Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic

Abstract

Background: During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants’ “survey fatigue”, which may impact inferences. Methods: A negative binomial generalized additive model for location, scale, and shape (NBI GAMLSS) was adopted to estimate the number of contacts reported between age groups and to deal with under-reporting due to fatigue within the study. The dropout process was analyzed with first-order auto-regressive logistic regression to identify factors that influence dropout. Using the so-called next generation principle, we calculated the effect of under-reporting due to fatigue on estimating the reproduction number. Results: Fewer contacts were reported as people participated longer in the survey, which suggests under-reporting due to survey fatigue. Participant dropout is significantly affected by household size and age categories, but not significantly affected by the number of contacts reported in any of the two latest waves. This indicates covariate-dependent missing completely at random (MCAR) in the dropout pattern, when missing at random (MAR) is the alternative. However, we cannot rule out more complex mechanisms such as missing not at random (MNAR). Moreover, under-reporting due to fatigue is found to be consistent over time and implies a 15-30% reduction in both the number of contacts and the reproduction number (R0) ratio between correcting and not correcting for under-reporting. Lastly, we found that correcting for fatigue did not change the pattern of relative incidence between age groups also when considering age-specific heterogeneity in susceptibility and infectivity. Conclusions: CoMix data highlights the variability of contact patterns across age groups and time, revealing the mechanisms governing the spread/transmission of COVID-19/airborne diseases in the population. Although such longitudinal contact surveys are prone to bias, participant fatigue, and drop-out, we showed that these factors can be identified and corrected using NBI GAMLSS. This information can be used to improve the design of similar, future surveys.

Repetition in social contacts: implications in modelling the transmission of respiratory infectious diseases in pre-pandemic and pandemic settings

“…Drawing from data obtained through the POLYMOD and Belgian CoMix studies [2,5], we reconstructed individuals’ contact patterns over a, without loss of generality, one week period to replicate the social contact dynamics of each individual. In both surveys, participants were asked to provide at least basic sociodemographic information and report contacts made on a day between 05.00 on the day preceding the survey day and 05.00 of the survey day.…”

“…Unlike the POLYMOD study, which is a cross-sectional survey, the CoMix study employs a biweekly longitudinal approach. The longitudinal design of this survey has been observed to be susceptible to participant fatigue, a phenomenon that may occur when participants become bored or less interested in the survey and that can result in contacts underreporting [2]. While statistical methods for under-reporting have been previously developed for the total number of contacts, we have extended such methodology to also account for the frequency of contacts.…”

“…insects or animals), airborne aerosols, droplets or direct physical contact [1]. Notably, viral respiratory infections, primarily transmitted during social interactions via droplets, aerosols and physical contact, continue to be major contributors to global morbidity and mortality [2]. Recognizing the significance of information on close interactions is pivotal for elucidating disease transmission [3]; thus, social contact data are often employed as a valuable proxy for identifying transmission routes.…”

“…Numerous studies similar in set-up have been conducted in different countries in both pre-pandemic and pandemic periods [4]. The CoMix study is the largest study across Europe gathering social contact data aiming to assess changes in contact behaviour during the pandemic [2,6–11]. The information from these surveys has proved invaluable in informing transmission dynamics within mathematical models including network models [12], agent-based models [13] and compartmental models [14–16].…”

“…Building on previous research, our objective is to reconstruct temporal contact patterns of subgroups within the population and calculate the age-specific number of distinct contacts over a one week period. We use social contact data obtained from contact diary surveys [2,5], exploring various age groups and different countries to elucidate contact patterns in various settings. Furthermore, we also consider the effect of non-pharmaceutical interventions, delineating how these temporal patterns fluctuated during the COVID-19 pandemic.…”