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

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

Algorithm 1 only returns the MLEs for the parameters; however, it is also desirable to obtain CIs for those MLEs. For deterministic ODE models, Ionides et al. used 'sliced likelihood' to numerically estimate the Fisher information and standard errors (SEs) of MLEs (Ionides et al., 2006) . Here, for a highly stochastic system, evaluating the Fisher information numerically is challenging. As a result, we took another approach by running multiple realizations of the IF algorithm. In different runs, the MLEs are slightly different due to stochasticity in the agent-based model and in the initialization of the inference algorithm. In this work, we ran 100 independent realizations to generate the average MLEs of inferred parameters and their corresponding 95% CIs. Results from synthetic tests indicate that this approach is effective in calculating MLEs and quantifying their uncertainties.