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Optimal, near-optimal, and robust epidemic control

Medicine and Health

Optimal, near-optimal, and robust epidemic control

D. H. Morris, F. W. Rossine, et al.

This research by Dylan H. Morris, Fernando W. Rossine, Joshua B. Plotkin, and Simon A. Levin explores how to effectively use time-limited interventions during a novel disease outbreak. The study highlights that while optimal strategies are essential, simple and implementable approaches can yield impressive results. However, small errors in execution can dramatically affect outcomes, underscoring the importance of timely interventions for robust disease control.

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~3 min • Beginner • English
Abstract
In the absence of drugs and vaccines, policymakers use non-pharmaceutical interventions such as social distancing to decrease rates of disease-causing contact, aiming to reduce or delay the epidemic peak. Because these measures carry social and economic costs, they may only be maintainable for short periods. We derive the theoretically optimal strategy for using a time-limited intervention to reduce the peak prevalence of a novel disease in the classic Susceptible–Infectious–Recovered (SIR) model. We show that broad, easier-to-implement strategies can perform nearly as well as the optimal strategy. However, neither the optimal nor these near-optimal strategies are robust to implementation error: small errors in intervention timing can produce large increases in peak prevalence. Robust control requires a strong, early, and ideally sustained response.
Publisher
Communications Physics
Published On
Apr 20, 2021
Authors
Dylan H. Morris, Fernando W. Rossine, Joshua B. Plotkin, Simon A. Levin
Tags
disease outbreak
SIR model
intervention strategies
peak prevalence
disease control
implementation errors
public health
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