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

BMC Infectious Diseases

Research

Jodie McVernon¹  Nicholas Geard²  Cameron Zachreson²  Isobel Abell³  Thomas Waring⁴  Christopher Baker⁴  Eamon Conway⁵ 

[1] Peter Doherty Institute for Infection and Immunity, The University of Melbourne and the Royal Melbourne Hospital, Melbourne, Australia;Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia;Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Australia;School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia;School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia;Melbourne Centre for Data Science, The University of Melbourne, Melbourne, Australia;School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia;Melbourne Centre for Data Science, The University of Melbourne, Melbourne, Australia;Centre of Excellence for Biosecurity Risk Analysis, The University of Melbourne, Melbourne, Australia;Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia;

关键词: Infectious disease modelling;    Decision making;   

DOI  :  10.1186/s12879-023-08713-y

 received in 2023-03-06, accepted in 2023-10-15,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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Fig. 1: The conceptual framework for adherence to treatment guidelines in private drug outlets in Kisumu, Kenya

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