Post-pandemic social contacts in Italy: implications for distancing measures on in-person school and work attendance

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We conducted two waves of an online survey in Italy (March 2022 and March 2023), collecting representative data on direct (verbal/physical) and indirect (indoor co-location) contacts. Using a generalised linear mixed model, we analysed social contact determinants and the impact of work-from-home and distance learning on pathogen transmissibility. In-person work or school attendance significantly increased contacts: adults attending in person had 1.69 times (95% CI: 1.56–1.84) more contacts than those staying home, while for children and adolescents, this ratio was 2.38 (95% CI: 1.98–2.87). Even suspending all non-essential work had a marginal effect on transmissibility. However, combining work-from-home with distance learning (from primary school onwards) could reduce transmissibility by up to 23.7% (95% CI: 18.2–29.0%), with minimal additional benefit from suspending early childcare. These findings offer key data for modelling respiratory pathogen transmission in Italy post-COVID-19 and provide insights into the epidemiological impact of tailored distancing measures. They support a nuanced approach to social distancing policies, balancing public health benefits with economic and social considerations. Biological sciences/Computational biology and bioinformatics/Computational models Health sciences/Health care/Health policy Physical sciences/Mathematics and computing/Applied mathematics Figures Figure 1 Figure 2 Figure 3 Introduction Respiratory infectious diseases are spread in human populations through interactions associated with social contacts, occurring during the execution of everyday life activities. During severe health emergencies caused by respiratory infectious agents, such as pandemics, the limitation in the number of social contacts enacted through governmental mandates and recommendations (sometimes referred to as “social distancing” measures) can be an effective means to interrupt the chains of transmission and rapidly curb the disease spread 1 , 2 . However, such measures may come at extensive economic and societal costs. Among the many measures aimed at reducing the transmission of SARS-CoV-2, school closures have been widely debated 3 amidst concerns about their negative short- and long-term impact on young individuals' education and psychological well-being 3 – 5 . In addition to this, the suspension of most economic activities during the lockdown enacted by many countries in the spring of 2020 has resulted in massive economic damage, disruption of supply chains, and job losses 6 , exacerbating poverty and inequality. To properly weigh the costs and benefits of social distancing measures, a crucial step is to quantify their impact on transmission. This difficult task is usually addressed through mathematical modelling, which largely relies on the availability of information on how many social contacts individuals experience across different socio-demographic stratifications, social settings and activities 7 – 12 . Although such information was available, to some extent, for most countries at the onset of the COVID-19 pandemic 13 – 15 , the individuals' behavioural responses and imposed restrictions 16 made it somewhat outdated 17 . Several social contact studies were conducted in selected countries to assess changes in human mixing patterns throughout the course of the epidemics 7 , 8 , 18 – 20 . However, as COVID-19-related restrictions have progressively been lifted in most countries starting in 2022 16,21 , comprehensive knowledge of post-pandemic contact patterns is still missing 19 , 22 – 25 . Here, we provide updated evidence on contact patterns in Italy obtained by internet-based surveys conducted in March 2022 and March 2023. We combine the collected contact pattern data with data on educational demographics and age-specific in-person workforce participation to quantify the potential impact of targeted governmental restrictions on the transmissibility of a novel respiratory pathogen. Results We conducted two waves of the same online social contact survey on a sample representative of the age, sex, and area of residence (NUTS1 level, i.e. major socio-economic regions 26 ) of the Italian population. The first wave covered the period from March 17 to March 29, 2022, and the second from March 17 to April 5, 2023. Participants were asked to provide their socio-demographic and health-related information and to report both “direct” and “indirect” contacts encountered on the day before the survey. A direct contact was defined as a person with whom physical interactions (e.g. a handshake, a pat on the back, a hug, a kiss, etc.) and/or in-person verbal exchanges of at least five words occurred. Participants were asked to provide details on each of their direct contacts, such as their age, sex, or the setting in which the interaction occurred. Participants were additionally asked to estimate the number of indirect contacts encountered on the previous day. An indirect contact was defined as a person with whom the respondent was co-located in a closed environment for at least 30 minutes (e.g., classmates at school or colleagues at work). For the main analysis in this study, we defined a contact as any direct or indirect contact (see Materials and Methods). The survey was administered to both adults (18 or more years old) and minors (less than 18 years old). Participants aged 14 to 17 years were requested to compile the questionnaire under the supervision of a legal guardian (usually a parent); for participants under 14, the compilation was performed by a legal guardian and the active engagement of the minor participant was strongly encouraged to ensure accurate responses to the questions. The final sample included 3,743 participants producing 4,979 valid questionnaires, of which 2,474 were from the 2022 wave and 2,505 from the 2023 wave. A subset of 1,236 participants responded to both the 2022 and 2023 waves. Summary descriptions of the participants’ characteristics and the corresponding mean numbers of contacts are reported in Table 1 and Table 2 . Full details on the collected information are reported in the Supplementary Information. Table 1 Survey respondents’ characteristics and average contacts by wave. The table reports the number of respondents (and the percentage with respect to the number of respondents in each wave) and respondents' average number of social contacts (and 95% credible interval) stratified by different respondent characteristics: age, sex, household income, household size and COVID-19 vaccination status. Average and Confidence Intervals (CI) are computed through 10,000 bootstrap iterations sampling with replacement from the set of respondents within each demographic group. Variables March 2022 wave March 2023 wave Both waves Sample size Avg. Contacts (95% CI) Sample size Avg. Contacts (95% CI) Sample size Avg. Contacts (95% CI) Total 2474 7.3 (6.9–7.8) 2505 7.8 (7.4–8.2) 4979 7.5 (7.2–7.9) Age group 0–9 150 (6.1%) 11.1 (9.5–12.7) 112 (4.5%) 12.6 (10.5–14.8) 262 (5.3%) 11.7 (10.5–13) 10–19 165 (6.7%) 12.7 (10.9–14.6) 103 (4.1%) 15.2 (12.5–18) 268 (5.4%) 13.6 (12.1–15.2) 20–29 202 (8.2%) 10.4 (8.5–12.6) 185 (7.4%) 10.8 (8.8–13.1) 387 (7.8%) 10.6 (9.2–12.2) 30–39 376 (15.2%) 8 (6.9–9.2) 357 (14.3%) 8.8 (7.6–10.1) 733 (14.7%) 8.4 (7.6–9.3) 40–49 381 (15.4%) 6 (5.1–7.1) 339 (13.5%) 6.7 (5.8–7.7) 720 (14.5%) 6.4 (5.7–7.1) 50–59 432 (17.5%) 6.3 (5.4–7.2) 506 (20.2%) 7.4 (6.4–8.3) 938 (18.8%) 6.9 (6.2–7.5) 60–69 320 (12.9%) 6.5 (5.4–7.7) 358 (14.3%) 6.3 (5.3–7.4) 678 (13.6%) 6.4 (5.6–7.2) 70+ 448 (18.1%) 4.8 (4.2–5.5) 545 (21.8%) 5.7 (5–6.5) 993 (19.9%) 5.3 (4.8–5.8) Sex Male 1248 (50.4%) 7.5 (6.9–8.1) 1272 (50.8%) 8.2 (7.6–8.9) 2520 (50.6%) 7.9 (7.4–8.3) Female 1226 (49.6%) 7.1 (6.6–7.8) 1233 (49.2%) 7.3 (6.8–7.9) 2459 (49.4%) 7.2 (6.8–7.6) Net household income (euros per month) 3,000 509 (20.6%) 8.9 (8–10) 551 (22%) 10.2 (9.1–11.5) 1060 (21.3%) 9.6 (8.8–10.4) Undisclosed 415 (16.8%) 7.1 (6.2–8.2) 384 (15.3%) 6.8 (5.8–7.8) 799 (16%) 7 (6.3–7.7) Household size 1 357 (14.4%) 5.2 (4.5–6) 355 (14.2%) 5.9 (4.9–6.9) 712 (14.3%) 5.5 (4.9–6.2) 2 745 (30.1%) 5.7 (5–6.5) 844 (33.7%) 6.5 (5.7–7.2) 1589 (31.9%) 6.1 (5.6–6.6) 3 732 (29.6%) 8.2 (7.3–9.1) 677 (27%) 8.5 (7.6–9.3) 1409 (28.3%) 8.3 (7.7–8.9) 4 502 (20.3%) 9.1 (8.2–10.1) 492 (19.6%) 9.6 (8.7–10.6) 994 (20%) 9.3 (8.7–10.1) 5+ 138 (5.6%) 10.5 (8.7–12.6) 137 (5.5%) 11.1 (9.3–13.1) 275 (5.5%) 10.8 (9.4–12.2) COVID-19 vaccination status None 218 (8.8%) 6.8 (5.7–8.1) 212 (8.5%) 6.4 (5.3–7.6) 430 (8.6%) 6.6 (5.8–7.5) 1 dose 23 (0.9%) 7.2 (4.6–10.4) 28 (1.1%) 7.9 (5.1–11) 51 (1%) 7.6 (5.6–9.7) 2 doses 290 (11.7%) 8.7 (7.4–10.2) 310 (12.4%) 8.6 (7.4–10) 600 (12.1%) 8.7 (7.7–9.7) 3 + doses 1839 (74.3%) 7 (6.6–7.6) 1880 (75%) 7.6 (7.1–8.1) 3719 (74.7%) 7.3 (7–7.7) Exempt 104 (4.2%) 9.3 (7.6–11.2) 75 (3%) 13 (9.8–16.7) 179 (3.6%) 10.9 (9.2–12.8) Table 2 Survey respondents’ sample and average contacts by wave . The table reports the number of respondents included in the survey (and their population percentage with respect to the number of respondents per wave) and respondents' average number of social contacts (and 95% credible interval) stratified by additional respondent characteristics: educational attainment, employment status, working mode (e.g. remotely or in-presence), school attendance (e.g. distance or in-presence learning). Average and Confidence Intervals (CI) are computed through 10,000 bootstrap iterations sampling with replacement from the set of respondents within each variable group. Variables March 2022 wave March 2023 wave Both waves Sample size Avg. Contacts (95% CI) Sample size Avg. Contacts (95% CI) Sample size Avg. Contacts (95% CI) Educational attainment Lower secondary or below 324 (13.1%) 6.5 (5.4–7.6) 329 (13.1%) 6.5 (5.4–7.6) 653 (13.1%) 6.5 (5.7–7.2) Upper secondary 1380 (55.8%) 6.8 (6.3–7.4) 1377 (55%) 7.3 (6.8–7.8) 2757 (55.4%) 7.1 (6.7–7.4) Tertiary or above 770 (31.1%) 8.5 (7.8–9.4) 799 (31.9%) 9.2 (8.3–10.1) 1569 (31.5%) 8.9 (8.3–9.5) Employment status Employed (full or part-time) 1103 (44.6%) 7.6 (7–8.3) 1199 (47.9%) 8.5 (7.9–9.3) 2302 (46.2%) 8.1 (7.6–8.6) Looking after home/family 179 (7.2%) 4 (3.4–4.6) 204 (8.1%) 5.8 (4.9–7) 383 (7.7%) 5 (4.4–5.6) Student (full- or part-time) 378 (15.3%) 12.6 (11.3–13.9) 253 (10.1%) 14 (12.3–15.8) 631 (12.7%) 13.1 (12.1–14.2) Retired 272 (11%) 5.6 (4.4–7.1) 216 (8.6%) 4.9 (4.1–5.8) 488 (9.8%) 5.3 (4.5–6.2) Inactive 542 (21.9%) 5 (4.3–5.8) 633 (25.3%) 5.4 (4.8–6.1) 1175 (23.6%) 5.2 (4.8–5.7) Working mode (only employed respondents during weekdays) Remote or did not attend 195 (25.1%) 4.9 (4.1–5.7) 218 (26.5%) 5.6 (4.6–6.8) 413 (25.8%) 5.2 (4.6–6) In-person 582 (74.9%) 8.5 (7.7–9.4) 604 (73.5%) 10.2 (9.2–11.3) 1186 (74.2%) 9.4 (8.7–10.1) School attendance (only student respondents during weekdays) Remote or did not attend 72 (26.4%) 8.4 (5.9–11.3) 29 (13.9%) 7.8 (5.2–11) 101 (21%) 8.2 (6.2–10.5) In-person 201 (73.6%) 15.7 (14.1–17.4) 179 (86.1%) 16.8 (14.6–19.1) 380 (79%) 16.2 (14.8–17.6) The sample across the two waves included 127 children of preschool age (0–5 years), 64.4% of whom were enrolled in early childhood education services (ISCED level 0 27 ), and 312 individuals aged between 6 and 17 years, all of which were enrolled primary and secondary education (ISCED levels between 1 and 3). Among participants aged 18 or older (3304, representing 88.3% of respondents), 53.0% were employed either full-time or part-time, 7.7% were