document: Spatially regularized logistic regression for disease mapping on large moving populations Spatial analysis of disease risk , or disease mapping , typically relies on information about the residence and health status of individuals from population under study . However , residence information has its limitations because people are exposed to numerous disease risks as they spend time outside of their residences . Thanks to the wide-spread use of mobile phones and GPS-enabled devices , it is becoming possible to obtain a detailed record about the movement of human populations . Availability of movement information opens up an opportunity to improve the accuracy of disease mapping . Starting with an assumption that an individual 's disease risk is a weighted average of risks at the locations which were visited , we show that disease mapping can be accomplished by spatially regularized logistic regression . Due to the inherent sparsity of movement data , the proposed approach can be applied to large populations and over large spatial grids . In our experiments , we were able to map disease for a simulated population with 1.6 million people and a spatial grid with 65 thousand locations in several minutes . The results indicate that movement information can improve the accuracy of disease mapping as compared to residential data only . We also studied a privacy-preserving scenario in which only the aggregate statistics are available about the movement of the overall population , while detailed movement information is available only for individuals with disease . The results indicate that the accuracy of disease mapping remains satisfactory when learning from movement data sanitized in this way .
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