Rapid prototyping of models for COVID-19 outbreak detection in workplaces
BMC Infectious Diseases·
- DOI
- 10.1186/s12879-023-08713-y
- PMID
- —
- PMCID
- —
- OpenAlex
- W4387878986
- Study type
- Journal article
- Publisher
- Springer Science and Business Media LLC
- Article type
- journal-article
- Integrity
- current
Publication version
This article has a linked preprint
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Open linked preprint →Why this research matters now
The work informs workplace testing policy in Australia by illustrating how surveillance design choices shape detectability of COVID-19 outbreaks in occupational settings.
Structured evidence summary
Research question
How do test frequency, test sensitivity, and workplace scheduling patterns affect outbreak detection of COVID-19 in high-risk workplace settings?
Study design
Rapid prototype mathematical modelling study comprising an initial exponential growth model followed by a more detailed agent-based model, iteratively updated to address new policy questions.
Population and setting
High-risk workplace settings in Australia, with the agent-based model extended to incorporate intermittent workplace scheduling.
Main findings
The exponential model indicated that low-sensitivity tests can still yield high probabilities of outbreak detection when testing is frequent. The agent-based model broadly supported these results and extended them to intermittent work patterns, highlighting risks associated with particular scheduling arrangements and identifying testing strategies that improve detection.
Public-health relevance
The work informs workplace testing policy in Australia by illustrating how surveillance design choices shape detectability of COVID-19 outbreaks in occupational settings.
Important limitations
This summary is limited to the supplied single-article abstract and bibliographic metadata; the original paper is required to assess explicit assumptions, parameter values, and any stated limitations of the modelling approach.
GIDS interpretation
The study is discoverable through classifiers linking it to COVID-19 surveillance, outbreak investigation, and health policy in Australia; no connection to a live surveillance signal is made here.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 11 auditable classifier relationships to diseases, places, topics, and study design.