looking after the home or family, 4.3% were students (full-time or part-time), 25.0% were retired, and 9.9% were inactive. A total of 37,584 contacts were reported in the two study waves, of which 23,718 (63%) were direct and 13,866 (37%) were indirect (see Tables SI6, SI7, and SI8 for a detailed breakdown of direct and indirect contacts). Overall, 95.3% of the responses reported at least one contact on the previous day. Most of the contacts (90.8%) were reported in indoor settings, primarily at home (58.7% of total contacts). Contacts at school or work accounted for 16% of reported contacts, leisure social contacts for 13.1%, contacts on transportation means for 1.6%, and 10.6% of contacts were reported in other non-specified settings (see Table SI8). The mean number of reported daily contacts per respondent across the two waves was 7.5 (95% bootstrapped Confidence Interval (CI) of the mean: 7.2–7.9) and was highly heterogeneous by age group, educational attainment and household size (Table 1 ). Significant differences were found with respect to employment status and attendance at work/school (Table 2 ). Among adult respondents who were employed, those who worked in person reported a higher number of social contacts, 9.4 (95%CI: 8.7–10.1), compared to those who worked remotely or did not work, 5.2 (95%CI: 4.6-6.0). Attendance to work was similar across the two waves (25.1% of the sample reported working remotely or not working in the 2022 wave and 26.5% in the 2023 wave). Similarly, students attending schools and universities in person reported a higher number of contacts, 16.2 (95%CI: 14.8–17.6), compared to those who did distance learning or did not attend school at all, 6.8 (95%CI: 6.5–7.2). During the 2022 wave, 73.6% of students attended school or university in person, compared to 86.1% among students from the 2023 wave. Determinants of social contacts We modelled the total number of contacts as a function of the covariates measured in the survey using a generalised linear mixed effects model (GLMM) with negative binomial distribution and log-link function (see Methods and Sec. SI1.3). Two separate models were fitted to data from adults and minors to account for the different available covariates (see Sec. SI2.1). Among adults, the main factors contributing to a higher number of contacts were in-person attendance to work or school, living in larger households, younger age, and having completed the primary cycle (i.e., at least two doses) of the COVID-19 vaccine (Fig. 1 ). Adults who attended school or work in person reported 1.69 (95%CI: 1.56–1.84) times the contacts of those who attended remotely or did not attend. Adults who lived in households of size 4 reported 1.50 (95%CI: 1.32–1.71) times the contacts of individuals living alone. Individuals of age 40 or more reported significantly fewer contacts than individuals in the age group 18–29, with a contact rate ratio ranging, on average, between 0.70 (95%CI: 0.60–0.81) in the age group 40–49 and 0.81 (95%CI: 0.69–0.95) in the age group 60–69. Respondents who received at least two COVID-19 vaccine doses reported 1.26 (95%CI: 1.11–1.42) times the contacts of unvaccinated or incompletely vaccinated respondents. Weaker but significant effects were estimated for educational attainment, income levels, and the presence of a cohabitant with underlying conditions. Small but significant differences were also found between first- and second-wave respondents, with the latter reporting 1.11 (95%CI: 1.06–1.17) times the contacts of the former. Consistently with results for the adult population, in-presence attendance to school was the most important determinant of the number of contacts also among minors, with in-person attendees reporting 2.38 (95%CI: 1.98–2.87) times the contacts of students not attending (see Figure SI4). We ran a sensitivity analysis where we considered, for both minors and adults, only direct contacts as an alternative outcome variable. Results show that in-person school/work attendance was the most important determinant of the number of contacts also in this case (see Sec. SI2.3). Effect of in-person school and work attendance on viral transmissibility Based on data collected across the two waves, we estimated a contact matrix by age, representing the mean number of daily contacts that an individual of age \(\:i\) has with individuals of age \(\:j\) (Fig. 2 A). Data were also disaggregated by respondents who attended school or worked in person (Fig. 2 B) and by those who did not (Fig. 2 C). Individuals who attended school or work in person are characterised by an overall higher number of interactions across all age groups, and by a higher level of assortative mixing in younger age groups, i.e. the tendency to interact with groups of similar age (top panels in Fig. 2 ). We used the contact matrices disaggregated by attendance in person to assess the impact of work-from-home and distance learning in reducing the transmission potential of a generic respiratory virus, in the absence of other measures. We considered different scenarios combining various levels of in-person work and school attendance 11 , 27 , and we quantified the relative change in reproduction numbers compared to a baseline scenario with full in-person attendance. The considered scenarios reflect the progressive physical closure of education levels, starting from tertiary education (ISCED 5–8) and progressively including lower levels, combined with three levels of in-person attendance at work: i) “pre-pandemic”, corresponding to data before 2020; ii) “sustainable”, reflecting the proportion of in-person workers observed in Italy after the reopening of economic activities following the COVID-19 lockdown on May 18, 2020; and iii) “minimum”, where only essential workers are allowed to work in person, as during the 2020 national lockdown. The age-specific populations of students enrolled in the different education levels are reported in Fig. 3 A, and those of in-person workers in the three scenarios are reported in Fig. 3 B. Using measures targeting only work attendance, we estimated a maximum reduction in transmissibility of 7.0% (95%CI: 4.8- 9.0%) compared to the baseline scenario (Fig. 3 C) when only essential workers are allowed to attend in person. A transmissibility reduction of about 3% would result from a “sustainable” scenario where only limited work-from-home mandates are put in place on non-essential workers 11 . Adding to this scenario the progressive suspension of in-person education services would result in transmissibility reductions of: 5.0% (95%CI: 3.2–6.8%) when tertiary education alone is suspended, (ISCED 5–8); 11.9% (95%CI: 7.8–16.4%) when including the upper secondary (ISCED 3); 16.2% (95%CI: 10.2–22.4%) with lower secondary (ISCED 2); 23.7% (95%CI: 18.2–29.0%) with primary education (ISCED 1); and 24.9% (95%CI:19.1–30.4%) with early childhood education (ISCED 0). The maximum transmissibility reduction, achievable by also limiting attendance in-person to only essential workers in addition to suspending all in-person schooling, was estimated to be 30.3% (95% CI: 22.6–35.9%) (see Fig. 3 C for detailed results on transmissibility reduction). Discussion In this study, we analysed data from a social contact survey conducted in Italy in two waves. The first wave took place in March 2022, immediately after the surge and decline of the Omicron variant 28 , 29 . The second wave was conducted in March 2023, several months after all restrictions had been lifted 30 and a month before the declaration of the end of the COVID-19 Public Health Emergency of International Concern. Respondents in the second wave reported a slight but statistically significant increase in the number of social contacts compared to the first wave, which may be attributed to the relaxation of the remaining restrictions and spontaneous behaviour change (see Figure SI13 for contact matrices differences between the two waves when accounting for in-person attendance). Similarly to a previous study run in late 2022 in the UK, Belgium and the Netherlands, we found that the mean number of contacts in Italy in 2022/2023 has increased compared to those recorded in 2021 31–33 (Table SI3 and SI4). In line with other recent contact studies, we identified a positive association between the number of reported contacts and higher education levels, higher income, larger household size, lower age and COVID-19 vaccination 34 – 36 . Importantly, we found that the strongest determinant of the total number of contacts was in-person attendance at work or school. Other contact studies have found a similar relationship between workers and non-workers, but our focus on work-from-home and distance learning on the number of contacts allowed us to provide quantitative insights on potential governmental interventions acting on work/school in-person attendance. We quantified the reduction in transmission potential of a respiratory virus transmitted through direct contacts or shared closed spaces that could be allowed by combinations of school closures and working-from-home mandates (or the suspension of non-essential economic activities altogether). In agreement with previous findings, we found that suspending in-person education has a generally stronger impact on transmissibility (up to 20% when applied to all education levels in the absence of measures on work attendance) 37 , 38 than reducing in-person workforce (up to 7% when all non-essential productive sectors are suspended). A combination of both interventions at the maximum level (similar to what was implemented during the Italian lockdown in March-April 2020) is expected to contribute to a reduction of the transmissibility of up to 30% in the absence of other preventive measures such as mask use, isolation of diagnosed individuals, tracing and quarantining of contacts, ventilation of closed spaces, restrictions on other social contacts, and spontaneous protective behaviour. The suspension of non-essential economic activities always had a limited (< 5%) additional effect on further reducing transmission when compared to sustainable work-from-home mandates. Distance learning is a highly debated measure due to its implications on the quality of education and psychological well-being of children and young adults and on the increased burden on parents who need to rebalance childcare with their work responsibilities. In particular, closing lower educational levels poses stronger challenges to families, since young children engage less effectively in distance learning 39 and require a higher intensity of care. In this context, a relevant finding of this study is that maintaining in-person attendance for early childhood education (children aged 0–5 years) minimally affects the effectiveness of intervention. We acknowledge, however, that since questionnaires for young children were compiled by their legal guardians, they may be more prone to biases due to second-hand reporting of the number of contacts; therefore, we advise caution in the interpretation of this result. In the context of social contact studies for epidemiological modelling, special attention needs to be placed on the definition of contacts. Traditional definitions, such as those proposed by Mossong et al. 13 or Coletti et al. 19 , emphasise the importance of physical and conversational interactions. However, COVID-19 highlighted the importance of airborne transmission in shared indoor spaces 40 – 42 . To account for this, we included indirect contacts due to co-location in closed spaces within the definition of contact in the main analysis. One