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

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

To infer epidemiological parameters in an agent-based model, we adapt an iterated filtering (IF) algorithm (Ionides et al., 2006; King et al., 2008; Ionides et al., 2011) . IF can be used to infer the maximum likelihood estimates (MLEs) of parameters in epidemic models and has been successfully applied to infectious diseases such as cholera (King et al., 2008) and measles (He et al., 2010) . Initially developed for ODE models, IF has subsequently been generalized for other model forms (e.g. stochastic models) using the plug-and-play approach (He et al., 2010) . Here, we adapt IF for agentbased models, leveraging an equation-free approach (Kevrekidis et al., 2003) that allows for mapping between the system-level observations (e.g. weekly incidence) used for the IF and the individual-level states evolved in the agent-based model (Appendix 1). In applying the IF, we perform multiple iterations using an efficient Bayesian filtering algorithm -the Ensemble Adjustment Kalman Filter (EAKF) (Anderson, 2001) , which has been widely used in infectious disease forecast and inference (Shaman and Karspeck, 2012; Yang et al., 2015; Pei et al., 2018a; Kandula et al., 2018) . Details of the IF implementation can be found in Materials and methods.