Using Mobile Phone Data to Estimate the Relationship between Population Flow and Influenza Infection Pathways.
Using Mobile Phone Data to Estimate the Relationship between Population Flow and Influenza Infection Pathways.
复制标题
使用手机数据估计人口流动与流感感染途径之间的关系。
DOI：
10.3390/ijerph18147439
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发表时间：
2021-07-12
影响因子：
--
通讯作者：
Naito T
中科院分区：
文献类型：
--
作者：
Chen Q;Tsubaki M;Minami Y;Fujibayashi K;Yumoto T;Kamei J;Yamada Y;Kominato H;Oono H;Naito T
本研究旨在利用全球定位系统（GPS）的位置数据分析人口流动，并通过确定人口流动与药店销售药物数量之间的关系来评估流感感染途径。神经集体图形模型（ncgm; Iwata and Shimizu 2019）应用于大阪、京都、奈良和兵库县的25个单元区域，每个单元面积为10 × 10 km2，以估计人口流动。NCGM使用神经网络来整合时空依赖问题并减少估计参数。数个高种群流动细胞间的处方峰与1 ~ 2天的延迟或7天的滞后高度相关。据观察，在工作日，从一个牢房流向外部地区的人口并不多。这种观察结果可能是由于地理特征和不发达的交通网络。在观察期间，该细胞的抗流感药物处方数量仍然很低。目前的研究结果表明，流感并没有传播到交通网络不发达的地区，而在因通勤而导致的区域间流动较大的地区，药物处方的高峰出现的时间滞后数天。
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 km2, 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.
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影响因子：
5
作者：
Fujibayashi K;Takahashi H;Tanei M;Uehara Y;Yokokawa H;Naito T
通讯作者：
Naito T
影响因子：
1.7
作者：
Goedel, William C.;Regan, Seann D.;Duncan, Dustin T.
通讯作者：
Duncan, Dustin T.
DOI：
10.1073/pnas.0308344101
发表时间：
2004-10-19
影响因子：
11.1
作者：
Hufnagel, L;Brockmann, D;Geisel, T
通讯作者：
Geisel, T
影响因子：
4.6
作者：
Guo P;Zhang J;Wang L;Yang S;Luo G;Deng C;Wen Y;Zhang Q
通讯作者：
Zhang Q