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An antibiogram is a periodic summary of antibiotic resistance results oforganisms from infected patients to selected antimicrobial drugs. Antibiogramshelp clinicians to understand regional resistance rates and select appropriateantibiotics in prescriptions. In practice, significant combinations ofantibiotic resistance may appear in different antibiograms, forming antibiogrampatterns. Such patterns may imply the prevalence of some infectious diseases incertain regions. Thus it is of crucial importance to monitor antibioticresistance trends and track the spread of multi-drug resistant organisms. Inthis paper, we propose a novel problem of antibiogram pattern prediction thataims to predict which patterns will appear in the future. Despite itsimportance, tackling this problem encounters a series of challenges and has notyet been explored in the literature. First of all, antibiogram patterns are noti.i.d as they may have strong relations with each other due to genomicsimilarities of the underlying organisms. Second, antibiogram patterns areoften temporally dependent on the ones that are previously detected.Furthermore, the spread of antibiotic resistance can be significantlyinfluenced by nearby or similar regions. To address the above challenges, wepropose a novel Spatial-Temporal Antibiogram Pattern Prediction framework,STAPP, that can effectively leverage the pattern correlations and exploit thetemporal and spatial information. We conduct extensive experiments on areal-world dataset with antibiogram reports of patients from 1999 to 2012 for203 cities in the United States. The experimental results show the superiorityof STAPP against several competitive baselines.

Spatial-Temporal Networks for Antibiogram Pattern Prediction