Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

Chen, Meijie; Chen, Yumin; Xu, Yanqing; An, Qianying; Min, Wankun

doi:10.1016/j.scs.2022.104256

Cited by 11 publications

(3 citation statements)

References 45 publications

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“…The above approaches, except for STSS, seem unattractive to the researchers in investigating the mobility pattern. Instead, we found several works that recently relate to this topic, including a population flow-based spatial–temporal eigenvector filtering model by Chen et al (2022) , space–time kernel density estimation by Kato (2021) , and a fuzzy clustering algorithm by Aljeri (2022) . Notably, we have also noticed numerous studies applied the geographically weighted regression model (GWR) and its modifications in analyzing spatiotemporal characteristics of COVID-19 ( Hassaan et al, 2021 , Lak et al, 2021 , Maiti et al, 2021 , Raymundo et al, 2021 ; etc.)…”

Section: Introductionmentioning

confidence: 99%

Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities

Asada

Arimura

2023

Transportation Research Interdisciplinary Perspectives

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Section: Introductionmentioning

confidence: 99%

Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities

Asada

Arimura

2023

Transportation Research Interdisciplinary Perspectives

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“…The reopening policy may expose the public to the risk of new COVID-19 infections 5 . However, due to natural and social factors, the evolutionary characteristics of the epidemic vary in different space 6 .In large cities, the epidemic spreads in a short time due to the concentration of population and mobility of people. In contrast, in remote rural areas, where the population is small and people are not closely connected, it takes a longer time for the virus to spread 7 .…”

Section: Introductionmentioning

confidence: 99%

Assessment of the impact of reopening strategies on the spatial transmission risk of COVID-19: A data-driven transmission model

Wang

Huang

Dong

et al. 2023

Preprint

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COVID-19 has dramatically changed people’s mobility behavior patterns and affected the operations of different functional sites. In the context of the successive reopening of countries around the world since 2022, it is crucial to understand whether the reopening of different types of locations poses a risk of widespread epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the mobile network data provided by Safegraph website, and taking into account the crowd flow characteristics and the changes of susceptible and latent populations, we simulated the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the implementation of reopening strategies. Accordingly, the impact of crowd flow changes under different reopening strategies on the risk of epidemic transmission was judged. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the implementation of the reopening strategy, especially the general dine-in restaurants were at higher risk. Religious activity centers were the points of interest with the highest average infection rates after the implementation of the reopening strategy. Points of interest such as convenience stores, large shopping malls and pharmacies were at a lower risk for outbreak impact after the reopening strategy was implemented. Based on this, reopening prevention and control strategies for different functional points of interest are proposed to provide decision support for the development of precise prevention and control measures for different sites.

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“…The reopening policy may expose the public to the risk of new COVID-19 infections 5 . However, due to natural and social factors, the evolutionary characteristics of the epidemic vary in different spaces 6 . In large cities, the epidemic spreads in a short time due to the concentration of the population and the mobility of people.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the impact of reopening strategies on the spatial transmission risk of COVID-19 based on a data-driven transmission model

Wang

Huang

Dong

et al. 2023

Sci Rep

View full text Add to dashboard Cite

COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots.

show abstract

Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

Cited by 11 publications

References 45 publications

Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities

Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities

Assessment of the impact of reopening strategies on the spatial transmission risk of COVID-19: A data-driven transmission model

Assessment of the impact of reopening strategies on the spatial transmission risk of COVID-19 based on a data-driven transmission model

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