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Influence of Absolute Humidity, Temperature and Population Density on COVID-19 Spread and Decay Durations: Multi-Prefecture Study in Japan

Author

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  • Essam A. Rashed

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
    Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt)

  • Sachiko Kodera

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan)

  • Jose Gomez-Tames

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
    Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan)

  • Akimasa Hirata

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
    Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan)

Abstract

This study analyzed the spread and decay durations of the COVID-19 pandemic in different prefectures of Japan. During the pandemic, affordable healthcare was widely available in Japan and the medical system did not suffer a collapse, making accurate comparisons between prefectures possible. For the 16 prefectures included in this study that had daily maximum confirmed cases exceeding ten, the number of daily confirmed cases follow bell-shape or log-normal distribution in most prefectures. A good correlation was observed between the spread and decay durations. However, some exceptions were observed in areas where travelers returned from foreign countries, which were defined as the origins of infection clusters. Excluding these prefectures, the population density was shown to be a major factor, affecting the spread and decay patterns, with R 2 = 0.39 ( p < 0.05) and 0.42 ( p < 0.05), respectively, approximately corresponding to social distancing. The maximum absolute humidity was found to affect the decay duration normalized by the population density ( R 2 > 0.36, p < 0.05). Our findings indicate that the estimated pandemic spread duration, based on the multivariate analysis of maximum absolute humidity, ambient temperature, and population density (adjusted R 2 = 0.53, p -value < 0.05), could prove useful for intervention planning during potential future pandemics, including a second COVID-19 outbreak.

Suggested Citation

  • Essam A. Rashed & Sachiko Kodera & Jose Gomez-Tames & Akimasa Hirata, 2020. "Influence of Absolute Humidity, Temperature and Population Density on COVID-19 Spread and Decay Durations: Multi-Prefecture Study in Japan," IJERPH, MDPI, vol. 17(15), pages 1-14, July.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:15:p:5354-:d:389557
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    References listed on IDEAS

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    1. Behrouz Pirouz & Sina Shaffiee Haghshenas & Behzad Pirouz & Sami Shaffiee Haghshenas & Patrizia Piro, 2020. "Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development," IJERPH, MDPI, vol. 17(8), pages 1-17, April.
    2. Toshiki Kamiya & Ryo Onishi & Sachiko Kodera & Akimasa Hirata, 2019. "Estimation of Time-Course Core Temperature and Water Loss in Realistic Adult and Child Models with Urban Micrometeorology Prediction," IJERPH, MDPI, vol. 16(24), pages 1-15, December.
    3. Sachiko Kodera & Essam A. Rashed & Akimasa Hirata, 2020. "Correlation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidity," IJERPH, MDPI, vol. 17(15), pages 1-14, July.
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    Cited by:

    1. Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    2. Peng Zeng & Zongyao Sun & Yuqi Chen & Zhi Qiao & Liangwa Cai, 2021. "COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    3. Samuel Raine & Amy Liu & Joel Mintz & Waseem Wahood & Kyle Huntley & Farzanna Haffizulla, 2020. "Racial and Ethnic Disparities in COVID-19 Outcomes: Social Determination of Health," IJERPH, MDPI, vol. 17(21), pages 1-16, November.
    4. Sachiko Kodera & Essam A. Rashed & Akimasa Hirata, 2020. "Correlation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidity," IJERPH, MDPI, vol. 17(15), pages 1-14, July.
    5. Essam A. Rashed & Akimasa Hirata, 2021. "Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling," IJERPH, MDPI, vol. 18(15), pages 1-15, July.
    6. Sourya Subhra Nasker & Ananya Nanda & Balamurugan Ramadass & Sasmita Nayak, 2021. "Epidemiological Analysis of SARS-CoV-2 Transmission Dynamics in the State of Odisha, India: A Yearlong Exploratory Data Analysis," IJERPH, MDPI, vol. 18(21), pages 1-13, October.
    7. Woraphon Yamaka & Siritaya Lomwanawong & Darin Magel & Paravee Maneejuk, 2022. "Analysis of the Lockdown Effects on the Economy, Environment, and COVID-19 Spread: Lesson Learnt from a Global Pandemic in 2020," IJERPH, MDPI, vol. 19(19), pages 1-21, October.
    8. Mengyue Yuan & Tong Liu & Chao Yang, 2022. "Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan," IJERPH, MDPI, vol. 19(11), pages 1-22, May.
    9. Jerald M. Velasco & Wei-Chun Tseng & Chia-Lin Chang, 2021. "Factors Affecting the Cases and Deaths of COVID-19 Victims," IJERPH, MDPI, vol. 18(2), pages 1-10, January.
    10. Vallejo-Borda, Jose Agustin & Giesen, Ricardo & Basnak, Paul & Reyes, José P. & Mella Lira, Beatriz & Beck, Matthew J. & Hensher, David A. & Ortúzar, Juan de Dios, 2022. "Characterising public transport shifting to active and private modes in South American capitals during the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 186-205.

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