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

Qiushi Chen, Michiko Tsubaki, Yasuhiro Minami, Kazutoshi Fujibayashi, Tetsuro Yumoto, Junzo Kamei, Yuka Yamada, Hidenori Kominato, Hideki Oono and Toshio Naito
Additional contact information
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

IJERPH, 2021, vol. 18, issue 14, 1-32

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.

Keywords: influenza; human/epidemiology; disease outbreaks; machine learning; neural networks; computer; geographic information systems (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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