Impact of the representation of contact data on the evaluation of interventions in infectious diseases simulations (preprint)

ABSTRACT

Computational models offer a unique setting to test strategies to mitigate infectious diseases’ spread, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high resolution contact data, we use data representations ranging from very detailed to very coarse to inform a model for the spread of SARS-CoV-2 and simulate several mitigation strategies. We find that coarse data representations underestimate the risk of super-spreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs that may trigger the adoption of protocols, as these may depend on data representation.