Predicting Dynamic Patterns of Short-Term Movement
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, Seneg...
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Published by Oxford University Press on behalf of the World Bank
2021
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| Online Access: | http://hdl.handle.net/10986/36120 |
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okr-10986-361202021-08-17T14:19:44Z Predicting Dynamic Patterns of Short-Term Movement Milusheva, Sveta MOBILITY PREDICTIVE MODELING TELECOMMUNICATIONS DATA PUBLIC POLICY HEALTH POLICY 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. 2021-08-13T20:17:33Z 2021-08-13T20:17:33Z 2020-02 Journal Article World Bank Economic Review 1564-698X http://hdl.handle.net/10986/36120 CC BY-NC-ND 3.0 IGO http://creativecommons.org/licenses/by-nc-nd/3.0/igo World Bank Published by Oxford University Press on behalf of the World Bank Publications & Research Publications & Research :: Journal Article Africa Africa Western and Central (AFW) Senegal |
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Digital Repository |
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Foreign Institution |
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Digital Repositories |
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World Bank Open Knowledge Repository |
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World Bank |
| topic |
MOBILITY PREDICTIVE MODELING TELECOMMUNICATIONS DATA PUBLIC POLICY HEALTH POLICY |
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MOBILITY PREDICTIVE MODELING TELECOMMUNICATIONS DATA PUBLIC POLICY HEALTH POLICY Milusheva, Sveta Predicting Dynamic Patterns of Short-Term Movement |
| geographic_facet |
Africa Africa Western and Central (AFW) Senegal |
| description |
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. |
| format |
Journal Article |
| author |
Milusheva, Sveta |
| author_facet |
Milusheva, Sveta |
| author_sort |
Milusheva, Sveta |
| title |
Predicting Dynamic Patterns of Short-Term Movement |
| title_short |
Predicting Dynamic Patterns of Short-Term Movement |
| title_full |
Predicting Dynamic Patterns of Short-Term Movement |
| title_fullStr |
Predicting Dynamic Patterns of Short-Term Movement |
| title_full_unstemmed |
Predicting Dynamic Patterns of Short-Term Movement |
| title_sort |
predicting dynamic patterns of short-term movement |
| publisher |
Published by Oxford University Press on behalf of the World Bank |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10986/36120 |
| _version_ |
1764484512634372096 |