Disease-dependent interaction policies to support health and economic outcomes during the COVID-19 epidemic

Li, Guanlin; Shivam, Shashwat; Hochberg, Michael; Wardi, Yoraı̈; Weitz, Joshua S.

doi:10.1016/j.isci.2021.102710

Cited by 12 publications

(4 citation statements)

References 43 publications

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“…In the context of infectious disease control, the objective of testing optimization is usually to control the spread of a disease by finding and isolating infectious individuals [9][10][11][12][13] or, less frequently, by finding recovered and immune individuals to end their isolation. 14,15 Put in terms of value of information, the uncertain parameter would be the health statuses of individuals. Yet the decision made once this information is available is typically predefined and fixed: it often simply consists in isolating tested individuals found to be infectious or in following a more sophisticated rule.…”

Section: Passive Uncertainty Management In Public Healthmentioning

confidence: 99%

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions

Flaig¹,

Houy

2023

Med Decis Making

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Background Recent epidemics and measures taken to control them—through vaccination or other actions—have highlighted the role and importance of uncertainty in public health. There is generally a tradeoff between information collection and other uses of resources. Whether this tradeoff is solved explicitly or implicitly, the concept of value of information is central to inform policy makers in an uncertain environment. Method We use a deterministic SIR (susceptible, infectious, recovered) disease emergence and transmission model with vaccination that can be administered as 1 or 2 doses. The disease parameters and vaccine characteristics are uncertain. We study the tradeoffs between information acquisition and 2 other measures: bringing vaccination forward and acquiring more vaccine doses. To do this, we quantify the expected value of perfect information (EVPI) under different constraints faced by public health authorities (i.e., the time of the vaccination campaign implementation and the number of vaccine doses available). Results We discuss the appropriateness of different responses under uncertainty. We show that, in some cases, vaccinating later or with less vaccine doses but more information about the epidemic, and the efficacy of control strategies may bring better results than vaccinating earlier or with more doses and less information, respectively. Conclusion In the present methodological article, we show in an abstract setting how clearly defining and treating the tradeoff between information acquisition and the relaxation of constraints can improve public health decision making. Highlights Uncertainties can seriously hinder epidemic control, but resolving them is costly. Thus, there are tradeoffs between information collection and alternative uses of resources. We use a generic SIR model with vaccination and a value-of-information framework to explore these tradeoffs. We show in which cases vaccinating later with more information about the epidemic and the efficacy of control measures may be better—or not—than vaccinating earlier with less information. We show in which cases vaccinating with fewer vaccine doses and more information about the epidemic and the efficacy of control measures may be better—or not—than vaccinating with more doses and less information.

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Section: Passive Uncertainty Management In Public Healthmentioning

confidence: 99%

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions

Flaig¹,

Houy

2023

Med Decis Making

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“…The adoption of these techniques to vaccine incentive scheduling presents a different set of questions and challenges but can be addressed within a similar modeling framework. Other recent work that makes use of feedback control ideas to develop COVID-19 policies includes [24]. While the merging of reinforcement learning models with optimal/adaptive control theory is a new and promising field with many potential applications, a nice introduction to the field, described mostly in the robotics framework, can be found in a recent monograph [25].…”

Section: Aspects Of Vaccine Policy and Individual Decision Mak-mentioning

confidence: 99%

COVID-19 vaccine incentive scheduling using an optimally controlled reinforcement learning model

Stuckey

Newton

2022

Preprint

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We model Covid-19 vaccine uptake as a reinforcement learning dynamic between two populations: the vaccine adopters, and the vaccine hesitant. Using data available from the Center for Disease Control (CDC), we calculate a payoff matrix governing the dynamic interaction between these two groups and show they are playing a Hawk-Dove evolutionary game with an internal evolutionarily stable Nash equilibrium (the asymptotic percentage of vaccinated in the population). We then ask whether vaccine adoption can be improved by implementing dynamic incentive schedules that reward/punish the vaccine hesitant, and if so, what schedules are optimal and how effective are they likely to be? When is the optimal time to start an incentive program, and how large should the incentives be? By using a tailored replicator dynamic reinforcement learning model together with optimal control theory, we show that well designed and timed incentive programs can improve vaccine uptake by shifting the Nash equilibrium upward in large populations, but only so much, and incentive sizes above a certain threshold show diminishing returns.

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“…It is urgent for governments at different levels to take effective prevention measures to contain the spread of this pandemic. However, different countries take different measures by considering their realities, leading to different performances and social costs (Li et al, 2021). As such, factors affecting pandemic prevention and control have become hot topics that are currently receiving global attention.…”

Section: Introductionmentioning

confidence: 99%

Sustainable social development promotes COVID-19 pandemic control

et al. 2022

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Disease-dependent interaction policies to support health and economic outcomes during the COVID-19 epidemic

Cited by 12 publications

References 43 publications

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions

COVID-19 vaccine incentive scheduling using an optimally controlled reinforcement learning model

Sustainable social development promotes COVID-19 pandemic control

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