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

Abstract

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.

Insights into the role of children in the COVID-19 pandemic in Belgium: a longitudinal sensitivity analysis

“…However, in the early stages of an epidemic, such detailed data may not be available 17 , limiting our ability to generalize the approach and to reconstruct finer age structures from social contact survey data 69 . On the other hand, efforts to reconstruct social contact data on a finer time-scale 70 would definitely enhance the potential of our inherently linear analysis 27,63 . This is particularly relevant in scenarios where social contact patterns change rapidly and notably, such as around the implementation of new NPIs or during seasonal shifts, an aspect not explicitly addressed in our study.…”

“…Reporting biases, sampling errors, and selection biases 12 affect the age-specific representativeness of social contact data. A recent study 70 highlighted the importance of reconstructing social contact patterns considering the frequency and repetitiveness of contacts, showing how they can significantly influence the predicted dynamics of an epidemic. Uncertainty in age-specific contact patterns, susceptibility estimates, and the parameters used to construct our NGM likely affect the statistical significance of detected age-specific differences.…”

“…in the set Ω = [0, 6), [6,12), [12,18), [18,30), [30,40), [40,50), [50,60), [60,70), [70, ∞). Each index provides insights into how changes within a specific age group impact the transmission dynamics, as detailed in the Methods section and Section SM1.…”

“…This model initially divides individuals into ten distinct age groups, each spanning 10 years. To align with the specific age partitions of Ω = [0, 6),[6,12),[12,18),[18,30),[30,40),[40,50),[50,60),[60,70), [70, ∞ we adjust these groups. This adjustment assumes that the distribution within overlapping age intervals reflects the demographic composition of those intervals.…”