Repetition in Social Contact Interactions: Implications in Modelling the Transmission of Respiratory Infectious Diseases in Pre-pandemic & Pandemic Settings

The spread of viral respiratory infections is intricately linked to human interactions, and this relationship can be characterised and modelled using social contact data. However, many analyses tend to overlook the recurrent nature of these contacts. To bridge this gap, we undertake the task of describing individuals contact patterns over time, by characterising the interactions made with distinct individuals during a week. Moreover, we gauge the implications of this temporal reconstruction on disease transmission by juxtaposing it with the assumption of random mixing over time. This involves the development of an age-structured individual-based model, utilising social contact data from a pre-pandemic scenario (the POLYMOD study) and a pandemic setting (the Belgian CoMix study), respectively. We found that accounting for the frequency of contacts impacts the number of new, distinct, contacts, revealing a lower total count than a naive approach, where contact repetition is neglected. As a consequence, failing to account for the repetition of contacts can result in an underestimation of the transmission probability given a contact, potentially leading to inaccurate conclusions when using mathematical models for disease control. We therefore underscore the necessity of acknowledging the longitudinal nature of contacts when formulating effective public health strategies.