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.