ArticleInternational journal of environmental research and public health2021
Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Nurses Lived Experience of Transition From Bedside Nurse to Nurse Manager in Jordan: A Qualitative Study.Journal of nursing management · 2026Article
- COVID-19 Pandemic Risk Assessment: Systematic Review.Risk management and healthcare policy · 2024Review
- Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19.JMIR public health and surveillance · 2023Article
- Article
- Habit-Does It Matter? Bringing Habit and Emotion into the Development of Consumer's Food Waste Reduction Behavior with the Lens of the Theory of Interpersonal Behavior.International journal of environmental research and public health · 2022Article
- Sustainability of Nursing Leadership and Its Contributing Factors in a Developing Economy: A Study in Mongolia.Frontiers in public health · 2022Article
- Stochastic actor-oriented modelling of the impact of COVID-19 on financial network evolution.Stat (International Statistical Institute) · 2021Article
- Modelling Voluntary General Population Vaccination Strategies during COVID-19 Outbreak: Influence of Disease Prevalence.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus disease 2019 (COVID-19) pandemic data. We developed a pandemic space concept that locates different regions so that their connections can be quantified according to the distances between them. A main feature of the pandemic space is to allow visualization of the pandemic status over time through the connectedness between regions. We applied the latent pandemic space model to dynamic pandemic networks constructed using data of confirmed cases of COVID-19 in 164 countries. We observed the ways in which pandemic risk evolves by tracing changes in the locations of countries within the pandemic space. Empirical results gained through this pandemic space analysis can be used to quantify the effectiveness of lockdowns, travel restrictions, and other measures in regard to reducing transmission risk across countries.
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Registered trials
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