A statistical algorithm for outbreak detection in a multi-site setting: the case of sick leave monitoring

Duchemin, T.; Noufaily, A.; Hocine, M. N.

Surveillance for infectious disease outbreak or for other processes should sometimes be implemented simultaneously on multiple sites to detect local events. Sick leave can be monitored accross companies to detect issues such as local outbreaks and identify companies-related issues as local spreading of infectious diseases or bad management practice. In this context, we proposed an adaptation of the Quasi-Poisson regression-based Farrington algorithm for multi-site surveillance. The proposed algorithm consists of a Negative-Binomial mixed effect regression with a new re-weighting procedure to account for past outbreaks and increase sensitivity of the model. We perform a wide range simulations to assess the performance of the model in terms of False Positive Rate and Probability of Detection. We propose an application to sick leave rate in the context of COVID-19. The proposed algorithm provides good overall performance and opens up new opportunities for multi-site data surveillance.