The approach utilises an agent-based model simulating pandemic responses in Australia, and accounts for a heterogeneous population with variable levels of compliance fluctuating over time and across individuals
Quang Dang Nguyen, Mikhail Prokopenko

Objective:

This work combined machine learning and agent-based modelling to explore various trade-offs between health effects of pandemic interventions and the incurred economic costs. In particular, it was shown that a socially acceptable balance between health effects and incurred economic costs is achievable over a long term, despite possible early setbacks. Informally, this study demonstrated the choice between 'health' and 'economy' is a false choice.

Findings:

The study analysis showed that a significant net health benefit may be attained by adaptive non-pharmaceutical interventions formed by partial social distancing measures, coupled with moderate levels of the society's willingness to pay for health losses. It was demonstrated that a socially acceptable balance between health effects and incurred economic costs is achievable over a long term, despite possible early setbacks.

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First published: May 19, 2022
Centre for Complex Systems & Sydney Institute for Infectious Diseases
The Centre for Complex Systems (CCS) at The University in Sydney studies collective and critical behaviours, with diverse applications ranging from computational epidemiology and systems biology to urban and social dynamics. The CCS has a strategic partnership with The University of Sydney Institute for Infectious Diseases.