tetano
Editor, Senior Moderator
Int J Environ Res Public Health
. 2021 Jul 12;18(14):7439.
doi: 10.3390/ijerph18147439.
Using Mobile Phone Data to Estimate the Relationship between Population Flow and Influenza Infection Pathways
Qiushi Chen[SUP] 1 [/SUP], Michiko Tsubaki[SUP] 1 [/SUP], Yasuhiro Minami[SUP] 1 [/SUP], Kazutoshi Fujibayashi[SUP] 2 [/SUP], Tetsuro Yumoto[SUP] 3 [/SUP], Junzo Kamei[SUP] 3 [/SUP], Yuka Yamada[SUP] 4 [/SUP], Hidenori Kominato[SUP] 4 [/SUP], Hideki Oono[SUP] 4 [/SUP], Toshio Naito[SUP] 2 [/SUP]
Affiliations
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[SUP]2[/SUP], 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: computer; disease outbreaks; geographic information systems; human/epidemiology; influenza; machine learning; neural networks.
. 2021 Jul 12;18(14):7439.
doi: 10.3390/ijerph18147439.
Using Mobile Phone Data to Estimate the Relationship between Population Flow and Influenza Infection Pathways
Qiushi Chen[SUP] 1 [/SUP], Michiko Tsubaki[SUP] 1 [/SUP], Yasuhiro Minami[SUP] 1 [/SUP], Kazutoshi Fujibayashi[SUP] 2 [/SUP], Tetsuro Yumoto[SUP] 3 [/SUP], Junzo Kamei[SUP] 3 [/SUP], Yuka Yamada[SUP] 4 [/SUP], Hidenori Kominato[SUP] 4 [/SUP], Hideki Oono[SUP] 4 [/SUP], Toshio Naito[SUP] 2 [/SUP]
Affiliations
- PMID: 34299889
- DOI: 10.3390/ijerph18147439
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[SUP]2[/SUP], 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: computer; disease outbreaks; geographic information systems; human/epidemiology; influenza; machine learning; neural networks.