Literature DB >> 35968515

Temporal-spatial risk assessment of COVID-19 under the influence of urban spatial environmental parameters: The case of Shenyang city.

Sui Li1,2, Zhe Li1, Yixin Dong3, Tiemao Shi1,4, Shiwen Zhou1, Yumeng Chen1, Xun Wang5, Feifei Qin2.   

Abstract

Respiratory infection is the main route for the transmission of coronavirus pneumonia, and the results have shown that the urban spatial environment significantly influences the risk of infection. Based on the Wells-Riley model of respiratory infection probability, the study determined the human respiratory-related parameters and the effective influence range; extracted urban morphological parameters, assessed the ventilation effects of different spatial environments, and, combined with population flow monitoring data, constructed a method for assessing the risk of Covid-19 respiratory infection in urban-scale grid cells. In the empirical study in Shenyang city, a severe cold region, urban morphological parameters, population size, background wind speed, and individual behavior patterns were used to calculate the distribution characteristics of temporal and spatial concomitant risks in urban areas grids under different scenarios. The results showed that the correlation between the risk of respiratory infection in urban public spaces and the above variables was significant. The exposure time had the greatest degree of influence on the probability of respiratory infection risk among the variables. At the same time, the change in human body spacing beyond 1 m had a minor influence on the risk of infection. Among the urban morphological parameters, building height had the highest correlation with the risk of infection, while building density had the lowest correlation. The actual point distribution of the epidemic in Shenyang from March to April 2022 was used to verify the evaluation results. The overlap rate between medium or higher risk areas and actual cases was 78.55%. The planning strategies for epidemic prevention and control were proposed for the spatial differentiation characteristics of different risk elements. The research results can accurately classify the risk level of urban space and provide a scientific basis for the planning response of epidemic prevention and control and the safety of public activities. © Tsinghua University Press 2022.

Entities:  

Keywords:  COVID-19; GIS data simulations; epidemic containment planning; infection risk assessment; urban morphological parameters analysis; virus infection rate

Year:  2022        PMID: 35968515      PMCID: PMC9364280          DOI: 10.1007/s12273-022-0918-8

Source DB:  PubMed          Journal:  Build Simul        ISSN: 1996-3599            Impact factor:   4.008


  21 in total

1.  The Impact of Ventilation and Early Diagnosis on Tuberculosis Transmission in Brazilian Prisons.

Authors:  Juliana Urrego; Albert I Ko; Andrea da Silva Santos Carbone; Dayse Sanchez Guimarães Paião; Renata Viebrantz Enne Sgarbi; Catherine W Yeckel; Jason R Andrews; Julio Croda
Journal:  Am J Trop Med Hyg       Date:  2015-07-20       Impact factor: 2.345

2.  Association of the infection probability of COVID-19 with ventilation rates in confined spaces.

Authors:  Hui Dai; Bin Zhao
Journal:  Build Simul       Date:  2020-08-04       Impact factor: 3.751

3.  Modelling the transmission of airborne infections in enclosed spaces.

Authors:  C J Noakes; C B Beggs; P A Sleigh; K G Kerr
Journal:  Epidemiol Infect       Date:  2006-02-14       Impact factor: 2.451

4.  How far droplets can move in indoor environments--revisiting the Wells evaporation-falling curve.

Authors:  X Xie; Y Li; A T Y Chwang; P L Ho; W H Seto
Journal:  Indoor Air       Date:  2007-06       Impact factor: 5.770

5.  Risk of transmission of airborne infection during train commute based on mathematical model.

Authors:  Hiroyuki Furuya
Journal:  Environ Health Prev Med       Date:  2007-03       Impact factor: 3.674

6.  Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil.

Authors:  Pedro S Peixoto; Diego Marcondes; Cláudia Peixoto; Sérgio M Oliva
Journal:  PLoS One       Date:  2020-07-16       Impact factor: 3.240

7.  Human exhalation characterization with the aid of schlieren imaging technique.

Authors:  Chunwen Xu; Peter V Nielsen; Li Liu; Rasmus L Jensen; Guangcai Gong
Journal:  Build Environ       Date:  2016-11-19       Impact factor: 6.456

8.  Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA.

Authors:  Jeon-Young Kang; Alexander Michels; Fangzheng Lyu; Shaohua Wang; Nelson Agbodo; Vincent L Freeman; Shaowen Wang
Journal:  Int J Health Geogr       Date:  2020-09-14       Impact factor: 3.918

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  1 in total

1.  Analysis of COVID-19 clusters involving vertical transmission in residential buildings in Hong Kong.

Authors:  Pengcheng Zhao
Journal:  Build Simul       Date:  2022-08-31       Impact factor: 4.008

  1 in total

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