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Modelling transmission and control of the COVID-19 pandemic in Australia (2003.10218v4)

Published 23 Mar 2020 in q-bio.PE, cs.MA, and q-bio.QM

Abstract: 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.

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Authors (5)
  1. Sheryl L. Chang (11 papers)
  2. Nathan Harding (5 papers)
  3. Cameron Zachreson (16 papers)
  4. Oliver M. Cliff (12 papers)
  5. Mikhail Prokopenko (52 papers)
Citations (543)

Summary

Modelling Transmission and Control of the COVID-19 Pandemic in Australia

The paper investigates the dynamics and control strategies of COVID-19 in Australia using a high-resolution, agent-based model (ABM). Developed by researchers at the University of Sydney, the model is calibrated to capture the nuanced characteristics of COVID-19, including age-dependent symptomatic fractions and various intervention strategies.

The paper evaluates several intervention strategies—international travel restrictions, case isolation, home quarantine, social distancing, and school closures. Each strategy is analyzed for its effectiveness in curbing the epidemic spread.

Key Findings and Results

  • Age-Dependent Symptomatic Fractions: One significant calibration outcome is the marked difference in symptomatic cases between children and adults, where children’s symptomatic cases are determined to be a fifth of adult cases.
  • School Closures: While school closures delay the epidemic peak by approximately four weeks, they do not substantially alter the overall attack rate unless paired with high compliance in social distancing.
  • Social Distancing: Strong compliance is critical. The research identifies a threshold between 70% and 80% compliance, below which the epidemic cannot be adequately controlled. Compliance at or above 90% effectively suppresses the disease within approximately 13-14 weeks.
  • Intervention Combinations: The combination of high-level social distancing (80% or 90%) with robust case isolation, home quarantine, and international travel restrictions presents the most effective strategy for epidemic control in Australia.
  • Sensitivity and Calibration: The sensitivity analysis confirms the robustness of the model, indicating the generation period and attack rate in children are particularly sensitive to input parameters.

Implications and Future Directions

The findings have critical implications for policy makers, highlighting the importance of maintaining high levels of social distancing compliance to manage the spread of COVID-19. The research also underscores the limited impact of school closures alone and emphasizes the synergy of combined interventions for effective disease control.

Theoretically, the paper contributes to understanding the phase transitions in epidemic spread, particularly within structured populations. Practically, it informs real-time policy adjustments and future pandemic preparedness by emphasizing quick and coordinated responses to maintain high social distancing compliance.

The insights gleaned from the Australian context provide a template for other nations managing the pandemic, particularly in understanding the thresholds needed for effective epidemic suppression. Future research could explore more refined attributes of COVID-19's natural history, update demographic inputs, and incorporate hospitalizations and in-hospital transmissions for enhanced accuracy. Comparative studies between ABMs and network-based models might also yield further insights into intervention strategies and transmission dynamics.

In summary, the research provides a comprehensive analysis of Australia’s strategic interventions in managing the COVID-19 pandemic, presenting critical thresholds and effective combinations needed to curb the spread of the virus.

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