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

arXiv: https://arxiv.org/abs/2003.10218 · HTML: https://mlsanity.com/paper/2003.10218/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 10 of 10)

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- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA](https://mlsanity.com/paper/2509.25459/) (2025)
- [Modelling the COVID-19 virus evolution with Incremental Machine Learning](https://mlsanity.com/paper/2104.09325/) (2021)
- [COVIDHunter: An Accurate, Flexible, and Environment-Aware Open-Source COVID-19 Outbreak Simulation Model](https://mlsanity.com/paper/2102.03667/) (2021)
- [Robust Pandemic Control Synthesis with Formal Specifications: A Case Study on COVID-19 Pandemic](https://mlsanity.com/paper/2103.14262/) (2021)
- [A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.06589/) (2021)
- [ArchABM: an agent-based simulator of human interaction with the built environment. CO2 and viral load analysis for indoor air quality](https://mlsanity.com/paper/2111.01484/) (2021)
- [COVIDHunter: COVID-19 pandemic wave prediction and mitigation via seasonality-aware modeling](https://mlsanity.com/paper/2206.06692/) (2022)
- [Spatio-temporal stochastic graph-based learning for infectious disease forecasting](https://mlsanity.com/paper/2605.30662/) (2026)

## Abstract (arXiv)

There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 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 apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, 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 several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13--14 weeks, when coupled with effective case isolation and international travel restrictions.