Modelling SARS‐CoV‐2 disease progression in Australia and New Zealand: an account of an agent‐based approach to support public health decision‐making

OBJECTIVE: In 2020, we developed a public health decision‐support model for mitigating the spread of SARS‐CoV‐2 infections in Australia and New Zealand. Having demonstrated its capacity to describe disease progression patterns during both countries’ first waves of infections, we describe its utilisation in Victoria in underpinning the State Government's then ‘RoadMap to Reopening’. METHODS: Key aspects of population demographics, disease, spatial and behavioural dynamics, as well as the mechanism, timing, and effect of non‐pharmaceutical public health policies responses on the transmission of SARS‐CoV‐2 in both countries were represented in an agent‐based model. We considered scenarios related to the imposition and removal of non‐pharmaceutical interventions on the estimated progression of SARS‐CoV‐2 infections. RESULTS: Wave 1 results suggested elimination of community transmission of SARS‐CoV‐2 was possible in both countries given sustained public adherence to social restrictions beyond 60 days’ duration. However, under scenarios of decaying adherence to restrictions, a second wave of infections (Wave 2) was predicted in Australia. In Victoria’s second wave, we estimated in early September 2020 that a rolling 14‐day average of <5 new cases per day was achievable on or around 26 October. Victoria recorded a 14‐day rolling average of 4.6 cases per day on 25 October. CONCLUSIONS: Elimination of SARS‐CoV‐2 transmission represented in faithfully constructed agent‐based models can be replicated in the real world. IMPLICATIONS FOR PUBLIC HEALTH: Agent‐based public health policy models can be helpful to support decision‐making in novel and complex unfolding public health crises.