limitation of this choice is that details on the contact (e.g., their age) would be difficult to recall for all co-located contacts (for example, customers attending the same restaurant or bar) given the lack of personal interaction and their potentially large number. Therefore, we chose not to collect information details on indirect contacts to avoid long survey completion times resulting in high dropout rates or low-quality responses. Information on the age of indirect contacts was inferred based on the details provided for direct contacts. Nonetheless, a sensitivity analysis where we considered only direct contacts provided consistent results with those of the main analysis (reported in the Supplementary Information). The impact of work-from-home and distance learning measures estimated here considered the spread of a generic pathogen through close contacts and airborne transmission in a fully susceptible population, neglecting, for example, possible age-specific heterogeneities in susceptibility and infectiousness. As such, results may need to be recalibrated to the features of the actual pathogen for which such measures are taken into consideration. However, data collected and made available through this study make such a reassessment relatively straightforward once the characteristics of the pathogen are known. Although we believe that qualitative results from this study may hold for countries with similar socio-demographic and economic structures, we caution against their direct extrapolation to other geographical settings, given heterogeneities in educational systems, workplace structures and social interactions at work, household composition, age-specific population size and contact patterns. This research provides data across multiple sociodemographic strata on social contact patterns in Italy after the COVID-19 emergency, and analytics identifying the effect of socio-economic and demographic determinants on the number of experienced social contacts. These results were combined with data on school enrolment and in-person work attendance to provide estimates of the potential impact of public health interventions involving the educational and productive sectors. Estimates of this kind can support considerations on the balance between the expected epidemiological benefits and their societal costs. Materials and Methods Data and data cleaning This work is based on a new sample of data collected online in two waves at the end of March 2022 and 2023, from a nationally representative panel of the Italian population in terms of age, sex, and area of residence. Except for minor updates (e.g. for the number of vaccine doses recommended to the population in 2023), the second wave was identical to the first and administered to a random group of respondents who already participated in the first wave (1,246 participants), and to a group of first-time respondents of approximately equal size. The survey consists of two main sections: i) socio-demographics, health-related information and behavioural information on the respondent, and ii) a contact diary in which respondents were asked to recall their direct and indirect contacts on the day prior to the survey administration. After data collection, responses underwent an accurate data-cleaning process including: i) removal of respondents with inconsistencies in responses, and ii) removal of respondents with incomplete information. A complete description of the data-cleaning procedure is provided in Sec. SI1.1. Indirect co-location events augmentation procedure For each direct contact, additional information was collected about the characteristics of the interaction and information on contacts themselves. However, indirect contacts were only reported as an aggregate number. To run the statistical model and construct age-specific contact matrices it was thus necessary to augment indirect contact data with the relevant missing information. The data augmentation procedure used in the baseline analysis can be summarized as follows. For each respondent: we defined the number of indirect contacts to be augmented (N), by subtracting from the reported count for indirect contacts the number of indoor direct contacts that they reported. This assumption conservatively accounts for contacts potentially reported both as direct indoor and as indirect. we reconstructed the age and setting of indirect contacts by sampling N times (with replacement) this information from the set of direct contacts reported by the same participant, excluding cohabitants and outdoor contacts. if the respondent did not report direct contacts, indirect contacts data augmentation was performed by sampling the information from direct contacts of other participants of the same age as the respondent. We performed a sensitivity analysis on the data augmentation procedure by limiting the sampling for the assignment of indoor contacts only to the setting in which the respondent reported most of their direct contacts, rather than sampling from the overall direct contacts. See Section SI1.2 for more details. Determinants of social contacts: the statistical framework To investigate the determinants of the total number of contacts, we fitted a generalised linear mixed model 43 . We initially screened 35 covariates relative to the respondent and cohabitant’s characteristics for significant associations with the total number of contacts, fitting a set of negative binomial regression models. We built these models using as independent variables each individual covariate, an intercept, and a term accounting for the different data collection waves (2022 or 2023) (see Table SI5 for a full list of the screened covariates). Covariates that were not significant in the multivariate model were subsequently filtered out, and the final model included 15 covariates: sex, age group aggregated by 10-year intervals, occupation, household income, household size, in-presence attendance at work/school, contact happened on a Sunday, completion of SARS-CoV-2 primary vaccination cycle, recent (in the last 4 months) SARS-CoV-2 infection, presence of chronic comorbidities in the respondent, presence of chronic comorbidities in cohabitants, senior (65 years or older) cohabitants, having children, and participation to the first survey wave. Finally, a random intercept accounted for within-individual correlation in longitudinal responses across the two waves. Full details on the generalised linear mixed model selection are reported in SI1.3. Age-specific contact matrices We constructed age-specific matrices of average total contacts for the Italian population stratified by 15 age groups (0–4, 5–9, 10–14, 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, and 70 + years old). We then computed the population normalised reciprocal contact matrix, following established procedures from the literature 8 , 13 , 23 . The matrices for the average total contacts obtained by 1000-fold bootstrapping of responses from both waves are reported in Fig. 2 (see also Figure SI11 for robustness). In the SI, we report contact matrices disaggregated by setting (Figures SI5-7) and matrices including only direct social contacts (Figure SI9). Effect of school and work attendance on the transmissibility of a generic respiratory virus We evaluated the potential effect of different scenarios of school and work attendance on the transmissibility of a novel respiratory virus (i.e., spreading in a fully susceptible population in the absence of interventions) under the assumption of homogeneous susceptibility and infectiousness across age groups. To do so, we combined data from Eurostat on age-specific populations attending different education levels 27 (from early childhood to tertiary education) and on age-specific in-person workforce in Italy from a previously published study 11 . We denote by \(\:{R}_{s}\) the reproduction number associated with scenario s and compute the relative reduction \(\:{\alpha\:}_{s}\) with respect to the baseline scenario with full school and work attendance: $$\:{\alpha\:}_{s}=1-\frac{{R}_{s}}{{R}_{0}}=1-\frac{\rho\:\left({NGM}_{s}\right)}{\rho\:\left({NGM}_{0}\right)}$$ where \(\:\:\rho\:\left({NGM}_{s}\right)\) and \(\:\rho\:\left({NGM}_{0}\right)\) represent the dominant eigenvalues of the next-generation matrices associated with scenario s and with the baseline scenario, respectively 11 , 44 , 45 . Since infection-related parameters are the same across scenarios, \(\:{\alpha\:}_{s}\) can be simplified to: $$\:{\alpha\:}_{s}=1-\frac{\rho\:\left({M}_{s}\right)}{\rho\:\left({M}_{0}\right)}$$ where \(\:{M}_{s}\) is a block-matrix $$\:{M}_{s}=\:\left(\begin{array}{cc}{A}_{i,j}^{s}&\:{A}_{i,j}^{s}\\\:{B}_{i,j}^{s}&\:{B}_{i,j}^{s}\end{array}\right)$$ The blocks are defined as \(\:{A}_{i,j}^{s}=\) \(\:{C}_{i,j}^{P}\) \(\:\frac{{N}_{i}^{P}\left(s\right)}{{N}_{j}^{P}\left(s\right)+{N}_{j}^{NP}\left(s\right)}\) and \(\:{B}_{i,j}^{s}=\) \(\:{C}_{i,j}^{NP}\) \(\:\frac{{N}_{i}^{NP}\left(s\right)}{{N}_{j}^{P}\left(s\right)+{N}_{j}^{NP}\left(s\right)}\) with \(\:{C}_{i,j}^{P}\) representing the overall contact matrix estimated for the population attending work or school in-person; \(\:{C}_{i,j}^{NP}\) representing the overall contact matrix estimated for the population not attending work or school in-person; \(\:{N}_{i}^{P}\left(s\right)\) representing the number of individuals of age i attending schools or work in-person in scenario s; \(\:{N}_{i}^{NP}\left(s\right)\) representing the number of individuals of age i not attending schools or work in-person in scenario s. Ethics statement This study was conducted in accordance with the ethical standards set by Bocconi University and has received approval from the Bocconi University Ethical Board (Approval Number: FA000383–17 January 2022). Participants were informed about the nature and purpose of the research, including the voluntary nature of their participation and their right to withdraw at any time without any negative consequences. Upon completion, participants received compensation for their time and effort through the survey company. Informed consent was obtained from all participants prior to their involvement in the study. All analyses were carried out on anonymised data. Declarations Acknowledgment & Funding LL, CC, FT, VO, EDA, AM acknowledge funding from the ERC Consolidator Grant IMMUNE (no. 101003183). Researchers from the Bocconi Covid Crisis Lab acknowledge funding from the Romeo and Enrica Invernizzi Foundation. VM, GG, MM, PP, and SM acknowledge funding from the Fondazione Valorizzazione Ricerca Trentina (VRT), project COVIDVAX. This research was supported by EU funding within the NextGenerationEU-MUR M4C2.I.1.3 PNRR Extended Partnership initiative on Emerging Infectious Diseases (PE00000007, INF-ACT) “One Health Basic and Translational Research Actions addressing Unmet Needs on Emerging Infectious Diseases” through the INF-ACT Cascade Open Call 2023 (COC-1-2023-ISS-02) – CUP I83C22001810007. References Perra N (2021) Non-pharmaceutical interventions during the COVID-19 pandemic: A review. Phys Rep 913:1–52 Mendez-Brito A, El Bcheraoui C, Pozo-Martin F (2021) Systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions against COVID-19. J Infect 83:281–293 Parolin Z, Lee EK (2021) Large socio-economic, geographic and demographic disparities exist in exposure to school closures. 