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.
regularization disease mapping privacy spatial epidemiology movement trajectories spatial-temporal data mining