Background
Infectious diseases and, more specifically, airborne infections can be transmitted between hosts via close contact interactions; therefore, quantifying such interactions provides important information for properly modelling infectious disease transmission. In recent years, we have witnessed a paradigm shift with respect to this: whereas at the start of this century, mathematical models relied on simplifying assumptions such as homogeneous mixing or on using mathematically convenient “Who Acquires Infection From Whom” constructs [
1], a vast number of studies now rely on the use of social contact data [
2‐
8].
The literature on social contact surveys has shown how human interactions are heterogeneous in nature and present a large degree of homophily in terms of age [
9,
10] and sex [
11]. The information coming from social contact surveys is therefore usually summarized in what is called the social contact matrix, quantifying the average number of contacts made between individuals within and between given age classes. Using the
social contact hypothesis [
2], i.e. assuming that transmission rates are proportional to social contact rates, these data-driven mixing patterns have been implemented into models of infectious disease transmission showing good correspondence to (sero)prevalence data; see, e.g., [
4,
9,
12].
Social contact survey data allow for an exploration of contact rate patterns stratified by age, sex, and location, which helps to better describe the structure of the transmission network [
13,
14]. However, a systematic review by Hoang et al. (2019) [
10] showed that half of the social contact surveys before 2019 used convenience sampling, while quite a few surveys were conducted in specific settings, e.g., schools or universities, and/or focus on specific target groups; thus, it is impossible to extrapolate the results to an entire population. Even in population-based social contact surveys with representative samples, two problems might still exist: the sample does not cover all age ranges of the population, or the number of elderly participants is insufficient for investigating mixing patterns of these people. Indeed, no study reported the contact rates of people up to 99 years old.
Particular attention has been devoted to behavioural changes with respect to individual health status (e.g., being ill [
6,
15‐
17]), weather conditions [
5] or day of the week (weekday or weekend in holiday/non-holiday or regular periods [
9,
18‐
22]) - hereafter referred to as
microscopic time settings, and how these affect disease dynamics [
6,
8,
19,
23,
24].
The use of social contact data to inform modelling has become so prominent in recent works that it has also been applied to settings for which social contact studies are not available, leading to the question of how social contact matrices should be projected onto other geographical areas and in time [
7,
25‐
27]. However, to the best of our knowledge, there has been no empirical assessment of whether mixing patterns change over longer time periods (e.g., years) within a particular population and how this should be taken into account when projecting social contact matrices. We will refer to these as
macroscopic time changes to mark the difference with
microscopic time changes.
A first population-based social contact survey in Belgium was conducted in 2006, and its results were reported in [
9,
19,
28], in which the impact of
microscopic time changes on the contact mixing pattern was investigated, although this study was not designed for doing so. A second population-based survey in Belgium was conducted 5 years later in 2010-2011. This survey was conceived as an improvement over the 2006 survey, with a larger sample size covering a wider age range of participants and a better distribution of surveyed participants over four different time settings (weekday/weekend days in regular/holiday periods).
In this work, we aim to describe and analyse the social contact survey in Flanders, Belgium from 2010-2011 by accounting for the mixing patterns of people 0-99 years of age, with a particular focus on elderly people. We study both the impact of microscopic and macroscopic time changes on contact patterns, and we assess whether the contact rates remain stable over 5 years timespan.
Data
Social contact survey in 2006: This survey was part of the POLYMOD project, in which social contact surveys were conducted in 8 European countries in 2005-2006 [
9]. In the social contact survey conducted in Belgium in 2006, a total of 750 participants were recruited by random digit dialing on land lines. The survey sample covered all three regions in Belgium; i.e. the Flemish, Walloon and Brussels-Capital regions, with quota sampling by age, sex, and region, making it representative for the whole Belgian population. Each participant was asked to fill in a background questionnaire and a paper diary in which they record their contacts over 2 days: one randomly assigned weekday and one randomly assigned weekend day. Two types of contacts were defined: (1) two-way conversations during which at least three words were spoken and (2) contacts that involved skin-to-skin touching. Information recorded in the diary included sex and the exact age or presumed age interval of each contacted person over the entire day. Contact features included frequency, location and duration. If participants established more than 20 professional contacts per day, then they only had to provide an estimated number of professional contacts and the age interval(s) with whom they interacted most. Contact information (e.g., contact age or contact duration) was then imputed for such contacts. More details can be found in [
28]. We will refer to those contacts as additional professional contacts.
