Modelling transmission and control of the COVID-19 pandemic in Australia

Abstract

In this paper we develop an agent-based model for a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to reproduce several characteristics of COVID19 transmission, accounting for its reproductive number, the length of incubation and generation periods, age- dependent attack rates, and the growth rate of cumulative incidence during a sustained and unmitigated local transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We then apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, social distancing with varying levels of compliance, and school closures. School closures are not found to bring decisive benefits, unless coupled with high level of social distancing compliance. We report an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels. This suggests that a compliance of below 70% is unlikely to succeed for any duration of social distancing, while a compliance at the 90% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions