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A network modelling approach to assess non-pharmaceutical disease controls in a worker population : an application to SARS-CoV-2
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Hill, Edward M., Atkins, Benjamin D., Keeling, Matt J., Dyson, Louise and Tildesley, Michael J. (2021) A network modelling approach to assess non-pharmaceutical disease controls in a worker population : an application to SARS-CoV-2. PLoS Computational Biology, 17 (6). e1009058. doi:10.1371/journal.pcbi.1009058 ISSN 1553-7358.
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WRAP-network-modelling-approach-assess-non-pharmaceutical-disease-controls-worker-population-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3013Kb) | Preview |
Official URL: http://dx.doi.org/10.1371/journal.pcbi.1009058
Abstract
As part of a concerted pandemic response to protect public health, businesses can enact non-pharmaceutical controls to minimise exposure to pathogens in workplaces and premises open to the public. Amendments to working practices can lead to the amount, duration and/or proximity of interactions being changed, ultimately altering the dynamics of disease spread. These modifications could be specific to the type of business being operated. We use a data-driven approach to parameterise an individual-based network model for transmission of SARS-CoV-2 amongst the working population, stratified into work sectors. The network is comprised of layered contacts to consider the risk of spread in multiple encounter settings (workplaces, households, social and other). We analyse several interventions targeted towards working practices: mandating a fraction of the population to work from home; using temporally asynchronous work patterns; and introducing measures to create ‘COVID-secure’ workplaces. We also assess the general role of adherence to (or effectiveness of) isolation and test and trace measures and demonstrate the impact of all these interventions across a variety of relevant metrics. The progress of the epidemic can be significantly hindered by instructing a significant proportion of the workforce to work from home. Furthermore, if required to be present at the workplace, asynchronous work patterns can help to reduce infections when compared with scenarios where all workers work on the same days, particularly for longer working weeks. When assessing COVID-secure workplace measures, we found that smaller work teams and a greater reduction in transmission risk reduced the probability of large, prolonged outbreaks. Finally, following isolation guidance and engaging with contact tracing without other measures is an effective tool to curb transmission, but is highly sensitive to adherence levels. In the absence of sufficient adherence to non-pharmaceutical interventions, our results indicate a high likelihood of SARS-CoV-2 spreading widely throughout a worker population. Given the heterogeneity of demographic attributes across worker roles, in addition to the individual nature of controls such as contact tracing, we demonstrate the utility of a network model approach to investigate workplace-targeted intervention strategies and the role of test, trace and isolation in tackling disease spread.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics |
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| Library of Congress Subject Headings (LCSH): | COVID-19 (Disease) , COVID-19 (Disease) -- Prevention -- Simulation methods, COVID-19 (Disease) -- Transmission -- Prevention -- Simulation methods, Contact tracing (Epidemiology) -- Simulation methods, Industrial hygiene -- -- Simulation methods | ||||||||||||
| Journal or Publication Title: | PLoS Computational Biology | ||||||||||||
| Publisher: | Public Library of Science | ||||||||||||
| ISSN: | 1553-7358 | ||||||||||||
| Official Date: | 16 June 2021 | ||||||||||||
| Dates: |
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| Volume: | 17 | ||||||||||||
| Number: | 6 | ||||||||||||
| Article Number: | e1009058 | ||||||||||||
| DOI: | 10.1371/journal.pcbi.1009058 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
| Date of first compliant deposit: | 17 June 2021 | ||||||||||||
| Date of first compliant Open Access: | 18 June 2021 | ||||||||||||
| RIOXX Funder/Project Grant: |
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| Is Part Of: | 1 |
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