Social contact survey in 2010-2011: This survey was conducted between September 2010 and February 2011 in Flanders (including the Flemish and Brussels-Capital regions) in Belgium using an adapted version of the diaries used in the first Belgian survey in 2006. Three different types of diaries were designed to adapt to the age of participants: one for children (less than 13 years old), which was completed by a proxy, e.g., parents or school teachers; one for people aged 13-60 years and one for people aged 60+ years, which could also be filled out by a proxy. A total of 1,774 participants were recruited by random digit dialing on mobile phones and landlines, with quota sampling by age, sex and geographical location. The contact definitions were the same as those used in the 2006 survey. Participants were asked to complete a background survey and record their social contacts in a paper diary during one randomly assigned day. Information on additional professional contacts was imputed the same way as done for 2006 data. Compared with the 2006 survey, the 2010-2011 survey explored more features that might influence the number of contacts recorded: the health conditions of participants, time use, distance from home, animal ownership and touching. To date, the impact of animal ownership and touching on social contacts has been investigated [
29], so has the impact of weather on social contacts [
5]. Of particular focus were people aged 60 years and above; i.e., participants up to 99 years of age were recruited, and information about contact frequency with children and grandchildren and residence size for elderly people living in nursing/elderly homes was recorded.
The design of the 2010-2011 survey is similar to that of 2006, with the difference being that in 2010-2011, participants reported information for only one day, whereas in 2006, information was collected for two days. Since participants have been shown to be influenced by fatigue in reporting on multiple days [
10,
22], only data on the first day of the 2006 survey was used for comparison with the 2010-2011 survey in this work. Additionally, we extracted the 511 participants recruited in Flanders from the 2006 survey to be in line with the study population in the 2010-2011 survey. In the 2010-2011 survey, 15 cases were removed since the diaries were unreliable (many answers left blank, incoherent answers, etc.). We also excluded 46 people living in an elderly/nursing home and explored the contact patterns of these people separately. In addition, 6 people aged 90 and older (4 in the age group [90;95) and 2 in the age group [95;100)) for reasons of data sparsity, e.g. when investigating the impact of microscopic time differences on the age group-specific degree distribution or sex differences in mixing patterns. As a result, the final sample for the analysis of the 2010-2011 survey is 1,707 participants. We defined four microscopic time settings: regular weekdays, regular weekends, holiday weekends, and holiday weekdays. Holiday periods include both public holidays and weekends inside or adjacent to these holidays. More details on the number of participants by age and microscopic time in both surveys can be found in Additional file
1 Table S1. The datasets of both surveys are available online within the
social contact data sharing initiative [
30] and the SOCRATES platform [
14].
Discussion
Social contact surveys provide empirical data on populations’ mixing patterns that can inform mathematical models of infectious diseases. In Belgium, two large diary-based social contact surveys were conducted in 2006 and 2010-2011. In this work, we present the results of the latest survey, discussing the impact of microscopic time differences on mixing patterns and comparing the 2010-2011 data with the 2006 data (in Flanders only). This approach allowed for assessing changes over macroscopic time differences, albeit in the limiting scenario of two surveys conducted only 4-5 years apart in a region where demography has remained fairly stable.
The association rules revealed that contacts of less than 15 min with non-household members usually do not involve skin-to-skin touching. This finding is in line with the results of the 2006 survey [
28]. To investigate the contact profiles of participants, we performed clustering analysis. Our clustering results are comparable to the results in [
3], in which a two-step clustering approach was applied to contact data from eight European countries. Specifically, we endorsed the “school profile”, “professional profile”, and “leisure profile” from [
3], with more contacts during leisure activities during weekends.
