comix comparing mixing patterns in the belgian population during and after lockdown CORD-Papers-2022-06-02 (Version 1)

Title: CoMix: comparing mixing patterns in the Belgian population during and after lockdown
Abstract: The COVID-19 pandemic has shown how a newly emergent communicable disease can lay considerable burden on public health. To avoid system collapse governments have resorted to several social distancing measures. In Belgium this included a lockdown and a following period of phased re-opening. A representative sample of Belgian adults was asked about their contact behaviour from mid-April to the beginning of August during different stages of the intervention measures in Belgium. Use of personal protection equipment (face masks) and compliance to hygienic measures was also reported. We estimated the expected reproduction number computing the ratio of [Formula: see text] with respect to pre-pandemic data. During the first two waves (the first month) of the survey the reduction in the average number of contacts was around 80% and was quite consistent across all age-classes. The average number of contacts increased over time particularly for the younger age classes still remaining significantly lower than pre-pandemic values. From the end of May to the end of July the estimated reproduction number has a median value larger than one although with a wide dispersion. Estimated [Formula: see text] fell below one again at the beginning of August. We have shown how a rapidly deployed survey can measure compliance to social distancing and assess its impact on COVID-19 spread. Monitoring the effectiveness of social distancing recommendations is of paramount importance to avoid further waves of COVID-19.
Published: 2020-12-14
Journal: Sci Rep
DOI: 10.1038/s41598-020-78540-7
DOI_URL: http://doi.org/10.1038/s41598-020-78540-7
Author Name: Coletti Pietro
Author link: https://covid19-data.nist.gov/pid/rest/local/author/coletti_pietro
Author Name: Wambua James
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wambua_james
Author Name: Gimma Amy
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gimma_amy
Author Name: Willem Lander
Author link: https://covid19-data.nist.gov/pid/rest/local/author/willem_lander
Author Name: Vercruysse Sarah
Author link: https://covid19-data.nist.gov/pid/rest/local/author/vercruysse_sarah
Author Name: Vanhoutte Bieke
Author link: https://covid19-data.nist.gov/pid/rest/local/author/vanhoutte_bieke
Author Name: Jarvis Christopher I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jarvis_christopher_i
Author Name: Van Zandvoort Kevin
Author link: https://covid19-data.nist.gov/pid/rest/local/author/van_zandvoort_kevin
Author Name: Edmunds John
Author link: https://covid19-data.nist.gov/pid/rest/local/author/edmunds_john
Author Name: Beutels Philippe
Author link: https://covid19-data.nist.gov/pid/rest/local/author/beutels_philippe
Author Name: Hens Niel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hens_niel
sha: 3d35638e8ce7a35203afd21d4187048dcb8b299d
license: cc-by
license_url: https://creativecommons.org/licenses/by/4.0/
source_x: Medline; PMC
source_x_url: https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/
pubmed_id: 33318521
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/33318521
pmcid: PMC7736856
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7736856
url: https://doi.org/10.1038/s41598-020-78540-7 https://www.ncbi.nlm.nih.gov/pubmed/33318521/
has_full_text: TRUE
Keywords Extracted from Text Content: lockdown COVID-19 Shanghai 5 face-masks post-lockdown direct/ Zenodo 31 COVID-19 children line Brussels and Netherlands 7 EC UZA 20/13/147 lockdown globe contacts US 9 UK matrix Shanghai and participants 5-17 phone/tablet Face coronavirus UK 6 15,26 close-contact Wuhan households social contacts France S15 Wuhan 5 multi-wave SARS-CoV-2 www.nature.com/scientificreports/ supermarkets/shops contact matrix www.nature.com/scientificreports/ Figure 3 (= people children contacts SocialMixr age-values ses/by/4.0/. line Innovations Programme-Project EpiPose http://creat iveco mmons COVID-19's Public Health