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Int J Psychol 56:566–576 Peng Z et al (2022) Practical Indicators for Risk of Airborne Transmission in Shared Indoor Environments and Their Application to COVID-19 Outbreaks. Environ Sci Technol 56:1125–1137 Zhao X, Liu S, Yin Y, Zhang T (Tim), Chen Q Airborne transmission of COVID-19 virus in enclosed spaces (eds) (2022) : An overview of research methods. Indoor Air 32, e13056 Bazant MZ, Bush JW (2021) M. A guideline to limit indoor airborne transmission of COVID-19. Proc. Natl. Acad. Sci. 118, e2018995118 Jiang J, Nguyen T (2021) Linear and Generalized Linear Mixed Models and Their Applications. Springer, New York, NY. 10.1007/978-1-0716-1282-8 Diekmann O, Heesterbeek JAP, Metz JAJ (1990) On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. J Math Biol 28:365–382 Diekmann O, Heesterbeek JaP, Roberts MG (2009) The construction of next-generation matrices for compartmental epidemic models. J R Soc Interface 7:873–885 Iozzi F et al (2010) Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns- Fitting Predicted Matrices to Serological Data. PLOS Comput Biol 6:e1001021 Additional Declarations There is NO Competing Interest. Supplementary Files SocialContactPatternsItalySI20250131.docx Supplementary Information: Post-pandemic social contacts in Italy: implications for social distancing measures on in-person school and work attendance Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":10999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRate ratios for the number of contacts in the adult population associated with respondent’s characteristics.\u003c/em\u003e Rate ratios higher (lower) than one indicate a higher (lower) average number of contacts for the given covariate relative to the reference. Points: mean rate ratio; lines: 95%CI.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6009950/v1/c886da64005b7e133f6a46c3.png"},{"id":94915842,"identity":"219c4043-fe72-46ce-8d47-a490b94e7590","added_by":"auto","created_at":"2025-11-01 11:44:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAge-specific\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003econtact patterns based on survey data from both waves (March 2022 and 2023).\u003c/em\u003e Matrices were constructed by averaging 1,000 bootstrap samples with replacement of survey responses. Each cell represents the mean number of total contacts per individual, stratified by age group pairs. The top panels indicate the mean number of contacts (full line) and the mean age assortativity\u003csup\u003e8,46\u003c/sup\u003e (dashed line) for each age group. A) Contact patterns obtained from the full sample, representative of the Italian population. B) Contact patterns for the subset of individuals attending school or work in person. C) Contact patterns for the subset of individuals not attending schools or working in person.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6009950/v1/db299f4f51c7193266f400cd.png"},{"id":94915847,"identity":"69325a4e-33e4-4f29-a427-643cb6189374","added_by":"auto","created_at":"2025-11-01 11:44:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":397196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTransmissibility reduction under different scenarios of work/school attendance.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eA) Age-specific population enrolled to different educational levels in Italy in 2020, according to the ISCED classification 2011\u003csup\u003e27\u003c/sup\u003e. B) Age-specific population working in person in Italy under the three considered scenarios \u003csup\u003e20\u003c/sup\u003e: baseline (corresponding to pre-pandemic estimates); sustainable (corresponding to the population after the reopening of all economic activities following the end of the COVID-19 lockdown in May 2020); minimum (corresponding to in-person workers estimated during the COVID-19 lockdown]). C) Relative reduction in transmissibility associated with intervention scenarios combining different levels of school closures (including distance-learning mandates) and in-person work, compared to a baseline scenario where the in-person workers are set at pre-pandemic levels\u003csup\u003e11\u003c/sup\u003e and students across all education levels attend in person.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6009950/v1/a23e4087ce6561fb4356ab02.jpeg"},{"id":94990923,"identity":"df5579fa-b45e-463a-a4aa-7ae58744d924","added_by":"auto","created_at":"2025-11-03 07:18:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1562081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6009950/v1/84ba9a2a-892b-410d-b277-c6fe0fea132e.pdf"},{"id":94915846,"identity":"5a2a4c77-1552-4d4f-8027-c35e44ee1b0f","added_by":"auto","created_at":"2025-11-01 11:44:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5058773,"visible":true,"origin":"","legend":"Supplementary Information: Post-pandemic social contacts in Italy: implications for social distancing measures on in-person school and work attendance","description":"","filename":"SocialContactPatternsItalySI20250131.docx","url":"https://assets-eu.researchsquare.com/files/rs-6009950/v1/2a70daaa4aaa82640a713c48.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Post-pandemic social contacts in Italy: implications for distancing measures on in-person school and work attendance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRespiratory infectious diseases are spread in human populations through interactions associated with social contacts, occurring during the execution of everyday life activities. During severe health emergencies caused by respiratory infectious agents, such as pandemics, the limitation in the number of social contacts enacted through governmental mandates and recommendations (sometimes referred to as \u0026ldquo;social distancing\u0026rdquo; measures) can be an effective means to interrupt the chains of transmission and rapidly curb the disease spread \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, such measures may come at extensive economic and societal costs. Among the many measures aimed at reducing the transmission of SARS-CoV-2, school closures have been widely debated\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e amidst concerns about their negative short- and long-term impact on young individuals' education and psychological well-being\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In addition to this, the suspension of most economic activities during the lockdown enacted by many countries in the spring of 2020 has resulted in massive economic damage, disruption of supply chains, and job losses\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, exacerbating poverty and inequality. To properly weigh the costs and benefits of social distancing measures, a crucial step is to quantify their impact on transmission. This difficult task is usually addressed through mathematical modelling, which largely relies on the availability of information on how many social contacts individuals experience across different socio-demographic stratifications, social settings and activities\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Although such information was available, to some extent, for most countries at the onset of the COVID-19 pandemic\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, the individuals' behavioural responses and imposed restrictions\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e made it somewhat outdated\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Several social contact studies were conducted in selected countries to assess changes in human mixing patterns throughout the course of the epidemics \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, as COVID-19-related restrictions have progressively been lifted in most countries starting in 2022\u003csup\u003e16,21\u003c/sup\u003e, comprehensive knowledge of post-pandemic contact patterns is still missing\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Here, we provide updated evidence on contact patterns in Italy obtained by internet-based surveys conducted in March 2022 and March 2023. We combine the collected contact pattern data with data on educational demographics and age-specific in-person workforce participation to quantify the potential impact of targeted governmental restrictions on the transmissibility of a novel respiratory pathogen.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe conducted two waves of the same online social contact survey on a sample representative of the age, sex, and area of residence (NUTS1 level, i.e. major socio-economic regions\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e) of the Italian population. The first wave covered the period from March 17 to March 29, 2022, and the second from March 17 to April 5, 2023. Participants were asked to provide their socio-demographic and health-related information and to report both \u0026ldquo;direct\u0026rdquo; and \u0026ldquo;indirect\u0026rdquo; contacts encountered on the day before the survey. A direct contact was defined as a person with whom physical interactions (e.g. a handshake, a pat on the back, a hug, a kiss, etc.) and/or in-person verbal exchanges of at least five words occurred. Participants were asked to provide details on each of their direct contacts, such as their age, sex, or the setting in which the interaction occurred. Participants were additionally asked to estimate the number of indirect contacts encountered on the previous day. An indirect contact was defined as a person with whom the respondent was co-located in a closed environment for at least 30 minutes (e.g., classmates at school or colleagues at work). For the main analysis in this study, we defined a contact as any direct or indirect contact (see Materials and Methods). The survey was administered to both adults (18 or more years old) and minors (less than 18 years old). Participants aged 14 to 17 years were requested to compile the questionnaire under the supervision of a legal guardian (usually a parent); for participants under 14, the compilation was performed by a legal guardian and the active engagement of the minor participant was strongly encouraged to ensure accurate responses to the questions.\u003c/p\u003e \u003cp\u003eThe final sample included 3,743 participants producing 4,979 valid questionnaires, of which 2,474 were from the 2022 wave and 2,505 from the 2023 wave. A subset of 1,236 participants responded to both the 2022 and 2023 waves. Summary descriptions of the participants\u0026rsquo; characteristics and the corresponding mean numbers of contacts are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Full details on the collected information are reported in the Supplementary Information.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSurvey respondents\u0026rsquo; characteristics and average contacts by wave.