COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020–2021
Abstract Background Policy responses to COVID-19 in Victoria, Australia over 2020–2021 have been supported by evidence generated through mathematical modelling. This study describes the design, key findings, and process for policy translation of a series of modelling studies conducted for the Victor...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-05-01
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| Series: | BMC Public Health |
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| Online Access: | https://doi.org/10.1186/s12889-023-15936-w |
| _version_ | 1797817745790730240 |
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| author | Nick Scott Romesh G Abeysuriya Dominic Delport Rachel Sacks-Davis Jonathan Nolan Daniel West Brett Sutton Euan M Wallace Margaret Hellard |
| author_facet | Nick Scott Romesh G Abeysuriya Dominic Delport Rachel Sacks-Davis Jonathan Nolan Daniel West Brett Sutton Euan M Wallace Margaret Hellard |
| author_sort | Nick Scott |
| collection | DOAJ |
| description | Abstract Background Policy responses to COVID-19 in Victoria, Australia over 2020–2021 have been supported by evidence generated through mathematical modelling. This study describes the design, key findings, and process for policy translation of a series of modelling studies conducted for the Victorian Department of Health COVID-19 response team during this period. Methods An agent-based model, Covasim, was used to simulate the impact of policy interventions on COVID-19 outbreaks and epidemic waves. The model was continually adapted to enable scenario analysis of settings or policies being considered at the time (e.g. elimination of community transmission versus disease control). Model scenarios were co-designed with government, to fill evidence gaps prior to key decisions. Results Understanding outbreak risk following incursions was critical to eliminating community COVID-19 transmission. Analyses showed risk depended on whether the first detected case was the index case, a primary contact of the index case, or a ‘mystery case’. There were benefits of early lockdown on first case detection and gradual easing of restrictions to minimise resurgence risk from undetected cases. As vaccination coverage increased and the focus shifted to controlling rather than eliminating community transmission, understanding health system demand was critical. Analyses showed that vaccines alone could not protect health systems and need to be complemented with other public health measures. Conclusions Model evidence offered the greatest value when decisions needed to be made pre-emptively, or for questions that could not be answered with empiric data and data analysis alone. Co-designing scenarios with policy-makers ensured relevance and increased policy translation. |
| first_indexed | 2024-03-13T08:58:00Z |
| format | Article |
| id | doaj.art-07d8b460e9ac411abd85d3b69250b3bb |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-03-13T08:58:00Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-07d8b460e9ac411abd85d3b69250b3bb2023-05-28T11:29:33ZengBMCBMC Public Health1471-24582023-05-0123111210.1186/s12889-023-15936-wCOVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020–2021Nick Scott0Romesh G Abeysuriya1Dominic Delport2Rachel Sacks-Davis3Jonathan Nolan4Daniel West5Brett Sutton6Euan M Wallace7Margaret Hellard8Disease Elimination Program, Burnet InstituteDisease Elimination Program, Burnet InstituteDisease Elimination Program, Burnet InstituteDisease Elimination Program, Burnet InstituteVictorian Government Department of HealthVictorian Government Department of HealthVictorian Government Department of HealthVictorian Government Department of HealthDisease Elimination Program, Burnet InstituteAbstract Background Policy responses to COVID-19 in Victoria, Australia over 2020–2021 have been supported by evidence generated through mathematical modelling. This study describes the design, key findings, and process for policy translation of a series of modelling studies conducted for the Victorian Department of Health COVID-19 response team during this period. Methods An agent-based model, Covasim, was used to simulate the impact of policy interventions on COVID-19 outbreaks and epidemic waves. The model was continually adapted to enable scenario analysis of settings or policies being considered at the time (e.g. elimination of community transmission versus disease control). Model scenarios were co-designed with government, to fill evidence gaps prior to key decisions. Results Understanding outbreak risk following incursions was critical to eliminating community COVID-19 transmission. Analyses showed risk depended on whether the first detected case was the index case, a primary contact of the index case, or a ‘mystery case’. There were benefits of early lockdown on first case detection and gradual easing of restrictions to minimise resurgence risk from undetected cases. As vaccination coverage increased and the focus shifted to controlling rather than eliminating community transmission, understanding health system demand was critical. Analyses showed that vaccines alone could not protect health systems and need to be complemented with other public health measures. Conclusions Model evidence offered the greatest value when decisions needed to be made pre-emptively, or for questions that could not be answered with empiric data and data analysis alone. Co-designing scenarios with policy-makers ensured relevance and increased policy translation.https://doi.org/10.1186/s12889-023-15936-wCOVID-19Mathematical modelOutbreak analysisDisease control |
| spellingShingle | Nick Scott Romesh G Abeysuriya Dominic Delport Rachel Sacks-Davis Jonathan Nolan Daniel West Brett Sutton Euan M Wallace Margaret Hellard COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020–2021 BMC Public Health COVID-19 Mathematical model Outbreak analysis Disease control |
| title | COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020–2021 |
| title_full | COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020–2021 |
| title_fullStr | COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020–2021 |
| title_full_unstemmed | COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020–2021 |
| title_short | COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020–2021 |
| title_sort | covid 19 epidemic modelling for policy decision support in victoria australia 2020 2021 |
| topic | COVID-19 Mathematical model Outbreak analysis Disease control |
| url | https://doi.org/10.1186/s12889-023-15936-w |
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