  An antibiogram is a periodic summary of antibiotic resistance results of
organisms from infected patients to selected antimicrobial drugs. Antibiograms
help clinicians to understand regional resistance rates and select appropriate
antibiotics in prescriptions. In practice, significant combinations of
antibiotic resistance may appear in different antibiograms, forming antibiogram
patterns. Such patterns may imply the prevalence of some infectious diseases in
certain regions. Thus it is of crucial importance to monitor antibiotic
resistance trends and track the spread of multi-drug resistant organisms. In
this paper, we propose a novel problem of antibiogram pattern prediction that
aims to predict which patterns will appear in the future. Despite its
importance, tackling this problem encounters a series of challenges and has not
yet been explored in the literature. First of all, antibiogram patterns are not
i.i.d as they may have strong relations with each other due to genomic
similarities of the underlying organisms. Second, antibiogram patterns are
often temporally dependent on the ones that are previously detected.
Furthermore, the spread of antibiotic resistance can be significantly
influenced by nearby or similar regions. To address the above challenges, we
propose a novel Spatial-Temporal Antibiogram Pattern Prediction framework,
STAPP, that can effectively leverage the pattern correlations and exploit the
temporal and spatial information. We conduct extensive experiments on a
real-world dataset with antibiogram reports of patients from 1999 to 2012 for
203 cities in the United States. The experimental results show the superiority
of STAPP against several competitive baselines.
