Research on the Relationships Between Population Flow Based on Global Positioning System Location Information from Mobile Phone Networks and Influenza Infection Pathways Based on the Number of Anti-Influenza Drug Prescriptions at Pharmacies: A Pilot Study (Preprint)

BACKGROUND Global seasonal influenza-associated respiratory excess mortality rates have been estimated at 4-8.8 per 100,000 individuals, and this is one of the major issues in public health. Designing efficient containment strategies for highly contagious diseases like influenza has been a subject of very considerable interest recently. Infectious disease epidemic tracking and forecasting have recently been attempted using data based on mobile phone global positioning system (GPS) location information. Tracking and forecasting local influenza spread may contribute to the control of influenza epidemics in an early stage. OBJECTIVE The objectives of this research were to analyze population flow using GPS location data based on the methods proposed by Iwata and Shimizu (2019), and to evaluate influenza infection pathways by determining the relationship between population flow and the number of drugs sold at pharmacies. METHODS Methods proposed by Iwata and Shimizu were applied for all 25 cells to estimate population flow. They proposed a neural collective graphical model (NCGM), which uses a neural network to incorporate the spatiotemporal dependency issue and reduce the estimated parameter. RESULTS The prescription peaks in cells 12 and 14, which had high population flows with cell 13, showed a high correlation with a delay of one to two days. The incubation period is one to four days (average two days) in seasonal influenza. One feature around cell 6 is the low number of prescriptions for anti-influenza drugs. The influenza infection may not have spread to cell 6 due to the low population flow from cells 12 and 13 with high prescriptions. Another feature is the observation of transmission of infection by a small number of influenza patients. In cells 5 and 6 where high population flows were suspected, there was a high cross-correlation value of prescription numbers with a seven-day time-lag. The time-lag is longer than the time-lag observed around cell 13 above. It was observed that not much population flows from cell 19 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 cell 19 remained low during the observation period. CONCLUSIONS This study conducted population flow estimation analyses during commuting times, based on region-specific GPS location data in four Prefectures in the Kansai region of Japan using methods proposed by Iwata and Shimizu. Furthermore, detailed comparative analyses of the relationship between estimated results of population flow and anti-influenza drug prescription data from pharmacies were conducted. It was found 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.