Reinforced Epidemic Control: Saving Both Lives and Economy

Song, Sirui; Zong, Zefang; Li, Yong; Xue, Liu; Yu, Yang

doi:10.48550/arxiv.2008.01257

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…In the epidemic field, GNNs have been employed for the prediction of disease prevalence [35][36][37], identification of patient zero [38], and estimation of epidemic state using limited information [39]. Few studies have developed dynamic epidemic control schemes that identify epidemic hotspots from partially observed epidemic state of each individual [40,41].…”

Section: Introductionmentioning

confidence: 99%

Effective vaccination strategy using graph neural network ansatz

Jhun¹

2021

Preprint

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Section: Introductionmentioning

confidence: 99%

Effective vaccination strategy using graph neural network ansatz

Jhun¹

2021

Preprint

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“…Duque et al 2020). RL has been applied previously to several mass-action models (Libin et al 2020;Song et al 2020). These models, however, do not take into account individual behaviors or any complex interaction patterns.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Optimization of COVID-19 Mitigation policies

Kompella,

Capobianco,

Jong

et al. 2020

Preprint

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The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world are faced with the challenge of protecting public health, while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, the even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) can be used to optimize mitigation policies that minimize the economic impact without overwhelming the hospital capacity. Our main contributions are (1) a novel agentbased pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; and (2) an RL-based methodology for optimizing fine-grained mitigation policies within this simulator. Our results validate both the overall simulator behavior and the learned policies under realistic conditions. Related WorkEpidemiological models differ based on the level of granularity in which they track individuals and their disease states.

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“…Recently, DRL has also been applied to improve sequential decision making during the pandemic. [20][21][22][23] For example, Song et al 24 developed a reinforcement learning (RL)based dynamic control algorithm on interregional mobility during COVID-19. Kompella et al 25 applied RL to a virtual agent-based simulator to search for the optimal policy, but the scale is limited too.…”

mentioning

confidence: 99%

PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning

et al. 2022

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Background Policy makers are facing more complicated challenges to balance saving lives and economic development in the post-vaccination era during a pandemic. Epidemic simulation models and pandemic control methods are designed to tackle this problem. However, most of the existing approaches cannot be applied to real-world cases due to the lack of adaptability to new scenarios and micro representational ability (especially for system dynamics models), the huge computation demand, and the inefficient use of historical information. Methods We propose a novel Pandemic Control decision making framework via large-scale Agent-based modeling and deep Reinforcement learning (PaCAR) to search optimal control policies that can simultaneously minimize the spread of infection and the government restrictions. In the framework, we develop a new large-scale agent-based simulator with vaccine settings implemented to be calibrated and serve as a realistic environment for a city or a state. We also design a novel reinforcement learning architecture applicable to the pandemic control problem, with a reward carefully designed by the net monetary benefit framework and a sequence learning network to extract information from the sequential epidemiological observations, such as number of cases, vaccination, and so forth. Results Our approach outperforms the baselines designed by experts or adopted by real-world governments and is flexible in dealing with different variants, such as Alpha and Delta in COVID-19. PaCAR succeeds in controlling the pandemic with the lowest economic costs and relatively short epidemic duration and few cases. We further conduct extensive experiments to analyze the reasoning behind the resulting policy sequence and try to conclude this as an informative reference for policy makers in the post-vaccination era of COVID-19 and beyond. Limitations The modeling of economic costs, which are directly estimated by the level of government restrictions, is rather simple. This article mainly focuses on several specific control methods and single-wave pandemic control. Conclusions The proposed framework PaCAR can offer adaptive pandemic control recommendations on different variants and population sizes. Intelligent pandemic control empowered by artificial intelligence may help us make it through the current COVID-19 and other possible pandemics in the future with less cost both of lives and economy. Highlights We introduce a new efficient, large-scale agent-based epidemic simulator in our framework PaCAR, which can be applied to train reinforcement learning networks in a real-world scenario with a population of more than 10,000,000. We develop a novel learning mechanism in PaCAR, which augments reinforcement learning with sequence learning, to learn the tradeoff policy decision of saving lives and economic development in the post-vaccination era. We demonstrate that the policy learned by PaCAR outperforms different benchmark policies under various reality conditions during COVID-19. We analyze the resulting policy given by PaCAR, and the lessons may shed light on better pandemic preparedness plans in the future.

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Reinforced Epidemic Control: Saving Both Lives and Economy

Cited by 4 publications

References 23 publications

Effective vaccination strategy using graph neural network ansatz

Effective vaccination strategy using graph neural network ansatz

Reinforcement Learning for Optimization of COVID-19 Mitigation policies

PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning

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