Rapid prototyping of models for COVID-19 outbreak detection in workplaces

Abell, I; Zachreson, C; Conway, E; Geard, N; McVernon, J; Waring, T; Baker, C

Author(s): Abell, I; Zachreson, C; Conway, E; Geard, N; McVernon, J; Waring, T; Baker, C;
Details: Publication Year 2023-10-23,Volume 23,Issue #1,Page 713
Journal Title: BMC Infectious Diseases
Abstract: Early case detection is critical to preventing onward transmission of COVID-19 by enabling prompt isolation of index infections, and identification and quarantining of contacts. Timeliness and completeness of ascertainment depend on the surveillance strategy employed. This paper presents modelling used to inform workplace testing strategies for the Australian government in early 2021. We use rapid prototype modelling to quickly investigate the effectiveness of testing strategies to aid decision making. Models are developed with a focus on providing relevant results to policy makers, and these models are continually updated and improved as new questions are posed. Developed to support the implementation of testing strategies in high risk workplace settings in Australia, our modelling explores the effects of test frequency and sensitivity on outbreak detection. We start with an exponential growth model, which demonstrates how outbreak detection changes depending on growth rate, test frequency and sensitivity. From the exponential model, we learn that low sensitivity tests can produce high probabilities of detection when testing occurs frequently. We then develop a more complex Agent Based Model, which was used to test the robustness of the results from the exponential model, and extend it to include intermittent workplace scheduling. These models help our fundamental understanding of disease detectability through routine surveillance in workplaces and evaluate the impact of testing strategies and workplace characteristics on the effectiveness of surveillance. This analysis highlights the risks of particular work patterns while also identifying key testing strategies to best improve outbreak detection in high risk workplaces.
Publisher: BMC
Keywords: Humans; *COVID-19/epidemiology; SARS-CoV-2; Australia/epidemiology; Disease Outbreaks/prevention & control; Workplace; Decision making; Infectious disease modelling
Research Division(s): Population Health And Immunity
PubMed ID: 37872480
Publisher's Version: https://doi.org/10.1186/s12879-023-08713-y
Open Access at Publisher's Site: https://doi.org/10.1186/s12879-023-08713-y
Terms of Use/Rights Notice: Refer to copyright notice on published article.
Documents: s12879-023-08713-y.pdf - (1.18MB, application/pdf)

Creation Date: 2023-11-15 05:00:01

Last Modified: 2023-11-15 05:05:52