Optimal shutdown strategies for COVID-19 with economic and mortality costs: British Columbia as a case study

Barlow, Martin T.; Marshall, Noah; Tyson, Richard V.

doi:10.1098/rsos.202255

Cited by 9 publications

(9 citation statements)

References 38 publications

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“…Finally, we illustrate how feedback-based decision making limits pandemic spread, even as new variants evolve, and can be used to guide reduction of interventions when vaccinations become available. [12]. This open-loop example illustrates the sensitivity of the pandemic to the timing of interventions, and it will be shown that closed-loop feedback control provides a more systematic framework for delivering the desired response.…”

Section: Resultsmentioning

confidence: 93%

Effective pandemic policy design through feedback does not need accurate predictions

Heusden

Gregory²,

Otto³

et al. 2023

PLOS Glob Public Health

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The COVID-19 pandemic has had an enormous toll on human health and well-being and led to major social and economic disruptions. Public health interventions in response to burgeoning case numbers and hospitalizations have repeatedly bent down the epidemic curve, effectively creating a feedback control system. Worst case scenarios have been avoided in many places through this responsive feedback. We aim to formalize and illustrate how to incorporate principles of feedback control into pandemic projections and decision-making, and ultimately shift the focus from prediction to the design of interventions. Starting with an epidemiological model for COVID-19, we illustrate how feedback control can be incorporated into pandemic management using a simple design that couples recent changes in case numbers or hospital occupancy with explicit policy restrictions. We demonstrate robust ability to control a pandemic using a design that responds to hospital cases, despite simulating large uncertainty in reproduction number R0 (range: 1.04-5.18) and average time to hospital admission (range: 4-28 days). We show that shorter delays, responding to case counts versus hospital measured infections, reduce both the cumulative case count and the average level of interventions. Finally, we show that feedback is robust to changing compliance to public health directives and to systemic changes associated with variants of concern and with the introduction of a vaccination program. The negative impact of a pandemic on human health and societal disruption can be reduced by coupling models of disease propagation with models of the decision-making process. In contrast to highly varying open-loop projections, incorporating feedback explicitly in the decision-making process is more reflective of the real-world challenge facing public health decision makers. Using feedback principles, effective control strategies can be designed even if the pandemic characteristics are highly uncertain, encouraging earlier and smaller actions that reduce both case counts and the extent of interventions.

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Section: Resultsmentioning

confidence: 93%

Effective pandemic policy design through feedback does not need accurate predictions

Heusden

Gregory²,

Otto³

et al. 2023

PLOS Glob Public Health

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“…So far, the focus of many epidemiological optimization models has been to determine the optimal level of social distancing needed to minimize infections and the socio-economic costs of interventions [11, 99, 55, 41, 3, 63, 125, 4, 77, 52]. However, as noted in [41] and [54], common modelling formulations can produce highly erroneous results when applied to situations where infection prevalence can be zero in reality (see also considerations listened in Box 1).…”

Section: Discussionmentioning

confidence: 99%

Is SARS-CoV-2 elimination or mitigation best? Regional and disease characteristics determine the recommended strategy

Martignoni,

Arino,

Hurford

2024

Preprint

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Public health responses to the COVID-19 pandemic varied across the world. Some countries (e.g., mainland China, New Zealand, and Taiwan) implemented elimination strategies involving strict travel measures and periods of rigorous non-pharmaceutical interventions (NPIs) in the community, aiming to achieve periods with no disease spread; while others (e.g., many European countries and the United States of America) implemented mitigation strategies involving less strict NPIs for prolonged periods, aiming to limit community spread. Travel measures and community NPIs have high economic and social costs, and there is a need for guidelines that evaluate the appropriateness of an elimination or mitigation strategy in regional contexts. To guide decisions, we identify key criteria and provide indicators and visualizations to help answer each question. Considerations include determining whether disease elimination is: (1) necessary to ensure health care provision; (2) feasible from an epidemiological point of view; and (3) cost effective when considering, in particular, the economic costs of travel measures and treating infections. We discuss our recommendations by considering the regional and economic variability of Canadian provinces and territories, and the epidemiological characteristics of different SARS-CoV-2 variants.

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“…Previous models of COVID-19 spread have analyzed the dynamics of disease spread with the adherence and non-adherence of social behavior protocols such as masking, social distancing, and the enforcement of closures/lock downs [14][15][16][17][18][19][20][21][22][23]. To our knowledge, few models have incorporated the effect of randomized daily testing (although many universities have used this strategy to mitigate disease spread [24][25][26]) with the goal of maximizing in-person time utilizing feedback mechanisms while maintaining a low number of infections.…”

Section: Introductionmentioning

confidence: 99%

Modeling optimal reopening strategies for COVID-19 and its variants by keeping infections low and fixing testing capacity

Dalton

Dougall²,

Darko³

et al. 2022

PLoS ONE

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Since early March 2020, government agencies have utilized a wide variety of non-pharmaceutical interventions to mitigate the spread of COVID-19 and have struggled to determine when it is appropriate to return to in-person activities after an outbreak is detected. At many universities, fundamental issues related to understanding the spread of the disease (e.g. the transmission rate), the ability of administrators to respond quickly enough by closing when there is a sudden rise in cases, and how to make a decision on when to reopen remains a concern. Surveillance testing strategies have been implemented in some places, and those test outcomes have dictated whether to reopen, to simultaneously monitor community spread, and/or to isolate discovered cases. However, the question remains as to when it is safe to reopen and how much testing is required to remain safely open while keeping infection numbers low. Here, we propose an extension of the classic SIR model to investigate reopening strategies for a fixed testing strategy, based on feedback from testing results. Specifically, we close when a predefined proportion of the population becomes infected, and later reopen when that infected proportion decreases below a predefined threshold. A valuable outcome of our approach is that our reopening strategies are robust to variation in almost all model parameters, including transmission rates, which can be extremely difficult to determine as they typically differ between variants, location, vaccination status, etc. Thus, these strategies can be, in theory, translated over to new variants in different regions of the world. Examples of robust feedback strategies for high disease transmission and a fixed testing capacity include (1) a single long lock down followed by a single long in-person period, and (2) multiple shorter lock downs followed by multiple shorter in-person periods. The utility of this approach of having multiple strategies is that administrators of universities, schools, business, etc. can use a strategy that is best adapted for their own functionality.

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Optimal shutdown strategies for COVID-19 with economic and mortality costs: British Columbia as a case study

Cited by 9 publications

References 38 publications

Effective pandemic policy design through feedback does not need accurate predictions

Effective pandemic policy design through feedback does not need accurate predictions

Is SARS-CoV-2 elimination or mitigation best? Regional and disease characteristics determine the recommended strategy

Modeling optimal reopening strategies for COVID-19 and its variants by keeping infections low and fixing testing capacity

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