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

Abstract

AbstractEarly 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. 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. The implementation of testing strategies in high risk settings in Australia was supported using models to explore the effects of test frequency and sensitivity on outbreak detection. An exponential growth model is firstly used to demonstrate how outbreak detection changes with varying growth rate, test frequency and sensitivity. From this model we see that low sensitivity tests can be compensated for by high frequency testing. This model is then updated to an Agent Based Model, which was used to test the robustness of the results from the exponential model, and to 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.