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 there is sufficient control for successful smoothing and targeting almost any desired level of low or high cases and immunity. This control proceeds from spontaneous public reaction to caseloads and news as well as government mediated recommendations and orders. We simulate 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. However, we find overshoot damping, whether from expert planning or natural public self-isolation, increases the likelihood of transition to an endemic disease. 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. Overshoot damping perversely increases the chance a pathogen will transition to an endemic disease, so we briefly describe the undershoot conditions that promote transition to endemic status.

This article addresses the generic dynamic decision problem of how to achieve sustained market growth by increasing two interdependent organizational resources needed (1) to increase and (2) to sustain demand. The speed and costs of increasing each resource are different. Failure to account for this difference has been reported to lead to policies that drive a quick increase of demand followed by decline. Three generic policies derived from the literature have been implemented in a system dynamics model. Simulation shows that all three policies can generate sustained exponential growth but differ in performance. These results suggest that even policies which risk generating overshoot and collapse can avoid it. This poses two questions for further research: (1) what is the reasoning of human decision-makers when choosing between these policies and (2) how can the important but easily overlooked features of such decision situations be made sufficiently salient to be accounted for?

Background: A large percentage of deaths in an epidemic or pandemic can be due to overshoot of population (herd) immunity, either from the initial peak or from planned or unplanned exit from lockdown or social distancing conditions. Objectives: We study partial unlock or reopening interaction with seasonal effects in a managed epidemic to quantify overshoot effects on small and large unlock steps and discover robust strategies for reducing overshoot. Methods: We simulate partial unlock of social distancing for epidemics over a range of replication factor, immunity duration and seasonality factor for strategies targeting immunity thresholds using overshoot optimization. Results: Seasonality change must be taken into account as one of the steps in an easing sequence, and a two step unlock, including seasonal effects, minimizes overshoot and deaths. It may cause undershoot, which causes rebounds and assists survival of the pathogen. Conclusions: Partial easing levels, even low levels for economic relief while waiting on a vaccine, have population immunity thresholds based on the reduced replication rates and may experience overshoot as well. We further find a two step strategy remains highly sensitive to variations in case ratio, replication factor, seasonality and timing. We demonstrate a three or more step strategy is more robust, and conclude that the best possible approach minimizes deaths under a range of likely actual conditions which include public response.