A Discrete-Event, Simulated Social Agent-Based Network Transmission (DESSABNeT) model for communicable diseases: Method and validation using SARS-CoV-2 data in three large Australian cities

IMPORTANCE: During pandemics Agent Based Models (ABMs) can model complex, fine-grained behavioural interactions occurring in social networks, that contribute to disease transmission by novel viruses such as SARS-CoV-2. OBJECTIVE: We present a new agent-based model (ABM) called the Discrete-Event, Simulated Social Agent based Network Transmission model (DESSABNeT) and demonstrate its ability to model the spread of COVID-19 in large cities like Sydney, Melbourne and Gold Coast. Our aim was to validate the model with its disease dynamics and underlying social network. DESIGN: DESSABNeT relies on disease transmission within simulated social networks. It employs an epidemiological SEIRD+M (Susceptible, exposed, infected, recovered, died and managed) structure. One hundred simulations were run for each city, with simulated social restrictions closely modelling real restrictions imposed in each location. MAIN OUTCOME(S) AND MEASURE(S): The mean predicted daily incidence of COVID-19 cases were compared to real case incidence data for each city. R(eff) and health service utilisation outputs were compared to the literature, or for the Gold Coast with daily incidence of hospitalisation. RESULTS: DESSABNeT modelled multiple physical distancing restrictions and predicted epidemiological outcomes of Sydney, Melbourne and the Gold Coast, validating this model for future simulation work. CONCLUSIONS AND RELEVANCE: DESSABNeT is a valid platform to model the spread of COVID-19 in large cities in Australia and potentially internationally. The platform is suitable to model different combinations of social restrictions, or to model contact tracing, predict, and plan for, the impact on hospital and ICU admissions, and deaths; and also the rollout of COVID-19 vaccines and optimal social restrictions during vaccination.