPID: Statistical inference of incidence of SARS-CoV-2 in the US using multiple data streams to identify levels of immunity and the impact of non-pharmaceutical interventions
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2223843
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Key facts
Disease
COVID-19Start & end year
20222023Known Financial Commitments (USD)
$200,000Funder
National Science Foundation (NSF)Principal Investigator
Derek CummingsResearch Location
United States of AmericaLead Research Institution
University of FloridaResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Disease transmission dynamics
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
The goal of this study is to integrate multiple, independent data sources to estimate the rate of SARS-CoV-2 infections across the US over time. Population-based SARS-CoV-2 serological assays are critical for understanding cumulative incidence and population-level immunity. The US CDC, in partnership with a number of laboratories, has conducted nationwide serosurveys which can help retrospectively assess the cumulative number of total infections. However, data from these surveys may be difficult to interpret due to heterogeneity in antibody response across individuals, by assay, and over time since infection. Reconciling patterns observed in seroprevalence with other data sources including reported COVID-19 cases and deaths can explain variation in seroprevalence across space and time in the US CDC. In addition, the project will estimate the proportion of the population with recent immunizing events (infection or vaccination) to understand the immunity landscape prior to the Omicron-variant-driven wave in 2021-2022 in the US. The project will develop tools to jointly analyze serology, case
and death data, and contribute to the training of a post-doctoral scholar.
The primary objective in this study is to integrate multiple independent data streams using statistical and mechanistic models to estimate the rate of seroreversion in assays used in serosurveys across the US, and estimate seroprevalence and cumulative incidence over time by state. The model will provide information about SARS-CoV-2 transmission from case, hospitalization and death data by taking a multi-objective approach and adapting fast inference techniques that we have developed. Methods such as these have been applied to state-level
data on COVID-19 incidence, including by this group.
This project was funded in collaboration with the CDC to support rapid-response research projects to further advance federal infectious disease modeling capabilities.
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.
and death data, and contribute to the training of a post-doctoral scholar.
The primary objective in this study is to integrate multiple independent data streams using statistical and mechanistic models to estimate the rate of seroreversion in assays used in serosurveys across the US, and estimate seroprevalence and cumulative incidence over time by state. The model will provide information about SARS-CoV-2 transmission from case, hospitalization and death data by taking a multi-objective approach and adapting fast inference techniques that we have developed. Methods such as these have been applied to state-level
data on COVID-19 incidence, including by this group.
This project was funded in collaboration with the CDC to support rapid-response research projects to further advance federal infectious disease modeling capabilities.
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.