Simulating Pandemic Disease Spread and the Impact of Interventions in Complex Societal Networks
Abstract
Introduction
Projecting disease spread is challenging because of the heterogeneous nature of human interactions, including both natural societal interactions and how they change in response to pandemics. Simulations can provide important guidance regarding the likely impact of interventions on an assumed base case.
Methods
This paper uses assumptions based on the COVID-19 pandemic to construct a Susceptible-Infectious-Recovered model representative of US society, focusing on the interrelationships of groups with differing contact networks (essential/non-essential workers and urban/non-urban populations). The model is used to explore the impact of interventions (reduced interactions, vaccinations and selective isolation) on overall and group-specific disease spread.
Results
In the absence of herd immunity, temporary interventions will only reduce the overall number of disease cases moderately and spread them over a greater period of time unless they virtually eliminate disease and no new infections occur from exogenous sources. Vaccinations can provide stronger benefit, but can be limited by efficacy and utilization rates.
Conclusions
While a highly effective and broadly utilized vaccine might halt disease spread, some combination of increased long-term surveillance and selective isolation of the most vulnerable populations might be necessary to minimize morbidity and mortality if only moderately effective vaccines are available.
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