\u003c/em\u003e The table reports the number of respondents (and the percentage with respect to the number of respondents in each wave) and respondents' average number of social contacts (and 95% credible interval) stratified by different respondent characteristics: age, sex, household income, household size and COVID-19 vaccination status. Average and Confidence Intervals (CI) are computed through 10,000 bootstrap iterations sampling with replacement from the set of respondents within each demographic group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMarch 2022 wave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMarch 2023 wave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eBoth waves\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSample size\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAvg. Contacts\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSample size\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAvg. Contacts\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSample size\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAvg. Contacts\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTotal\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3 (6.9\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.8 (7.4\u0026ndash;8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.5 (7.2\u0026ndash;7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAge group\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1 (9.5\u0026ndash;12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.6 (10.5\u0026ndash;14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e262 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.7 (10.5\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7 (10.9\u0026ndash;14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.2 (12.5\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e268 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.6 (12.1\u0026ndash;15.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4 (8.5\u0026ndash;12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.8 (8.8\u0026ndash;13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e387 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.6 (9.2\u0026ndash;12.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e376 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (6.9\u0026ndash;9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e357 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.8 (7.6\u0026ndash;10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e733 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.4 (7.6\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e381 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (5.1\u0026ndash;7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e339 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7 (5.8\u0026ndash;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e720 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.4 (5.7\u0026ndash;7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e432 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3 (5.4\u0026ndash;7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e506 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.4 (6.4\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e938 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.9 (6.2\u0026ndash;7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 (5.4\u0026ndash;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.3 (5.3\u0026ndash;7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e678 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.4 (5.6\u0026ndash;7.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e448 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8 (4.2\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7 (5\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e993 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.3 (4.8\u0026ndash;5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSex\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1248 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5 (6.9\u0026ndash;8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1272 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.2 (7.6\u0026ndash;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2520 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.9 (7.4\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1226 (49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1 (6.6\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1233 (49.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.3 (6.8\u0026ndash;7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2459 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.2 (6.8\u0026ndash;7.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNet household income (euros per month)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e583 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8 (5.1\u0026ndash;6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e568 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.3 (5.5\u0026ndash;7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1151 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.1 (5.5\u0026ndash;6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1,500-2,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e967 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5 (6.8\u0026ndash;8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1002 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.6 (7\u0026ndash;8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1969 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.5 (7.1\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e509 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9 (8\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e551 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2 (9.1\u0026ndash;11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1060 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.6 (8.8\u0026ndash;10.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndisclosed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1 (6.2\u0026ndash;8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e384 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.8 (5.8\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e799 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 (6.3\u0026ndash;7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHousehold size\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e357 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2 (4.5\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e355 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9 (4.9\u0026ndash;6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e712 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.5 (4.9\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e745 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7 (5\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e844 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5 (5.7\u0026ndash;7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1589 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.1 (5.6\u0026ndash;6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e732 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2 (7.3\u0026ndash;9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e677 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5 (7.6\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1409 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.3 (7.7\u0026ndash;8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e502 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.1 (8.2\u0026ndash;10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e492 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6 (8.7\u0026ndash;10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e994 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.3 (8.7\u0026ndash;10.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5 (8.7\u0026ndash;12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.1 (9.3\u0026ndash;13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e275 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.8 (9.4\u0026ndash;12.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCOVID-19 vaccination status\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8 (5.7\u0026ndash;8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.4 (5.3\u0026ndash;7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e430 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.6 (5.8\u0026ndash;7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2 (4.6\u0026ndash;10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.9 (5.1\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.6 (5.6\u0026ndash;9.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 doses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.7 (7.4\u0026ndash;10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.6 (7.4\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e600 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.7 (7.7\u0026ndash;9.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;+\u0026thinsp;doses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1839 (74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (6.6\u0026ndash;7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1880 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.6 (7.1\u0026ndash;8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3719 (74.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.3 (7\u0026ndash;7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExempt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.3 (7.6\u0026ndash;11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (9.8\u0026ndash;16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e179 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.9 (9.2\u0026ndash;12.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSurvey respondents\u0026rsquo; sample and average contacts by wave\u003c/em\u003e. The table reports the number of respondents included in the survey (and their population percentage with respect to the number of respondents per wave) and respondents' average number of social contacts (and 95% credible interval) stratified by additional respondent characteristics: educational attainment, employment status, working mode (e.g. remotely or in-presence), school attendance (e.g. distance or in-presence learning). Average and Confidence Intervals (CI) are computed through 10,000 bootstrap iterations sampling with replacement from the set of respondents within each variable group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMarch 2022 wave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMarch 2023 wave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eBoth waves\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSample size\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAvg. Contacts\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSample size\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAvg. Contacts\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSample size\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAvg. Contacts\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEducational attainment\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower secondary or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 (5.4\u0026ndash;7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e329 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5 (5.4\u0026ndash;7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e653 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.5 (5.7\u0026ndash;7.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1380 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8 (6.3\u0026ndash;7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1377 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.3 (6.8\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2757 (55.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.1 (6.7\u0026ndash;7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e770 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5 (7.8\u0026ndash;9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e799 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2 (8.3\u0026ndash;10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1569 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.9 (8.3\u0026ndash;9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEmployment status\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed (full or part-time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1103 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.6 (7\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1199 (47.