tetano
Editor, Senior Moderator
Build Environ
. 2022 May 4;109153.
doi: 10.1016/j.buildenv.2022.109153. Online ahead of print.
Assessing personal exposure to COVID-19 transmission in public indoor spaces based on fine-grained trajectory data: A simulation study
Pengfei Chen[SUP] 1 2 [/SUP], Dongchu Zhang[SUP] 1 [/SUP], Jianxiao Liu[SUP] 3 [/SUP], Izzy Yi Jian[SUP] 4 [/SUP]
Affiliations
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to worldwide health systems in quick response to epidemics. The assessment of personal exposure to COVID-19 in enclosed spaces is critical to identifying potential infectees and preventing outbreaks. However, traditional contact tracing methods rely heavily on a manual interview, which is costly and time consuming given the large population involved. With advanced indoor localisation techniques, it is possible to collect people's footprints accurately by locating their smartphones. This study presents a new framework for the assessment of personal exposure to COVID-19 carriers using their fine-grained trajectory data. An integral model was established to quantify the exposure risk, in which the spatial and temporal decay effects are simultaneously considered when modelling the airborne transmission of COVID-19. Regarding the obstacle effect of the indoor layout on airborne transmission, a weight graph based on the space syntax technique was further introduced to constrain the transmission strength between subspaces that are less inter-visible. The proposed framework was demonstrated by a simulation study, in which external comparison and internal analysis were conducted to justify its validity and robustness in different scenarios. Our method is expected to promote the efficient identification of potential infectees and provide an extensible spatial-temporal model to simulate different control measures and examine their effectiveness in a built environment.
Keywords: COVID-19; Contact tracing; Space syntax; Spatio-temporal model; Visibility analysis.
. 2022 May 4;109153.
doi: 10.1016/j.buildenv.2022.109153. Online ahead of print.
Assessing personal exposure to COVID-19 transmission in public indoor spaces based on fine-grained trajectory data: A simulation study
Pengfei Chen[SUP] 1 2 [/SUP], Dongchu Zhang[SUP] 1 [/SUP], Jianxiao Liu[SUP] 3 [/SUP], Izzy Yi Jian[SUP] 4 [/SUP]
Affiliations
- PMID: 35531051
- PMCID: PMC9066746
- DOI: 10.1016/j.buildenv.2022.109153
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to worldwide health systems in quick response to epidemics. The assessment of personal exposure to COVID-19 in enclosed spaces is critical to identifying potential infectees and preventing outbreaks. However, traditional contact tracing methods rely heavily on a manual interview, which is costly and time consuming given the large population involved. With advanced indoor localisation techniques, it is possible to collect people's footprints accurately by locating their smartphones. This study presents a new framework for the assessment of personal exposure to COVID-19 carriers using their fine-grained trajectory data. An integral model was established to quantify the exposure risk, in which the spatial and temporal decay effects are simultaneously considered when modelling the airborne transmission of COVID-19. Regarding the obstacle effect of the indoor layout on airborne transmission, a weight graph based on the space syntax technique was further introduced to constrain the transmission strength between subspaces that are less inter-visible. The proposed framework was demonstrated by a simulation study, in which external comparison and internal analysis were conducted to justify its validity and robustness in different scenarios. Our method is expected to promote the efficient identification of potential infectees and provide an extensible spatial-temporal model to simulate different control measures and examine their effectiveness in a built environment.
Keywords: COVID-19; Contact tracing; Space syntax; Spatio-temporal model; Visibility analysis.