System Engineering and Overshoot Damping for Epidemics Such As COVID-19

Abstract

Like the trajectory of a rocket whose landing point is unpredictable unless it is steered, an epidemic takes a trajectory highly dependent on human behavior. To control the threat of a pandemic, a city or region must also be viewed as a system with certain capacities and constraints. The goal of this paper is to contribute the perspective of a systems engineer to the effort to fight pandemics. The availability of low latency case data and effectiveness of social distancing suggest sufficient control authority is for successful smoothing and targeting almost any desired level of low or high cases and immunity. We examine multi-step and intermittent-with-feedback partial unlock of social distancing for rapidly-spreading moderate-mortality epidemics and pandemics similar to COVID-19. Optimized scenarios reduce total cases and therefore deaths typically 8% and up to 30% by controlling overshoot as groups cross the herd immunity threshold, or lower thresholds to manage medical resources and provide economic relief. We analyze overshoot and provide guidance on how to damp it. An SIR model is used to evaluate scenarios that are intended to function over a wide variety of parameters. The end result is not a case trajectory prediction, but a prediction of which strategies produce near-optimal results over a wide range of epidemiological and social parameters.