Detecting Spatial Clusters of Disease Infection Risk Using Sparsely Sampled Social Media Mobility Patterns Standard spatial cluster detection methods used in public health surveillance assign each disease case a single location (e.g., patient’s home address), aggregate locations to small areas, and monitor the number of cases in each area over time. However, this approach lacks the accuracy and specificity to deal with infectious disease outbreaks where human mobility plays a key role. Here we propose two new spatial scan methods (the unconditional and conditional spatial logistic models) which search for spatial clusters of increased infection risk in mobility patterns by maximizing a generalized log-likelihood ratio statistic over subsets of the data. The methods correctly account for the multiple, varying number of spatial locations observed per individual, either by non-parametric estimation of the odds of being a case or by matching case and control individuals with similar numbers of observed locations. By applying our methods to synthetic and real-world scenarios, we demonstrate robust performance in detecting spatial clusters of infection risk from mobility data, outperforming competing baselines.