Demographic factors, including age, household and province of residence, had significant effects on the number of contacts, as did the temporal factors, e.g., weekdays vs weekend days or regular terms vs holiday periods [
9,
10,
16,
22,
40]. It is noted that the interaction between people aged 60+ years and young children/teenagers was significantly higher during holidays and weekends compared to regular- weekdays. For people living in an elderly/nursing home, however, almost no contacts with young children/teenagers were reported. Using public transportation was associated with a higher number of contacts in total. Our analysis also showed that those who reported to feel ill had fewer contacts than those who reported to be healthy [
6,
10,
15‐
17]. This also holds for participants reporting health problems such as anxiety or those experiencing problems in daily activities.
There was evidence, at least among school-aged children, that contact patterns were assortative with respect to both age and sex. While an assortative mixing pattern with respect to age was still observed in adults, albeit with lower contact rates, an assortative mixing pattern with respect to sex disappears in people aged 30+ years. This analysis was also performed in [
41], where a hierarchical Bayesian model was used to infer age-specific contact rates between sexes. In contrast to [
41], we did not find significant differences in infection risk between males and females. There are some reasons that may explain this difference. First, we aggregated the age of participants in 20 age classes instead of using continuous age, which can incur an inevitable loss of detail. Second, the dispersion parameter in our model was assumed to be age-dependent, while it was treated as a nuisance parameter in [
41] to avoid computation challenges. In addition, we used diary weights in contact modelling to account for under-/over-sampling over the age of participants, while weights were not taken into account in the model of [
41].
We found that the number of contacts was lower on weekends than on weekdays and during holidays compared to regular periods. We find a 30% (BCI:[17; 37%) reduction in
R0 for weekends versus weekdays or a 29% (BCI:[14; 40%]) reduction in
R0 for holidays versus regular periods. This result is consistent with the results of other studies [
8,
19,
40,
42,
43]. However, computing the age-specific relative incidence showed that this reduction was due to the younger age classes, both during weekends and during holidays. Additionally, the age-specific relative incidence showed that during holidays, there was a more complex change than during regular weekends: while younger people had a lower relative incidence, people older than 60 years had an increased relative incidence during holidays.
Contact matrices were compared in different microscopic time settings, namely, regular weekdays, holiday weekdays and weekends, to obtain insights into possible changes in contact patterns between 2006 and 2010-2011 (Fig.
6). Our result showed that irrespective of microscopic time settings, the contact patterns in 2006 and 2010-2011 followed the same trend of assortativeness. Furthermore, we observed pronounced inter-generational age mixing (the two sub-diagonals of the contact matrices), most likely indicating parent-child mixing patterns. This finding supports the evidence that households are central units in the epidemiology of airborne infections, e.g., influenza and SARS, because of the nature of the frequent and intimate contacts among household members. Children thus can have a bridging function, allowing for the spread of infection within households and to other households, from schools to workplaces or vice versa in a community [
44]. The mixing patterns obtained from the contact matrices in 2006 and 2010-2011 in our study are in agreement with mixing patterns observed in similar studies [
9,
10]. The relative incidences based on the 2006 and 2010-2011 data were quite similar between regular weekdays and holiday weekdays but are dissimilar on the weekend, as the highest relative incidence was found in two different age classes (10-15 years and 20-25 years for 2006 and 2010-2011, respectively).
In this study, we found that contact patterns remained fairly constant over 4-5 years. Additionally, within each microscopic time period, no substantial changes in the spread of infection, measured by the relative basic reproduction number and age-specific incidences, were observed (Fig.
7). After taking into account multiple testings, the pair-wise comparison of contact rates over time presented only few significant differences during holiday weekdays, mostly for people aged 50+ years. While the comparison of only two observational periods about five years apart can be considered a limitation, to the best of our knowledge, this is the first study, that investigates empirically whether contact rates remain stable, in the absence of major shocks to risk perception (as we expect to observe in the SARS-CoV-2 pandemic emergence year 2020) and demography.
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