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Using Mobile Phone Data to Estimate the Relationship between Population Flow and Influenza Infection Pathways

Author

Listed:
  • Qiushi Chen

    (Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan)

  • Michiko Tsubaki

    (Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan)

  • Yasuhiro Minami

    (Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan)

  • Kazutoshi Fujibayashi

    (Department of General Medicine, Juntendo University Faculty of Medicine, 3-1-3 Hongo, Bunkyo-Ku, Tokyo 113-8421, Japan)

  • Tetsuro Yumoto

    (Division of Pharmacy Professional Development and Research, Hoshi University, 2-4-41 Ebara, Shinagawa-Ku, Tokyo 142-8501, Japan)

  • Junzo Kamei

    (Division of Pharmacy Professional Development and Research, Hoshi University, 2-4-41 Ebara, Shinagawa-Ku, Tokyo 142-8501, Japan)

  • Yuka Yamada

    (I&H Corporation, 1-18, Oomasu, Ashiya, Hyogo 659-0066, Japan)

  • Hidenori Kominato

    (I&H Corporation, 1-18, Oomasu, Ashiya, Hyogo 659-0066, Japan)

  • Hideki Oono

    (I&H Corporation, 1-18, Oomasu, Ashiya, Hyogo 659-0066, Japan)

  • Toshio Naito

    (Department of General Medicine, Juntendo University Faculty of Medicine, 3-1-3 Hongo, Bunkyo-Ku, Tokyo 113-8421, Japan)

Abstract

This study aimed to analyze population flow using global positioning system (GPS) location data and evaluate influenza infection pathways by determining the relationship between population flow and the number of drugs sold at pharmacies. Neural collective graphical models (NCGMs; Iwata and Shimizu 2019) were applied for 25 cell areas, each measuring 10 × 10 km 2 , in Osaka, Kyoto, Nara, and Hyogo prefectures to estimate population flow. An NCGM uses a neural network to incorporate the spatiotemporal dependency issue and reduce the estimated parameters. The prescription peaks between several cells with high population flow showed a high correlation with a delay of one to two days or with a seven-day time-lag. It was observed that not much population flows from one cell to the outside area on weekdays. This observation may have been due to geographical features and undeveloped transportation networks. The number of prescriptions for anti-influenza drugs in that cell remained low during the observation period. The present results indicate that influenza did not spread to areas with undeveloped traffic networks, and the peak number of drug prescriptions arrived with a time lag of several days in areas with a high amount of area-to-area movement due to commuting.

Suggested Citation

  • Qiushi Chen & Michiko Tsubaki & Yasuhiro Minami & Kazutoshi Fujibayashi & Tetsuro Yumoto & Junzo Kamei & Yuka Yamada & Hidenori Kominato & Hideki Oono & Toshio Naito, 2021. "Using Mobile Phone Data to Estimate the Relationship between Population Flow and Influenza Infection Pathways," IJERPH, MDPI, vol. 18(14), pages 1-32, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7439-:d:592822
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    References listed on IDEAS

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    2. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    3. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
    4. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
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