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.2023;31(6):917-932.
doi: 10.1007/s10389-021-01611-0. Epub 2021 Jun 28.

Epidemiological modelling of the health and economic effects of COVID-19 control in Australia's second wave

Affiliations

Epidemiological modelling of the health and economic effects of COVID-19 control in Australia's second wave

R Quentin Grafton et al. Z Gesundh Wiss.2023.

Abstract

Background: We investigated the public health and economy outcomes of different levels of social distancing to control a 'second wave' outbreak in Australia and identify implications for public health management of COVID-19.

Methods: Individual-based and compartment models were used to simulate the effects of different social distancing and detection strategies on Australian COVID-19 infections and the economy from March to July 2020. These models were used to evaluate the effects of different social distancing levels and the early relaxation of suppression measures, in terms of public health and economy outcomes.

Results: The models, fitted to observations up to July 2020, yielded projections consistent with subsequent cases and showed that better public health outcomes and lower economy costs occur when social distancing measures are more stringent, implemented earlier and implemented for a sufficiently long duration. Early relaxation of suppression results in worse public health outcomes and higher economy costs.

Conclusions: Better public health outcomes (reduced COVID-19 fatalities) arepositively associated with lower economy costs and higher levels of social distancing; achieving zero community transmissionlowers both public health and economy costs compared to allowing community transmission to continue; and early relaxation of social distancingincreases both public health and economy costs.

Keywords: Compartment models; Individual based models; Pandemic; SARS-CoV-2; Social distancing.

© The Author(s) 2021.

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Conflict of interest statement

Conflicts of interestThe authors have no conflicts of interest in relation to any part of the research including the modelling and reporting of the results.

Figures

Fig. 1
Fig. 1
Model suite representation of COVID-19 progression
Fig. 2
Fig. 2
a Community elimination: SD = 1.0 (day 35), trigger = 100 daily cases, PQ = 0.b Community elimination: SD = 0.7 (day 35), trigger = 100 daily cases, PQ = 0. N.B: Simulations (median, quartiles, 5–95 percentiles) are from a 1000 members ensemble and observed daily new local Australian cases for SD levels. Median (thick line), quartiles (thin lines), 5–95 percentiles (dashed lines), observed daily new Australian local cases, 6 June to 15 July 2020 (*)
Fig. 3
Fig. 3
a Scenario A: SD = 1.0, suppression (40 days minimum) & relaxation triggers 100 & 20.b Scenario B: SD = 1.0 (day 35), suppression & relaxation triggers 100 & 20. N.B. Ensemble percentiles: median (thick line), quartiles (thin lines), 5–95 percentiles (dashed lines), observed daily new Australian local cases, 6 June to 15 July 2020 (*). Triggers defined by daily cases. SD begins at day 35. Quarantine leakage, PQ = 0.002 in 2a and 2b
Fig. 4
Fig. 4
Daily new Australian cases of local origin from 20 February 2020. Observations in black. Percentiles for a posterior ensemble from the DCM: median (blue), quartiles (red), 2.5 and 97.5 percentiles (green)
Fig. 5
Fig. 5
Daily new Australian cases of local origin from 20 February 2020. Observations in black; percentiles for a posterior ensemble from the SCM: median (blue), quartiles (red), 2.5 and 97.5 percentiles (green)
Fig. 6
Fig. 6
Cumulative COVID-19 fatalities for Australia starting 20 February 2020. Observations in black; percentiles for a posterior ensemble from the SCM: median (blue), quartiles (red), 2.5 and 97.5 percentiles (green)
Fig. 7
Fig. 7
Cumulative COVID-19 recoveries for Australia starting 20 February 2020. Observations in black; percentiles for a posterior ensemble from the SCM: median (blue), quartiles (red), 2.5 and 97.5 percentiles (green)
Fig. 8
Fig. 8
a Marginal posterior pdfs for parameters subject to Bayesian inference.b Marginal posterior pdfs for parameters subject to Bayesian inference
Fig. 9
Fig. 9
Posterior correlation structure among parameters subject to Bayesian inference
See this image and copyright information in PMC

References

    1. Australian Department of Health (2020a) Impact of COVID-19 in Australia – ensuring the health system can respond. Australian Department of Health: Impact of COVID-19.https://www.health.gov.au/resources/publications/impact-of-covid-19-in-a.... Accessed 31 July 2020
    1. Australian Department of Health (2020b) COVD-19 Current situation and case numbers.https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov.... Accessed 10 December 2020
    1. Blakely T, Thompson J, Carvalho N, Bablan L, Wilson N, Stevenson M (2020) Maximizing the probability that the 6-week lock-down in Victoria delivers a COVID-19 free Australia. Med J Aust.https://www.mja.com.au/journal/2020/maximizing-probability-6-week-lock-d... [preprint, 17 July 2020]
    1. Chang SL, Harding N, Zachreson C, Cliff OM, Prokopenko M. Modelling transmission and control of the COVID-19 pandemic in Australia. Nat Commun. 2020;11:5710. doi: 10.1038/s41467-020-19393-6. - DOI - PMC - PubMed
    1. Grafton RQ, Parslow J, Kompas T, Glass K, Banks E (2020) What can we learn from the Victoria (Australia) versus Western Europe COVID-19 ‘Second Wave’ Responses? SSRN.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3744720. Accessed 11 December 2020

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