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5 (7.9\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2302 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.1 (7.6\u0026ndash;8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLooking after home/family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.4\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8 (4.9\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e383 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (4.4\u0026ndash;5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent (full- or part-time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.6 (11.3\u0026ndash;13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (12.3\u0026ndash;15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e631 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.1 (12.1\u0026ndash;14.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6 (4.4\u0026ndash;7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e216 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9 (4.1\u0026ndash;5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e488 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.3 (4.5\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e542 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4.3\u0026ndash;5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e633 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.4 (4.8\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1175 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.2 (4.8\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWorking mode (only employed respondents during weekdays)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemote or did not attend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9 (4.1\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e218 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6 (4.6\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e413 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.2 (4.6\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e582 (74.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5 (7.7\u0026ndash;9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e604 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2 (9.2\u0026ndash;11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1186 (74.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.4 (8.7\u0026ndash;10.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSchool attendance (only student respondents during weekdays)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemote or did not attend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.4 (5.9\u0026ndash;11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.8 (5.2\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.2 (6.2\u0026ndash;10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201 (73.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.7 (14.1\u0026ndash;17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (86.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.8 (14.6\u0026ndash;19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e380 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.2 (14.8\u0026ndash;17.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe sample across the two waves included 127 children of preschool age (0\u0026ndash;5 years), 64.4% of whom were enrolled in early childhood education services (ISCED level 0\u003csup\u003e27\u003c/sup\u003e), and 312 individuals aged between 6 and 17 years, all of which were enrolled primary and secondary education (ISCED levels between 1 and 3). Among participants aged 18 or older (3304, representing 88.3% of respondents), 53.0% were employed either full-time or part-time, 7.7% were looking after the home or family, 4.3% were students (full-time or part-time), 25.0% were retired, and 9.9% were inactive.\u003c/p\u003e \u003cp\u003eA total of 37,584 contacts were reported in the two study waves, of which 23,718 (63%) were direct and 13,866 (37%) were indirect (see Tables SI6, SI7, and SI8 for a detailed breakdown of direct and indirect contacts). Overall, 95.3% of the responses reported at least one contact on the previous day. Most of the contacts (90.8%) were reported in indoor settings, primarily at home (58.7% of total contacts). Contacts at school or work accounted for 16% of reported contacts, leisure social contacts for 13.1%, contacts on transportation means for 1.6%, and 10.6% of contacts were reported in other non-specified settings (see Table SI8).\u003c/p\u003e \u003cp\u003eThe mean number of reported daily contacts per respondent across the two waves was 7.5 (95% bootstrapped Confidence Interval (CI) of the mean: 7.2\u0026ndash;7.9) and was highly heterogeneous by age group, educational attainment and household size (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Significant differences were found with respect to employment status and attendance at work/school (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among adult respondents who were employed, those who worked in person reported a higher number of social contacts, 9.4 (95%CI: 8.7\u0026ndash;10.1), compared to those who worked remotely or did not work, 5.2 (95%CI: 4.6-6.0). Attendance to work was similar across the two waves (25.1% of the sample reported working remotely or not working in the 2022 wave and 26.5% in the 2023 wave). Similarly, students attending schools and universities in person reported a higher number of contacts, 16.2 (95%CI: 14.8\u0026ndash;17.6), compared to those who did distance learning or did not attend school at all, 6.8 (95%CI: 6.5\u0026ndash;7.2). During the 2022 wave, 73.6% of students attended school or university in person, compared to 86.1% among students from the 2023 wave.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDeterminants of social contacts\u003c/h2\u003e \u003cp\u003eWe modelled the total number of contacts as a function of the covariates measured in the survey using a generalised linear mixed effects model (GLMM) with negative binomial distribution and log-link function (see Methods and Sec. SI1.3). Two separate models were fitted to data from adults and minors to account for the different available covariates (see Sec. SI2.1). Among adults, the main factors contributing to a higher number of contacts were in-person attendance to work or school, living in larger households, younger age, and having completed the primary cycle (i.e., at least two doses) of the COVID-19 vaccine (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Adults who attended school or work in person reported 1.69 (95%CI: 1.56\u0026ndash;1.84) times the contacts of those who attended remotely or did not attend. Adults who lived in households of size 4 reported 1.50 (95%CI: 1.32\u0026ndash;1.71) times the contacts of individuals living alone. Individuals of age 40 or more reported significantly fewer contacts than individuals in the age group 18\u0026ndash;29, with a contact rate ratio ranging, on average, between 0.70 (95%CI: 0.60\u0026ndash;0.81) in the age group 40\u0026ndash;49 and 0.81 (95%CI: 0.69\u0026ndash;0.95) in the age group 60\u0026ndash;69. Respondents who received at least two COVID-19 vaccine doses reported 1.26 (95%CI: 1.11\u0026ndash;1.42) times the contacts of unvaccinated or incompletely vaccinated respondents. Weaker but significant effects were estimated for educational attainment, income levels, and the presence of a cohabitant with underlying conditions. Small but significant differences were also found between first- and second-wave respondents, with the latter reporting 1.11 (95%CI: 1.06\u0026ndash;1.17) times the contacts of the former. Consistently with results for the adult population, in-presence attendance to school was the most important determinant of the number of contacts also among minors, with in-person attendees reporting 2.38 (95%CI: 1.98\u0026ndash;2.87) times the contacts of students not attending (see Figure SI4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe ran a sensitivity analysis where we considered, for both minors and adults, only direct contacts as an alternative outcome variable. Results show that in-person school/work attendance was the most important determinant of the number of contacts also in this case (see Sec. SI2.3).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffect of in-person school and work attendance on viral transmissibility\u003c/h3\u003e\n\u003cp\u003eBased on data collected across the two waves, we estimated a contact matrix by age, representing the mean number of daily contacts that an individual of age \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e has with individuals of age \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Data were also disaggregated by respondents who attended school or worked in person (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and by those who did not (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Individuals who attended school or work in person are characterised by an overall higher number of interactions across all age groups, and by a higher level of assortative mixing in younger age groups, i.e. the tendency to interact with groups of similar age (top panels in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We used the contact matrices disaggregated by attendance in person to assess the impact of work-from-home and distance learning in reducing the transmission potential of a generic respiratory virus, in the absence of other measures. We considered different scenarios combining various levels of in-person work and school attendance\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and we quantified the relative change in reproduction numbers compared to a baseline scenario with full in-person attendance. The considered scenarios reflect the progressive physical closure of education levels, starting from tertiary education (ISCED 5\u0026ndash;8) and progressively including lower levels, combined with three levels of in-person attendance at work: i) \u0026ldquo;pre-pandemic\u0026rdquo;, corresponding to data before 2020; ii) \u0026ldquo;sustainable\u0026rdquo;, reflecting the proportion of in-person workers observed in Italy after the reopening of economic activities following the COVID-19 lockdown on May 18, 2020; and iii) \u0026ldquo;minimum\u0026rdquo;, where only essential workers are allowed to work in person, as during the 2020 national lockdown. The age-specific populations of students enrolled in the different education levels are reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, and those of in-person workers in the three scenarios are reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing measures targeting only work attendance, we estimated a maximum reduction in transmissibility of 7.0% (95%CI: 4.8- 9.0%) compared to the baseline scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) when only essential workers are allowed to attend in person. A transmissibility reduction of about 3% would result from a \u0026ldquo;sustainable\u0026rdquo; scenario where only limited work-from-home mandates are put in place on non-essential workers\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Adding to this scenario the progressive suspension of in-person education services would result in transmissibility reductions of: 5.0% (95%CI: 3.2\u0026ndash;6.8%) when tertiary education alone is suspended, (ISCED 5\u0026ndash;8); 11.9% (95%CI: 7.8\u0026ndash;16.4%) when including the upper secondary (ISCED 3); 16.2% (95%CI: 10.2\u0026ndash;22.4%) with lower secondary (ISCED 2); 23.7% (95%CI: 18.2\u0026ndash;29.0%) with primary education (ISCED 1); and 24.9% (95%CI:19.1\u0026ndash;30.4%) with early childhood education (ISCED 0). The maximum transmissibility reduction, achievable by also limiting attendance in-person to only essential workers in addition to suspending all in-person schooling, was estimated to be 30.3% (95% CI: 22.6\u0026ndash;35.9%) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC for detailed results on transmissibility reduction).