ArticleComputational intelligence2022
A deep reinforcement learning based decision-making approach for avoiding crowd situation within the case of Covid'19 pandemic.
Article in Computational intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- The coming Omicron waves and factors affecting its spread after China reopening borders.BMC medical informatics and decision making · 2023Article
- A deep reinforcement learning based decision-making approach for avoiding crowd situation within the case of Covid'19 pandemic.Computational intelligence · 2022Article
- Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics.Network modeling and analysis in health informatics and bioinformatics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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 machine-learning component (i.e., IDQN) is in charge of the agent's movements control and I-SIR (improved "susceptible-infected-recovered" individuals) model is responsible to control the virus spread. We demonstrate the effectiveness of IDQN-I-SIR through a case-study of individuals' flow's management with infected cases' avoidance in an emergency department (often overcrowded in context of a pandemic crisis).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.