Extracted Text Content in Record: First 5000 Characters:The COVID-19 pandemic has shown how a newly emergent communicable disease can lay considerable burden on public health. To avoid system collapse, governments have resorted to several social distancing measures. In Belgium, this included a lockdown and a following period of phased re-opening. A representative sample of Belgian adults was asked about their contact behaviour from mid-April to the beginning of August, during different stages of the intervention measures in Belgium. Use of personal protection equipment (face masks) and compliance to hygienic measures was also reported. We estimated the expected reproduction number computing the ratio of R 0 with respect to pre-pandemic data. During the first two waves (the first month) of the survey, the reduction in the average number of contacts was around 80% and was quite consistent across all age-classes. The average number of contacts increased over time, particularly for the younger age classes, still remaining significantly lower than pre-pandemic values. From the end of May to the end of July , the estimated reproduction number has a median value larger than one, although with a wide dispersion. Estimated R 0 fell below one again at the beginning of August. We have shown how a rapidly deployed survey can measure compliance to social distancing and assess its impact on COVID-19 spread. Monitoring the effectiveness of social distancing recommendations is of paramount importance to avoid further waves of COVID-19. OPEN The COVID-19 pandemic due to the novel coronavirus (SARS-CoV-2) has shown how newly emerging infectious diseases can lay considerable burden on public health and social economic welfare of the society. Since its emergence, over million confirmed cases and deaths have been recorded as of 2020 1 . In the absence of established pharmaceutical interventions, many countries across the globe have resorted to non-pharmaceutical interventions, advocacy of proper hygienic measures (hand washing, sanitizing), as well as promotion of wide-spread usage of masks to help combat the spread of this disease. However, sustainability of some of the imposed measures is infeasible in the long term, due to an urgent need to returning back to normal social life as well as rekindling the economy. Thus governments have been prompted to lift some of the measures in a phased manner whilst enforcing new/existing rules such as wearing masks in designated places such as in public transport, hospitals, schools, workplaces and other places that attract large crowds and gatherings. As COVID-19 is primarily transmitted through close-contact interaction with infected individuals 2 , data on social contacts is indispensable in informing mathematical modeling studies being employed to explore the evolution of this disease. The last decade of research in infectious disease modeling has shown how quantifying contact patterns is crucial to capture disease dynamics 3 . However, social contact data capturing behavioral changes in the population during and across different stages of an epidemic is mostly lacking and mathematical models need to rely on various assumptions, which might be unverifiable. This raises validity concerns on their appropriateness in guiding decision making. Thus, as many governments are carefully monitoring the situation to avoid further waves of COVID-19, continual data collection is vitally important to closely monitor changes in social mixing. This can provide insights Scientific Reports | (2020) 10:21885 | https://doi.org/10.1038/s41598-020-78540-7 www.nature.com/scientificreports/ on the impact of different intervention measures as well as help in real-time management of the COVID-19 crisis, together with other insights from social and behavioral sciences 4 . Studies comparing social contact patterns before and during the COVID-19 pandemic have been reported for Wuhan and Shanghai 5 , the UK 6 , the Netherlands 7 , Luxembourg 8 , the US 9 and in multiple countries (Belgium, France, Germany, Italy, the Netherlands, Spain, the UK, and the US) 10 . The overall reduction in the total number of contacts made by individuals ranged from 48% to 85%, stressing once again the importance of quantifying the impact of social distancing separately for each country. Also, although little variations in the number of contacts over time were measured 10 up to mid-April, this may change as countries relieve stricter measures and social interactions need to adjust to the new post-lockdown reality 8 . In this paper, we present results from a longitudinal survey of the adult population in Belgium, representative by age, gender and region of residence. The survey involves multiple waves of data collection, and is part of a wider study to look at changes in contact patterns across European countries (see e.g. UK 6 ). Here, we present results for eight waves (= 16 weeks). We quantify the changes in social contact patterns comparing pre-pandemic, lockdown and post-lockdown period
Keywords Extracted from PMC Text: \documentclass[12pt]{minimal} UK SARS-CoV-2 sciences4 US)10 time25 initiative6 EC UZA 20/13/147 contact matrix Shanghai measured10 globe households people children21 adults19 S15 US9 lockdown \documentclass[12pt]{minimal children contacts mandatory23 multi-wave number15 place22 survey22 face-masks France phone/tablet contacts July14 Netherlands7 Zenodo31 bias3,24 Wuhan line children Wuhan5 's Brussels and reality8 Luxembourg8 close-contact participants age-values hypothesis"15,26 in29 19–65 individuals2 0.02813 SocialMixr matrix supermarkets/shops restaurants conversational contacts individuals UK6 Shanghai5 face Fig. 7 post-lockdown coronavirus COVID-19
