Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus

Pei, Sen; Morone, Flaviano; Liljeros, Fredrik; Makse, Hernán; Shaman, Jeffrey L

In its original implementation, the data assimilation method used in IF is sequential Monte Carlo, or particle filtering (Arulampalam et al., 2002) . Here, due to the high computational cost of the agent-based model, we use a different efficient data assimilation algorithm -the Ensemble Adjustment Kalman Filter (EAKF) (Anderson, 2001) . Unlike particle filtering, which requires a large ensemble size (usually of the order O(10 4 ) or higher) (Snyder et al., 2008) , the EAKF can generate results similar in performance using only hundreds of ensemble members (Shaman and Karspeck, 2012) . Originally developed for use in weather prediction, the EAKF assumes a Gaussian distribution of both the prior and likelihood, and adjusts the prior distribution to a posterior using Bayes rule in a deterministic way such that the first two moments (mean and variance) of an observed variable are adjusted while higher moments remain unchanged during the update (Anderson, 2001) . In epidemiological studies, the EAKF has been widely used for parameter inference and forecast of infectious diseases (Shaman and Karspeck, 2012; Yang et al., 2015; 2018a; Kandula et al., 2018) . The implementation details of the EAKF are introduced in Appendix 1.