Tracking clusters of patients over time enables extracting information from medico-administrative databases.

Context: Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity.

Predicting veal-calf trading events in France.

Global trade has been ranked as one of the top five drivers of infectious disease threat events. More specifically, livestock trade is known to increase the speed at which infectious diseases circulate and to facilitate their dissemination over large distances Therefore, predicting animal movements arising from trade is crucial for assessing epidemic risk and the impact of preventive measures. In this study, we developed a statistical framework for predicting trading events using predictors accessible from routinely collected data.

Tracking Temporal Clusters from Patient Networks.

Creating homogeneous groups (clusters) of patients from medico-administrative databases provides a better understanding of health determinants. But in these databases, patients have truncated care pathways. We developed an approach based on patient networks to construct care trajectories from such truncated data.