Short-term human mobility has important health consequences, but measuring short-term movement using survey data is difficult and costly, and use of mobile phone data to study short-term movement is only possible in locations that can access the data. Combining several accessible data sources, Senegal is used as a case study to predict short-term movement within the country. The focus is on two main drivers of movement-economic and social-which explain almost 70 percent of the variation in short-term movement. Comparing real and predicted short-term movement to measure the impact of population movement on the spread of malaria in Senegal, the predictions generated by the model provide estimates for the effect that are not significantly different from the estimates using the real data. Given that the data used in this paper are often accessible in other country settings, this paper demonstrates how predictive modeling can be used by policy makers to estimate short-term mobility.

中文翻译：

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预测短期运动的动态模式。

短期人员流动会对健康产生重要影响，但是使用调查数据来测量短期移动是困难且昂贵的，并且仅在可以访问数据的位置使用手机数据来研究短期移动。塞内加尔结合几个可访问的数据源，用作案例研究来预测该国短期内的流动。重点关注运动的两个主要驱动因素-经济的和社会的，这解释了短期运动中近70％的变化。比较实际和预测的短期流动以衡量塞内加尔人口流动对疟疾蔓延的影响，该模型产生的预测提供的影响估算与使用真实数据的估算没有显着差异。