Thang Van Hoang1, Pietro Coletti2, Yimer Wasihun Kifle3, Kim Van Kerckhove2, Sarah Vercruysse2, Lander Willem4, Philippe Beutels4,5, Niel Hens2,4. 1. I-Biostat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt, 3500, Belgium. vanthang.hoang@uhasselt.be. 2. I-Biostat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt, 3500, Belgium. 3. The Janssen Pharmaceutical Companies of Johnson & Johnson, Antwerpen, Belgium. 4. Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Diseases Institute, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium. 5. School of Public health and Community Medicine, University of New South Wales, Sydney, 2052, Australia.
Abstract
BACKGROUND: In 2010-2011, we conducted a social contact survey in Flanders, Belgium, aimed at improving and extending the design of the first social contact survey conducted in Belgium in 2006. This second social contact survey aimed to enable, for the first time, the estimation of social mixing patterns for an age range of 0 to 99 years and the investigation of whether contact rates remain stable over this 5-year time period. METHODS: Different data mining techniques are used to explore the data, and the age-specific number of social contacts and the age-specific contact rates are modelled using a generalized additive models for location, scale and shape (GAMLSS) model. We compare different matrices using assortativeness measures. The relative change in the basic reproduction number (R0) and the ratio of relative incidences with 95% bootstrap confidence intervals (BCI) are employed to investigate and quantify the impact on epidemic spread due to differences in sex, day of the week, holiday vs. regular periods and changes in mixing patterns over the 5-year time gap between the 2006 and 2010-2011 surveys. Finally, we compare the fit of the contact matrices in 2006 and 2010-2011 to Varicella serological data. RESULTS: All estimated contact patterns featured strong homophily in age and sex, especially for small children and adolescents. A 30% (95% BCI [17%; 37%]) and 29% (95% BCI [14%; 40%]) reduction in R0 was observed for weekend versus weekdays and for holiday versus regular periods, respectively. Significantly more interactions between people aged 60+ years and their grandchildren were observed on holiday and weekend days than on regular weekdays. Comparing contact patterns using different methods did not show any substantial differences over the 5-year time period under study. CONCLUSIONS: The second social contact survey in Flanders, Belgium, endorses the findings of its 2006 predecessor and adds important information on the social mixing patterns of people older than 60 years of age. Based on this analysis, the mixing patterns of people older than 60 years exhibit considerable heterogeneity, and overall, the comparison of the two surveys shows that social contact rates can be assumed stable in Flanders over a time span of 5 years.
BACKGROUND: In 2010-2011, we conducted a social contact survey in Flanders, Belgium, aimed at improving and extending the design of the first social contact survey conducted in Belgium in 2006. This second social contact survey aimed to enable, for the first time, the estimation of social mixing patterns for an age range of 0 to 99 years and the investigation of whether contact rates remain stable over this 5-year time period. METHODS: Different data mining techniques are used to explore the data, and the age-specific number of social contacts and the age-specific contact rates are modelled using a generalized additive models for location, scale and shape (GAMLSS) model. We compare different matrices using assortativeness measures. The relative change in the basic reproduction number (R0) and the ratio of relative incidences with 95% bootstrap confidence intervals (BCI) are employed to investigate and quantify the impact on epidemic spread due to differences in sex, day of the week, holiday vs. regular periods and changes in mixing patterns over the 5-year time gap between the 2006 and 2010-2011 surveys. Finally, we compare the fit of the contact matrices in 2006 and 2010-2011 to Varicella serological data. RESULTS: All estimated contact patterns featured strong homophily in age and sex, especially for small children and adolescents. A 30% (95% BCI [17%; 37%]) and 29% (95% BCI [14%; 40%]) reduction in R0 was observed for weekend versus weekdays and for holiday versus regular periods, respectively. Significantly more interactions between people aged 60+ years and their grandchildren were observed on holiday and weekend days than on regular weekdays. Comparing contact patterns using different methods did not show any substantial differences over the 5-year time period under study. CONCLUSIONS: The second social contact survey in Flanders, Belgium, endorses the findings of its 2006 predecessor and adds important information on the social mixing patterns of people older than 60 years of age. Based on this analysis, the mixing patterns of people older than 60 years exhibit considerable heterogeneity, and overall, the comparison of the two surveys shows that social contact rates can be assumed stable in Flanders over a time span of 5 years.
Authors: Simon P Johnstone-Robertson; Daniella Mark; Carl Morrow; Keren Middelkoop; Melika Chiswell; Lisa D H Aquino; Linda-Gail Bekker; Robin Wood Journal: Am J Epidemiol Date: 2011-11-09 Impact factor: 4.897
Authors: Benson Ogunjimi; Niel Hens; Nele Goeyvaerts; Marc Aerts; Pierre Van Damme; Philippe Beutels Journal: Math Biosci Date: 2009-01-12 Impact factor: 2.144
Authors: F DeStefano; M Haber; D Currivan; T Farris; B Burrus; B Stone-Wiggins; A McCalla; H Guled; H Shih; P Edelson; S Wetterhall Journal: Epidemiol Infect Date: 2010-10-14 Impact factor: 2.451
Authors: Eva Santermans; Kim Van Kerckhove; Amin Azmon; W John Edmunds; Philippe Beutels; Christel Faes; Niel Hens Journal: Math Biosci Date: 2016-12-24 Impact factor: 2.144
Authors: Elise J Kuylen; Andrea Torneri; Lander Willem; Pieter J K Libin; Steven Abrams; Pietro Coletti; Nicolas Franco; Frederik Verelst; Philippe Beutels; Jori Liesenborgs; Niel Hens Journal: PLoS Comput Biol Date: 2022-08-22 Impact factor: 4.779
Authors: James Wambua; Lisa Hermans; Pietro Coletti; Frederik Verelst; Lander Willem; Christopher I Jarvis; Amy Gimma; Kerry L M Wong; Adrien Lajot; Stefaan Demarest; W John Edmunds; Christel Faes; Philippe Beutels; Niel Hens Journal: Sci Rep Date: 2022-03-25 Impact factor: 4.379