Evidence map›Paper›PMID 34483431›Full record

ArticleLand use policy2021

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city.

Najmeh Neysani Samany, Ara Toomanian, Ali Maher, Khatereh Hanani, Ali Reza Zali

Abstract read
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Article in Land use policy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Najmeh Neysani SamanyDepartment of GIS & RS, Faculty of Geography, University of Tehran, Iran.
Ara ToomanianDepartment of GIS & RS, Faculty of Geography, University of Tehran, Iran.
Ali MaherSchool of Management and Medical Education, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Khatereh HananiMaster of Statistics, Statistics & Information Technology Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Ali Reza ZaliDepartment of Neurosurgery, School of Medicine, Functional Neurosurgery Research Center Shohada-e-Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0-500] and [500-1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients' financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0-500 m] while these quantities are 0.054088 and 0.902725 for [500-1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients' transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.

Indexed as

COVID-19Financial transactions, land useInfluence zoneMulti-layer perceptron (MLP)

Identifiers

PMID34483431
PMCPMC8403664

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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.