RAPID: The effect of contact network structure on the spread of COVID-19: balancing disease mitigation and socioeconomic well-being

  • Funded by National Science Foundation (NSF)
  • Total publications:1 publications

Grant number: 2030509

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2021
  • Known Financial Commitments (USD)

    $199,136
  • Funder

    National Science Foundation (NSF)
  • Principal Investigator

    Meggan Craft
  • Research Location

    United States of America
  • Lead Research Institution

    University of Minnesota-Twin Cities
  • Research 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

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Biological Sciences - What makes COVID-19 spread rapidly in some places, yet slowly in others? How should society lessen social distancing while limiting an increase in infections? To answer these questions, this Rapid Response Research (RAPID) project seeks to understand how patterns of interpersonal interaction (?structure?) in social contact networks affect disease spread in a population. The researchers will simulate a disease spreading through a variety of social contact networks, and use machine learning to relate each network?s structure to the number and timing of new infections. By limiting structures related to increased disease, societies may be able to reopen other parts of their economies while still curbing overall disease spread. The researchers will produce an interactive web application for the public and decision-makers to visualize trade-offs between reducing disease and maintaining social cohesion. This research will support the professional development of an early career scientist.

This research aims to determine the inherent risk of SARS-CoV-2 spread based on contact network structure. The researchers will use machine learning to 1) identify network structures that influence disease spread and 2) predict disease spread on empirical contact networks. Important network structures will serve as targets for simulated disease mitigation interventions (e.g. reducing structures that increase levels of disease or increasing structures that reduce disease levels). Finally, the researchers will investigate whether future outbreaks of COVID-19 or other diseases could be alleviated through optimizing social contact networks ahead of time. The outcomes of this research will inform and facilitate quick, efficient interventions to reduce the social and economic costs of COVID-19. This research will develop a general framework for relating disease to network structure. Thus, results can be generalized beyond the current pandemic, serving to further our understanding of potential future waves of COVID-19, as well as other directly-transmitted diseases in humans, livestock, and wildlife.

This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act

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.

Publicationslinked via Europe PMC

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The illusion of personal health decisions for infectious disease management: disease spread in social contact networks.