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we analysed data from a social contact survey conducted in Italy in two waves. The first wave took place in March 2022, immediately after the surge and decline of the Omicron variant\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The second wave was conducted in March 2023, several months after all restrictions had been lifted\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and a month before the declaration of the end of the COVID-19 Public Health Emergency of International Concern. Respondents in the second wave reported a slight but statistically significant increase in the number of social contacts compared to the first wave, which may be attributed to the relaxation of the remaining restrictions and spontaneous behaviour change (see Figure SI13 for contact matrices differences between the two waves when accounting for in-person attendance).\u003c/p\u003e \u003cp\u003eSimilarly to a previous study run in late 2022 in the UK, Belgium and the Netherlands, we found that the mean number of contacts in Italy in 2022/2023 has increased compared to those recorded in 2021\u003csup\u003e31\u0026ndash;33\u003c/sup\u003e (Table SI3 and SI4). In line with other recent contact studies, we identified a positive association between the number of reported contacts and higher education levels, higher income, larger household size, lower age and COVID-19 vaccination\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImportantly, we found that the strongest determinant of the total number of contacts was in-person attendance at work or school. Other contact studies have found a similar relationship between workers and non-workers, but our focus on work-from-home and distance learning on the number of contacts allowed us to provide quantitative insights on potential governmental interventions acting on work/school in-person attendance. We quantified the reduction in transmission potential of a respiratory virus transmitted through direct contacts or shared closed spaces that could be allowed by combinations of school closures and working-from-home mandates (or the suspension of non-essential economic activities altogether). In agreement with previous findings, we found that suspending in-person education has a generally stronger impact on transmissibility (up to 20% when applied to all education levels in the absence of measures on work attendance)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e than reducing in-person workforce (up to 7% when all non-essential productive sectors are suspended). A combination of both interventions at the maximum level (similar to what was implemented during the Italian lockdown in March-April 2020) is expected to contribute to a reduction of the transmissibility of up to 30% in the absence of other preventive measures such as mask use, isolation of diagnosed individuals, tracing and quarantining of contacts, ventilation of closed spaces, restrictions on other social contacts, and spontaneous protective behaviour. The suspension of non-essential economic activities always had a limited (\u0026lt;\u0026thinsp;5%) additional effect on further reducing transmission when compared to sustainable work-from-home mandates.\u003c/p\u003e \u003cp\u003eDistance learning is a highly debated measure due to its implications on the quality of education and psychological well-being of children and young adults and on the increased burden on parents who need to rebalance childcare with their work responsibilities. In particular, closing lower educational levels poses stronger challenges to families, since young children engage less effectively in distance learning\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and require a higher intensity of care. In this context, a relevant finding of this study is that maintaining in-person attendance for early childhood education (children aged 0\u0026ndash;5 years) minimally affects the effectiveness of intervention. We acknowledge, however, that since questionnaires for young children were compiled by their legal guardians, they may be more prone to biases due to second-hand reporting of the number of contacts; therefore, we advise caution in the interpretation of this result.\u003c/p\u003e \u003cp\u003eIn the context of social contact studies for epidemiological modelling, special attention needs to be placed on the definition of contacts. Traditional definitions, such as those proposed by Mossong et al.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e or Coletti et al.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, emphasise the importance of physical and conversational interactions. However, COVID-19 highlighted the importance of airborne transmission in shared indoor spaces\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. To account for this, we included indirect contacts due to co-location in closed spaces within the definition of contact in the main analysis. One limitation of this choice is that details on the contact (e.g., their age) would be difficult to recall for all co-located contacts (for example, customers attending the same restaurant or bar) given the lack of personal interaction and their potentially large number. Therefore, we chose not to collect information details on indirect contacts to avoid long survey completion times resulting in high dropout rates or low-quality responses. Information on the age of indirect contacts was inferred based on the details provided for direct contacts. Nonetheless, a sensitivity analysis where we considered only direct contacts provided consistent results with those of the main analysis (reported in the Supplementary Information).\u003c/p\u003e \u003cp\u003eThe impact of work-from-home and distance learning measures estimated here considered the spread of a generic pathogen through close contacts and airborne transmission in a fully susceptible population, neglecting, for example, possible age-specific heterogeneities in susceptibility and infectiousness. As such, results may need to be recalibrated to the features of the actual pathogen for which such measures are taken into consideration. However, data collected and made available through this study make such a reassessment relatively straightforward once the characteristics of the pathogen are known.\u003c/p\u003e \u003cp\u003eAlthough we believe that qualitative results from this study may hold for countries with similar socio-demographic and economic structures, we caution against their direct extrapolation to other geographical settings, given heterogeneities in educational systems, workplace structures and social interactions at work, household composition, age-specific population size and contact patterns.\u003c/p\u003e \u003cp\u003eThis research provides data across multiple sociodemographic strata on social contact patterns in Italy after the COVID-19 emergency, and analytics identifying the effect of socio-economic and demographic determinants on the number of experienced social contacts. These results were combined with data on school enrolment and in-person work attendance to provide estimates of the potential impact of public health interventions involving the educational and productive sectors. Estimates of this kind can support considerations on the balance between the expected epidemiological benefits and their societal costs.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData and data cleaning\u003c/h2\u003e \u003cp\u003eThis work is based on a new sample of data collected online in two waves at the end of March 2022 and 2023, from a nationally representative panel of the Italian population in terms of age, sex, and area of residence. Except for minor updates (e.g. for the number of vaccine doses recommended to the population in 2023), the second wave was identical to the first and administered to a random group of respondents who already participated in the first wave (1,246 participants), and to a group of first-time respondents of approximately equal size.\u003c/p\u003e \u003cp\u003eThe survey consists of two main sections: i) socio-demographics, health-related information and behavioural information on the respondent, and ii) a contact diary in which respondents were asked to recall their direct and indirect contacts on the day prior to the survey administration. After data collection, responses underwent an accurate data-cleaning process including: i) removal of respondents with inconsistencies in responses, and ii) removal of respondents with incomplete information. A complete description of the data-cleaning procedure is provided in Sec. SI1.1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIndirect co-location events augmentation procedure\u003c/h2\u003e \u003cp\u003eFor each direct contact, additional information was collected about the characteristics of the interaction and information on contacts themselves. However, indirect contacts were only reported as an aggregate number. To run the statistical model and construct age-specific contact matrices it was thus necessary to augment indirect contact data with the relevant missing information.\u003c/p\u003e \u003cp\u003eThe data augmentation procedure used in the baseline analysis can be summarized as follows. For each respondent:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ewe defined the number of indirect contacts to be augmented (N), by subtracting from the reported count for indirect contacts the number of indoor direct contacts that they reported. This assumption conservatively accounts for contacts potentially reported both as direct indoor and as indirect.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ewe reconstructed the age and setting of indirect contacts by sampling N times (with replacement) this information from the set of direct contacts reported by the same participant, excluding cohabitants and outdoor contacts.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eif the respondent did not report direct contacts, indirect contacts data augmentation was performed by sampling the information from direct contacts of other participants of the same age as the respondent.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eWe performed a sensitivity analysis on the data augmentation procedure by limiting the sampling for the assignment of indoor contacts only to the setting in which the respondent reported most of their direct contacts, rather than sampling from the overall direct contacts. See Section SI1.2 for more details.