About: Modelling the impact of reducing control measures on the COVID-19 pandemic in a low transmission setting

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About: Modelling the impact of reducing control measures on the COVID-19 pandemic in a low transmission setting Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Modelling the impact of reducing control measures on the COVID-19 pandemic in a low transmission setting
Creator	Palmer, Anna Wilson, David Klein, Daniel Mistry, Dina Heath, Katie Abeysuriya, Romesh Delport, Dominic Kerr, Cliff Stuart, Robyn Scott, Nick Hainsworth, Samuel Hellard, Margaret Pedrana, Alisa Sacks-Davis, Rachel Stoove, Mark
Source	MedRxiv
abstract	Aims: We assessed COVID-19 epidemic risks associated with relaxing a set of physical distancing restrictions in the state of Victoria, Australia - a setting with low community transmission - in line with a national framework that aims to balance sequential policy relaxations with longer-term public health and economic need. Methods: An agent-based model, Covasim, was calibrated to the local COVID-19 epidemiological and policy environment. Contact networks were modelled to capture transmission risks in households, schools and workplaces, and a variety of community spaces (e.g. public transport, parks, bars, cafes/restaurants) and activities (e.g. community or professional sports, large events). Policy changes that could prevent or reduce transmission in specific locations (e.g. opening/closing businesses) were modelled in the context of interventions that included testing, contact tracing (including via a smartphone app), and quarantine. Results: Policy changes leading to the gathering of large, unstructured groups with unknown individuals (e.g. bars opening, increased public transport use) posed the greatest risk, while policy changes leading to smaller, structured gatherings with known individuals (e.g. small social gatherings) posed least risk. In the model, epidemic impact following some policy changes took more than two months to occur. Model outcomes support continuation of working from home policies to reduce public transport use, and risk mitigation strategies in the context of social venues opening, such as >30% population-uptake of a contact-tracing app, physical distancing policies within venues reducing transmissibility by >40%, or patron identification records being kept to enable >60% contact tracing. Conclusions: In a low transmission setting, care should be taken to avoid lifting sequential COVID-19 policy restrictions within short time periods, as it could take more than two months to detect the consequences of any changes. These findings have implications for other settings with low community transmission where governments are beginning to lift restrictions.
has issue date	2020-06-12(xsd:dateTime)
bibo:doi	10.1101/2020.06.11.20127027
has license	medrxiv
sha1sum (hex)	edcf9e16c1e2ae85d55fe951cf1c4a8d6ccbdac8
schema:url	https://doi.org/10.1101/2020.06.11.20127027
resource representing a document's title	Modelling the impact of reducing control measures on the COVID-19 pandemic in a low transmission setting
resource representing a document's body	covid:edcf9e16c1e2ae85d55fe951cf1c4a8d6ccbdac8#body_text
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