Modelling transmission and control of the COVID-19 pandemic in Australia
By S. Chang, Nathan Harding, C. Zachreson, Oliver M. Cliff, Mikhail Prokopenko. Nature Communications, 2020
565 citations recorded by the source corpus.
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. There is ongoing debate about the effective combination of strategies for COIVD-19 control. Here, the authors use an agent-based model to quantify and compare several intervention strategies, and identify minimal levels of social distancing compliance required to control the epidemic in Australia.
Subfields: Clinical Psychology, Modeling and Simulation, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/5583c03f10e228394a01b1e3111956f0345e9311
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Citations in the corpus
- High Boredom Proneness and Low Trait Self-Control Impair Adherence to Social Distancing Guidelines during the COVID-19 Pandemic (2020, background): “Mathematical modeling shows that the effectiveness of social distancing measures can be severely undermined if a (small) proportion of the population does not adhere to them [34].”
- COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions (2020, unknown): “Another study using an ABM also analyzed several scenarios and highlighted that with 90% of the population in isolation, it is possible to control the disease within 13 weeks when joined with effective case isolation and international travel restrictions, considering the Australian context [34].”
- COVI White Paper (2020, background): “The plateau lasts for a number of days sampled from a Gaussian centred on 5 days The decrease lasts for a number of days sampled from a Gaussian centred on 5 days [36, 82].”
- Covasim: An agent-based model of COVID-19 dynamics and interventions (2020, background): “For microsimulation models, several agent-based influenza pandemic models have been repurposed to simulate the spread of COVID-19 transmission and the impact of social distancing measures in Australia (Chang et al., 2020), Singapore (Koo et al., 2020), the United States (Chao et al., 2020), and the U”
- To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic (2020, background): “Most mathematical models of the COVID-19 pandemic can broadly be divided into either population-based, SIR (Kermack-McKendrick)type models, driven by (potentially stochastic) differential equations [38, 20, 34, 22, 21, 23, 31, 26, 32, 24, 33] , or agent-based models [39, 28, 25, 27, 30] , in which i”