RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: unknown
Grant search
Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$173,640Funder
National Science Foundation (NSF)Principal Investigator
Madhav MaratheResearch Location
United States of AmericaLead Research Institution
University of Virginia Main CampusResearch 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
Unspecified
Occupations of Interest
Unspecified
Abstract
The Novel 2019 Coronavirus (COVID-19) has already caused unprecedented global social, economic, and health impact. This project will develop synthetic global multi-scale social contact networks. The synthetic but realistic social contact networks can capture human interactions either at an individual or community level. The networks can be used in conjunction with agent-based models to simulate the ongoing COVID-19 pandemic. The simulations can in-turn be used to design and assess various interventions that balance health benefits with social and economic costs. Data will be made available to the scientific community. The PIs will also work with other research groups and continue their partnership with other federal and state agencies to support their response efforts.
Developing synthetic social contact networks is a statistically and algorithmically challenging problem. This project will synthesize ensembles of two classes of synthetic social contact networks -- patch-based meta-population networks and individualized synthetic social contact populations and networks using a combination of machine learning and data driven modeling techniques. The need for such data driven mechanistic modeling methods has become abundantly clear in regimes when the available data is sparse and noisy. The project will undertake a detailed statistical analysis of the algorithms and the synthetic networks they produce. This includes methods to conduct global sensitivity analysis and methods to quantify the uncertainty in the outcomes as a function of the network structure. One of the many uses of this resource, is to support individual-based as well as meta-population-based simulation models for epidemic spread in general, and COVID-19 in particular. Beyond supporting ongoing COVID-19 outbreaks, these synthetic social contact networks will be useful in responding to other epidemics. The PIs plan to make this data available to the global research community so that researchers around the world can immediately use it to assess the pandemic and the response efforts in their respective regions.
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.
Developing synthetic social contact networks is a statistically and algorithmically challenging problem. This project will synthesize ensembles of two classes of synthetic social contact networks -- patch-based meta-population networks and individualized synthetic social contact populations and networks using a combination of machine learning and data driven modeling techniques. The need for such data driven mechanistic modeling methods has become abundantly clear in regimes when the available data is sparse and noisy. The project will undertake a detailed statistical analysis of the algorithms and the synthetic networks they produce. This includes methods to conduct global sensitivity analysis and methods to quantify the uncertainty in the outcomes as a function of the network structure. One of the many uses of this resource, is to support individual-based as well as meta-population-based simulation models for epidemic spread in general, and COVID-19 in particular. Beyond supporting ongoing COVID-19 outbreaks, these synthetic social contact networks will be useful in responding to other epidemics. The PIs plan to make this data available to the global research community so that researchers around the world can immediately use it to assess the pandemic and the response efforts in their respective regions.
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.