Extracted PMC Text Content in Record: First 5000 Characters:The COVID-19 pandemic due to the novel coronavirus (SARS-CoV-2) has shown how newly emerging infectious diseases can lay considerable burden on public health and social economic welfare of the society. Since its emergence, over million confirmed cases and deaths have been recorded as of 20201. In the absence of established pharmaceutical interventions, many countries across the globe have resorted to non-pharmaceutical interventions, advocacy of proper hygienic measures (hand washing, sanitizing), as well as promotion of wide-spread usage of masks to help combat the spread of this disease. However, sustainability of some of the imposed measures is infeasible in the long term, due to an urgent need to returning back to normal social life as well as rekindling the economy. Thus governments have been prompted to lift some of the measures in a phased manner whilst enforcing new/existing rules such as wearing masks in designated places such as in public transport, hospitals, schools, workplaces and other places that attract large crowds and gatherings. As COVID-19 is primarily transmitted through close-contact interaction with infected individuals2, data on social contacts is indispensable in informing mathematical modeling studies being employed to explore the evolution of this disease. The last decade of research in infectious disease modeling has shown how quantifying contact patterns is crucial to capture disease dynamics3. However, social contact data capturing behavioral changes in the population during and across different stages of an epidemic is mostly lacking and mathematical models need to rely on various assumptions, which might be unverifiable. This raises validity concerns on their appropriateness in guiding decision making. Thus, as many governments are carefully monitoring the situation to avoid further waves of COVID-19, continual data collection is vitally important to closely monitor changes in social mixing. This can provide insights on the impact of different intervention measures as well as help in real-time management of the COVID-19 crisis, together with other insights from social and behavioral sciences4. Studies comparing social contact patterns before and during the COVID-19 pandemic have been reported for Wuhan and Shanghai5, the UK6, the Netherlands7 , Luxembourg8, the US9 and in multiple countries (Belgium, France, Germany, Italy, the Netherlands, Spain, the UK, and the US)10. The overall reduction in the total number of contacts made by individuals ranged from 48% to 85%, stressing once again the importance of quantifying the impact of social distancing separately for each country. Also, although little variations in the number of contacts over time were measured10 up to mid-April, this may change as countries relieve stricter measures and social interactions need to adjust to the new post-lockdown reality8. In this paper, we present results from a longitudinal survey of the adult population in Belgium, representative by age, gender and region of residence. The survey involves multiple waves of data collection, and is part of a wider study to look at changes in contact patterns across European countries (see e.g. UK6). Here, we present results for eight waves (= 16 weeks). We quantify the changes in social contact patterns comparing pre-pandemic, lockdown and post-lockdown periods and its impact on the transmission dynamics of COVID-19 based on the changes in the basic reproduction number relying on the next generation principle. We use a published survey of the Flemish region (Belgium) conducted in 201011,12 as reference for the pre-pandemic social mixing. Also, we assess the uptake of face mask wearing and adherence to hygienic measures in the population over time. During the first wave, 1542 participants took part in the survey, divided among 732 males (47.5%) and 810 females (52.5%) (Table 1). Table S2 presents information on participation rates for each wave. The average participant's age was 48.4 years (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {standard deviation (sd)} =16.3\hbox { years}$$\end{document}standard deviation (sd)=16.3years), with a median age of 50 years, and an inter-quartile range (IQR) of [35–65]. The average household size was 2.8 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {sd} = 1.4$$\end{document}sd=1.4), IQR [2–4] with a maximum household size of 10. In total, data on 4290 household members, including the participants, was collected. Nearly half of the participants were living with children (51.
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