ArticleFrontiers in public health2023
Night-time population consistently explains the transmission dynamics of coronavirus disease 2019 in three megacities in Japan.
Article in Frontiers in public health, 2023. 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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4 citing papers in PubMed.
- Population shifts during the reconstruction period in areas marked as evacuation zones after the Fukushima Daiichi nuclear power plant accident: a mobile spatial statistics data-based time-series clustering analysis.Journal of radiation research · 2024Article
- Enhancing healthcare planning using population data generated from mobile phone networks in Futaba County after the Great East Japan earthquake.Scientific reports · 2024Article
- Local effects of non-pharmaceutical interventions on mitigation of COVID-19 spread through decreased human mobilities in Japan: a prefecture-level mediation analysis.Scientific reports · 2024Article
- Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan.Pathogens (Basel, Switzerland) · 2023Article
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5 authors.
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Abstract
Background: Mobility data are crucial for understanding the dynamics of coronavirus disease 2019 (COVID-19), but the consistency of the usefulness of these data over time has been questioned. The present study aimed to reveal the relationship between the transmissibility of COVID-19 in Tokyo, Osaka, and Aichi prefectures and the daily night-time population in metropolitan areas belonging to each prefecture. Methods: In Japan, the Results: In the time-varying regression analysis, night-time population level tended to show positive to neutral effects on COVID-19 transmission, whereas the daily change of night-time population showed neutral to negative effects. The fixed-effect regression analysis revealed that for Tokyo and Osaka, regression models with 8-day-lagged night-time population level and daily change were the best fit, whereas in Aichi, the model using only the 9-day-lagged night-time population level was the best fit using the widely applicable information criterion. For all regions, the best-fit model suggested a positive relationship between night-time population and transmissibility, which was maintained over time. Conclusion: Our results revealed that, regardless of the period of interest, a positive relationship between night-time population levels and COVID-19 dynamics was observed. The introduction of vaccinations and major outbreaks of Omicron BA. Two subvariants in Japan did not dramatically change the relationship between night-time population and COVID-19 dynamics in three megacities in Japan. Monitoring the night-time population continues to be crucial for understanding and forecasting the short-term future of COVID-19 incidence.
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