Reinforcement learning-based decision support system for COVID-19

Padmanabhan, Regina; Meskin, Nader; Khattab, Tamer; Shraim, Mujahed; Al‐Hitmi, Mohammed

doi:10.1016/j.bspc.2021.102676

Cited by 26 publications

(14 citation statements)

References 35 publications

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“…Apart from the use of control strategies for deriving active intervention protocols, artificial intelligence, and digital methods are used worldwide in many application such as symptom detectors, X-ray image analysis, AI-based intelligent robot assistance for sanitizing, lifting or transporting infected peoples, lockdown patrol, human activity or interaction detection, hospital triage, blood-sample collection, to name some [40] , [46] , [86] , [106] , [108] . However, AI-based techniques for deriving effective control measures for mitigating the spread is scarce [96] . In [96] , a

-learning-based model-free closed-loop controller that accounts for cost and hospital saturation constraints related to COVID-19 mitigation is discussed.…”

Section: Discussionmentioning

confidence: 99%

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A review of mathematical model-based scenario analysis and interventions for COVID-19

Padmanabhan¹,

Abed²,

Meskin³

et al. 2021

Computer Methods and Programs in Biomedicine

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Mathematical model-based analysis has proven its potential as a critical tool in the battle against COVID-19 by enabling better understanding of the disease transmission dynamics, deeper analysis of the cost-effectiveness of various scenarios, and more accurate forecast of the trends with and without interventions. However, due to the outpouring of information and disparity between reported mathematical models, there exists a need for a more concise and unified discussion pertaining to the mathematical modeling of COVID-19 to overcome related skepticism. Towards this goal, this paper presents a review of mathematical model-based scenario analysis and interventions for COVID-19 with the main objectives of (1) including a brief overview of the existing reviews on mathematical models, (2) providing an integrated framework to unify models, (3) investigating various mitigation strategies and model parameters that reflect the effect of interventions, (4) discussing different mathematical models used to conduct scenario-based analysis, and (5) surveying active control methods used to combat COVID-19.

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-learning-based model-free closed-loop controller that accounts for cost and hospital saturation constraints related to COVID-19 mitigation is discussed.…”

Section: Discussionmentioning

confidence: 99%

“…However, AI-based techniques for deriving effective control measures for mitigating the spread is scarce [96] . In [96] , a

-learning-based model-free closed-loop controller that accounts for cost and hospital saturation constraints related to COVID-19 mitigation is discussed.…”

Section: Discussionmentioning

confidence: 99%

A review of mathematical model-based scenario analysis and interventions for COVID-19

Padmanabhan¹,

Abed²,

Meskin³

et al. 2021

Computer Methods and Programs in Biomedicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…• those that retrospectively evaluate the effects of NPIs (26, 27, 31-34, 40, 48, 49, 56, 60, 97, 98), • those that make forecasts on the effects of a specified NPI in the sense of scenario planning (26, 31, 35, 38, 50-55, 58, 96), • and those that develop methods for optimal control policy identification (59,(100)(101)(102)(103)(104).…”

Section: Planning and Evaluating Npismentioning

confidence: 99%

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

Botz

Wang

Lambert³

et al. 2022

Front. Public Health

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The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

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“…Padmanabhan et al 24 proposed a decision support system capable of incorporating different interventions to minimize the impact of widespread respiratory infectious pandemics, including the recent COVID‐19. They took into account pandemic characteristics, health system parameters and socio‐economic aspects.…”

Section: Related Workmentioning

confidence: 99%

A deep reinforcement learning based decision‐making approach for avoiding crowd situation within the case of Covid'19 pandemic

Abdallah

Kanzari

Sallami

et al. 2022

Computational Intelligence

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Individuals' flow's fluidifcation in the same way as the thinning of the population's concentration remains among major concerns within the context of the pandemic crisis situations. The recent COVID-19 pandemic crisis is a typical example of the aforementioned where on despite of the containment phases that radically isolate the population but are not applicable persistently, people have to adapt their behavior to new daily-life situations tempering Individuals' stream, avoiding tides, and watering down population's concentration. Crowd evacuation is one of the well-known research domains that can play a pertinent role to face the challenge of the COVID-19 pandemic. In fact, considering the population's concentration thinning within the slant of the "crowd evacuation" paradigm allows managing the flow of the population, and consequently, decreasing the probable number of infected cases. In other words, crowd evacuation modeling and simulation with the aim of better-exploiting individuals' flow allow the study and analysis of different possible outcomes for designing population's concentration thinning strategies. In this article, a new decision-making approach is proposed in order to cope with the aforesaid challenges, which relies on an independent Deep Q Network with an improved SIR model (IDQN-I-SIR). The 416

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Reinforcement learning-based decision support system for COVID-19

Cited by 26 publications

References 35 publications

A review of mathematical model-based scenario analysis and interventions for COVID-19

A review of mathematical model-based scenario analysis and interventions for COVID-19

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

A deep reinforcement learning based decision‐making approach for avoiding crowd situation within the case of Covid'19 pandemic

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