Variable-Selection-Based Epidemic Disease Diagnosis

Epidemic surveillance in a community involves monitoring infection trend, triggering alarms before outbreaks, and identifying sources and paths of disease transmission. Algorithms for outbreak detection that are derived from industrial statistical process control (SPC) and scan statistics have been reported in the literature, but there are relatively few methods reported for identifying transmission paths. In this work, we propose an expanded spatial-temporal (EST) model for identifying infection sources. Three dimensional information, subject, location, and time, are expanded into a two-dimensional space by dividing the time horizon into segments and multiplying each segment by the locations. Based on the EST model, we further propose a variable-selection algorithm to identify potential location/time combinations as sources of infection, and thus achieve diagnosis. Numerical simulations show that the proposed scheme is effective in locating infection sources.