Adaptive Policies to Balance Health Benefits and Economic Costs of Physical Distancing Interventions during the COVID-19 Pandemic

Yaesoubi, Reza; Havumaki, Joshua; Chitwood, Melanie H.; Menzies, Nicolas A; Gonsalves, Gregg; Salomon, Joshua A.; Paltiel, A. David; Cohen, Ted

doi:10.1177/0272989x21990371

Cited by 14 publications

(15 citation statements)

References 24 publications

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“…However, the optimization did not operate on real-life NPIs, and as such, this approach cannot be directly used by policy-makers. Chen et al 8 created a linear programming tool to explore the trade-off between the expected mortality rate of COVID-19 and return to normal activities, while Yaesoubi et al 9 developed a decision tool to determine when to trigger, continue, or stop physical distancing intervention in order to minimize both the deaths from COVID-19 and intervention duration. Both studies combined the objectives into a single function and the final result was a single intervention plan.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Non-Pharmaceutical Intervention Strategies Against COVID-19 Using Artificial Intelligence

Janko¹,

Reščič²,

Vodopija³

et al. 2021

Preprint

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One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational optimization problem. This paper proposes a framework developed to help policy-makers plan the best combination of non-pharmaceutical interventions and to change them over time. We developed a hybrid machine-learning epidemiological model to forecast the infection trends, aggregated the socio-economic costs from literature and expert knowledge, and used a multi-objective optimization algorithm to find and evaluate various intervention plans. The framework is modular and easily adjustable to a real-world situation, it is trained and tested with data collected from almost all countries of the world, and its proposed intervention plans generally outperform those used in real life in terms of both the number of infections and intervention costs.

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Section: Introductionmentioning

confidence: 99%

Optimizing Non-Pharmaceutical Intervention Strategies Against COVID-19 Using Artificial Intelligence

Janko¹,

Reščič²,

Vodopija³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Deaths were minimized by optimizing the implementation of non-pharmaceutical interventions, such as social distancing and mask mandates. In [172] , the authors developed a decision tool to determine the optimal timing and duration of physical distancing (PD) interventions. The objective is to maximize a measure of control over the pandemic by implementing PD while minimizing the deaths due to COVID-19 and economic costs (which are reflected by the duration of the PD interventions).…”

Section: The Four Framework and Literature Reviewmentioning

confidence: 99%

“…While many studies mention the potential of their models to aid in decision-making processes (e.g., [11] , [94] , [172] , [197] ), few actually fit our definition of a decision support tool. For the purpose of this paper, we define a decision support tool as a model or tool that utilizes formal optimization methods and explicitly suggests optimal action plan(s).…”

Section: The Four Framework and Literature Reviewmentioning

confidence: 99%

“…Decision tools that provide optimal NPI strategies as outputs are another subset to look at. Some of these models have been mentioned in previous sections (e.g., [11] , [154] , [172] ), while [37] demonstrates how an evolutionary surrogate-assisted prescription (ESP) AI model can be used to determine the most optimal intervention strategies. The surrogate model approach enables the generation of a large number of candidate NPI solutions.…”

Section: The Four Framework and Literature Reviewmentioning

confidence: 99%

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Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

et al. 2021

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This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

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“…In this regard, the use of total lockdowns is currently seen as a last resort, backed by studies showing their harm not only to economy [9] but also to mental health [18,13,3]. Generally, efficient strategies can be described as the ones that allow the containment (or total eradication if possible) of the epidemic while maximizing the social, economical and psychological conditions of the citizens [22].…”

Section: Introductionmentioning

confidence: 99%

Testing more and earlier = better control of the epidemic and lower social costs.

Rosselló

Pezzutto

Castagliuolo

et al. 2021

Preprint

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In this note we explore the effect of the number of daily tests on an epidemics control policy purely based on testing and selective quarantine, and the impact of these actions depending on the time their application starts. Surprisingly, the results not only confirm that increasing the number of tests lowers the number of infected individuals, but also that it has a very beneficial effect limiting the number of quarantined individuals, and thus the socio-economical costs of the epidemics. The results also show that the timing in the application of the measures is as important as the measures themselves. The results suggest that fast decision making and investments to increase testing capabilities are highly rewarded not only from the public health viewpoint, but also from the socio-economical one. The study is carried out in simulation using stochastic cellular automata representing a community of 50'000 individuals. The selection of the tested individuals is carried out based on a contact tracing strategy focused on the closer contacts.

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Adaptive Policies to Balance Health Benefits and Economic Costs of Physical Distancing Interventions during the COVID-19 Pandemic

Cited by 14 publications

References 24 publications

Optimizing Non-Pharmaceutical Intervention Strategies Against COVID-19 Using Artificial Intelligence

Optimizing Non-Pharmaceutical Intervention Strategies Against COVID-19 Using Artificial Intelligence

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

Testing more and earlier = better control of the epidemic and lower social costs.

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