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Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model

  • Author(s) / Creator(s)
  • Classical epidemiological models assume mass action. However, this assumption is
    violated when interactions are not random. With the recent COVID-19 pandemic, and
    resulting shelter in place social distancing directives, mass action models must be modified
    to account for limited social interactions. In this paper we apply a pairwise network model
    with moment closure to study the early transmission of COVID-19 in New York and San
    Francisco and to investigate the factors determining the severity and duration of outbreak
    in these two cities. In particular, we consider the role of population density, transmission
    rates and social distancing on the disease dynamics and outcomes. Sensitivity analysis
    shows that there is a strongly negative correlation between the clustering coefficient in the
    pairwise model and the basic reproduction number and the effective reproduction number. The shelter in place policy makes the clustering coefficient increase thereby reducing
    the basic reproduction number and the effective reproduction number. By switching
    population densities in New York and San Francisco we demonstrate how the outbreak
    would progress if New York had the same density as San Francisco and vice-versa. The
    results underscore the crucial role that population density has in the epidemic outcomes.
    We also show that under the assumption of no further changes in policy or transmission
    dynamics not lifting the shelter in place policy would have little effect on final outbreak
    size in New York, but would reduce the final size in San Francisco by 97%.

  • Date created
    2022-01-01
  • Subjects / Keywords
  • Type of Item
    Article (Published)
  • DOI
    https://doi.org/10.7939/r3-adqn-ef61
  • License
    Attribution-NonCommercial 4.0 International