Modeling the effect of transient populations on epidemics in Washington DC

Abstract

Large numbers of transients visit big cities, where they come into contact with many people at crowded areas. However, epidemiological studies have not paid much attention to the role of this subpopulation in disease spread. We evaluate the effect of transients on epidemics by extending a synthetic population model for the Washington DC metro area to include leisure and business travelers. A synthetic population is obtained by combining multiple data sources to build a detailed minute-by-minute simulation of population interaction resulting in a contact network. We simulate an influenza-like illness over the contact network to evaluate the effects of transients on the number of infected residents. We find that there are significantly more infections when transients are considered. Since much population mixing happens at major tourism locations, we evaluate two targeted interventions: closing museums and promoting healthy behavior (such as the use of hand sanitizers, covering coughs, etc.) at museums. Surprisingly, closing museums has no beneficial effect. However, promoting healthy behavior at the museums can both reduce and delay the epidemic peak. We analytically derive the reproductive number and perform stability analysis using an ODE-based model.

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Bi-Virus SIS Epidemics over Networks: Qualitative Analysis

“…As we explain in Section III, the results in [12] for the single virus dynamics do not apply to the bivirus case, and we need to develop a new methodology. Reference [13] studies via numerical simulations the epidemics over big cities resorting to dynamical systems similar to the one in equation (3), to model the mixing of different interacting groups of individuals.…”

Summarizing Simulation Results Using Causally-Relevant States

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