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

Abstract

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

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

“…The referred studies do not match the forest plot proposed synthesis (predicted vs. the actual number of cumulative COVID-19-positive cases) (Abdallah et al. 2022 ; Alali et al. 2022 ; Alamrouni et al.…”

“… GQ1, GQ2, SQ1, SQ3 Abdallah et al. ( 2022 ) Tunisia Propose a new strategy based on reinforcement learning (RL) to study the dynamics of COVID-19 evolution GQ1, GQ2, SQ1, SQ3 Ahouz and Golabpour ( 2021 ) Iran Predict the incidence of COVID-19 within two weeks to better manage the disease using the Least-Square Boosting Classification algorithm. GQ1, SQ1, SQ4 Alali et al.…”

“…The following studies presented some level of concern (13 of 45 included studies) (Abdallah et al. 2022 ; Alamrouni et al. 2022 ; Bi et al.…”