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDeterminants of social contacts: the statistical framework\u003c/h3\u003e\n\u003cp\u003eTo investigate the determinants of the total number of contacts, we fitted a generalised linear mixed model\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. We initially screened 35 covariates relative to the respondent and cohabitant\u0026rsquo;s characteristics for significant associations with the total number of contacts, fitting a set of negative binomial regression models. We built these models using as independent variables each individual covariate, an intercept, and a term accounting for the different data collection waves (2022 or 2023) (see Table SI5 for a full list of the screened covariates). Covariates that were not significant in the multivariate model were subsequently filtered out, and the final model included 15 covariates: sex, age group aggregated by 10-year intervals, occupation, household income, household size, in-presence attendance at work/school, contact happened on a Sunday, completion of SARS-CoV-2 primary vaccination cycle, recent (in the last 4 months) SARS-CoV-2 infection, presence of chronic comorbidities in the respondent, presence of chronic comorbidities in cohabitants, senior (65 years or older) cohabitants, having children, and participation to the first survey wave. Finally, a random intercept accounted for within-individual correlation in longitudinal responses across the two waves. Full details on the generalised linear mixed model selection are reported in SI1.3.\u003c/p\u003e\n\u003ch3\u003eAge-specific contact matrices\u003c/h3\u003e\n\u003cp\u003eWe constructed age-specific matrices of average total contacts for the Italian population stratified by 15 age groups (0\u0026ndash;4, 5\u0026ndash;9, 10\u0026ndash;14, 15\u0026ndash;19, 20\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;44, 45\u0026ndash;49, 50\u0026ndash;54, 55\u0026ndash;59, 60\u0026ndash;64, 65\u0026ndash;69, and 70\u0026thinsp;+\u0026thinsp;years old). We then computed the population normalised reciprocal contact matrix, following established procedures from the literature\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The matrices for the average total contacts obtained by 1000-fold bootstrapping of responses from both waves are reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (see also Figure SI11 for robustness). In the SI, we report contact matrices disaggregated by setting (Figures SI5-7) and matrices including only direct social contacts (Figure SI9).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEffect of school and work attendance on the transmissibility of a generic respiratory virus\u003c/h2\u003e \u003cp\u003eWe evaluated the potential effect of different scenarios of school and work attendance on the transmissibility of a novel respiratory virus (i.e., spreading in a fully susceptible population in the absence of interventions) under the assumption of homogeneous susceptibility and infectiousness across age groups. To do so, we combined data from Eurostat on age-specific populations attending different education levels\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e (from early childhood to tertiary education) and on age-specific in-person workforce in Italy from a previously published study\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe denote by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{s}\\)\u003c/span\u003e\u003c/span\u003e the reproduction number associated with scenario \u003cem\u003es\u003c/em\u003e and compute the relative reduction \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{s}\\)\u003c/span\u003e\u003c/span\u003e with respect to the baseline scenario with full school and work attendance:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\alpha\\:}_{s}=1-\\frac{{R}_{s}}{{R}_{0}}=1-\\frac{\\rho\\:\\left({NGM}_{s}\\right)}{\\rho\\:\\left({NGM}_{0}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\rho\\:\\left({NGM}_{s}\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\left({NGM}_{0}\\right)\\)\u003c/span\u003e\u003c/span\u003e represent the dominant eigenvalues of the next-generation matrices associated with scenario \u003cem\u003es\u003c/em\u003e and with the baseline scenario, respectively \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Since infection-related parameters are the same across scenarios, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{s}\\)\u003c/span\u003e\u003c/span\u003e can be simplified to:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\alpha\\:}_{s}=1-\\frac{\\rho\\:\\left({M}_{s}\\right)}{\\rho\\:\\left({M}_{0}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{s}\\)\u003c/span\u003e\u003c/span\u003e is a block-matrix\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{M}_{s}=\\:\\left(\\begin{array}{cc}{A}_{i,j}^{s}\u0026amp;\\:{A}_{i,j}^{s}\\\\\\:{B}_{i,j}^{s}\u0026amp;\\:{B}_{i,j}^{s}\\end{array}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe blocks are defined as\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{i,j}^{s}=\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i,j}^{P}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{N}_{i}^{P}\\left(s\\right)}{{N}_{j}^{P}\\left(s\\right)+{N}_{j}^{NP}\\left(s\\right)}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{i,j}^{s}=\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i,j}^{NP}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{N}_{i}^{NP}\\left(s\\right)}{{N}_{j}^{P}\\left(s\\right)+{N}_{j}^{NP}\\left(s\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewith\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i,j}^{P}\\)\u003c/span\u003e \u003c/span\u003e representing the overall contact matrix estimated for the population attending work or school in-person;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i,j}^{NP}\\)\u003c/span\u003e \u003c/span\u003e representing the overall contact matrix estimated for the population not attending work or school in-person;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{i}^{P}\\left(s\\right)\\)\u003c/span\u003e \u003c/span\u003e representing the number of individuals of age \u003cem\u003ei\u003c/em\u003e attending schools or work in-person in scenario \u003cem\u003es;\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{i}^{NP}\\left(s\\right)\\)\u003c/span\u003e \u003c/span\u003e representing the number of individuals of age \u003cem\u003ei\u003c/em\u003e not attending schools or work in-person in scenario \u003cem\u003es.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the ethical standards set by Bocconi University and has received approval from the Bocconi University Ethical Board (Approval Number: FA000383\u0026ndash;17 January 2022). Participants were informed about the nature and purpose of the research, including the voluntary nature of their participation and their right to withdraw at any time without any negative consequences. Upon completion, participants received compensation for their time and effort through the survey company. Informed consent was obtained from all participants prior to their involvement in the study. All analyses were carried out on anonymised data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAcknowledgment \u0026amp; Funding\u003c/h2\u003e \u003cp\u003eLL, CC, FT, VO, EDA, AM acknowledge funding from the ERC Consolidator Grant IMMUNE (no. 101003183). Researchers from the Bocconi Covid Crisis Lab acknowledge funding from the Romeo and Enrica Invernizzi Foundation. VM, GG, MM, PP, and SM acknowledge funding from the Fondazione Valorizzazione Ricerca Trentina (VRT), project COVIDVAX. This research was supported by EU funding within the NextGenerationEU-MUR M4C2.I.1.3 PNRR Extended Partnership initiative on Emerging Infectious Diseases (PE00000007, INF-ACT) \u0026ldquo;One Health Basic and Translational Research Actions addressing Unmet Needs on Emerging Infectious Diseases\u0026rdquo; through the INF-ACT Cascade Open Call 2023 (COC-1-2023-ISS-02) \u0026ndash; CUP I83C22001810007.\u003c/p\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePerra N (2021) Non-pharmaceutical interventions during the COVID-19 pandemic: A review. Phys Rep 913:1\u0026ndash;52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendez-Brito A, El Bcheraoui C, Pozo-Martin F (2021) Systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions against COVID-19. 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Springer, New York, NY. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-0716-1282-8\u003c/span\u003e\u003cspan address=\"10.1007/978-1-0716-1282-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiekmann O, Heesterbeek JAP, Metz JAJ (1990) On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. J Math Biol 28:365\u0026ndash;382\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiekmann O, Heesterbeek JaP, Roberts MG (2009) The construction of next-generation matrices for compartmental epidemic models. J R Soc Interface 7:873\u0026ndash;885\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIozzi F et al (2010) Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns- Fitting Predicted Matrices to Serological Data. PLOS Comput Biol 6:e1001021\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6009950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6009950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe collection of updated post-COVID-19 data on social contact patterns is critical for future epidemiological assessment and evaluation of non-pharmaceutical interventions.\u003c/p\u003e \u003cp\u003eWe conducted two waves of an online survey in Italy (March 2022 and March 2023), collecting representative data on direct (verbal/physical) and indirect (indoor co-location) contacts. Using a generalised linear mixed model, we analysed social contact determinants and the impact of work-from-home and distance learning on pathogen transmissibility.\u003c/p\u003e \u003cp\u003eIn-person work or school attendance significantly increased contacts: adults attending in person had 1.69 times (95% CI: 1.56\u0026ndash;1.84) more contacts than those staying home, while for children and adolescents, this ratio was 2.38 (95% CI: 1.98\u0026ndash;2.87). Even suspending all non-essential work had a marginal effect on transmissibility. However, combining work-from-home with distance learning (from primary school onwards) could reduce transmissibility by up to 23.7% (95% CI: 18.2\u0026ndash;29.0%), with minimal additional benefit from suspending early childcare.\u003c/p\u003e \u003cp\u003eThese findings offer key data for modelling respiratory pathogen transmission in Italy post-COVID-19 and provide insights into the epidemiological impact of tailored distancing measures. They support a nuanced approach to social distancing policies, balancing public health benefits with economic and social considerations.\u003c/p\u003e","manuscriptTitle":"Post-pandemic social contacts in Italy: implications for distancing measures on in-person school and work attendance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-01 11:43:58","doi":"10.21203/rs.3.rs-6009950/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"60f61593-677d-44b7-8392-8af662a5268c","owner":[],"postedDate":"November 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":44513437,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"},{"id":44513438,"name":"Health sciences/Health care/Health policy"},{"id":44513439,"name":"Physical sciences/Mathematics and computing/Applied mathematics"}],"tags":[],"updatedAt":"2026-02-26T15:49:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-01 11:43:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6009950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6009950","identity":"rs-6009950","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}