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Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic.
Loedy, Neilshan; Coletti, Pietro; Wambua, James; Hermans, Lisa; Willem, Lander; Jarvis, Christopher I; Wong, Kerry L M; Edmunds, W John; Robert, Alexis; Leclerc, Quentin J; Gimma, Amy; Molenberghs, Geert; Beutels, Philippe; Faes, Christel; Hens, Niel.
  • Loedy N; Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium. neilshan.loedy@uhasselt.be.
  • Coletti P; Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
  • Wambua J; Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
  • Hermans L; Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
  • Willem L; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.
  • Jarvis CI; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom.
  • Wong KLM; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom.
  • Edmunds WJ; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom.
  • Robert A; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom.
  • Leclerc QJ; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom.
  • Gimma A; Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene & Tropical Medicine, London, United Kingdom.
  • Molenberghs G; Epidemiology and Modelling of Bacterial Escape to Antimicrobials, Institut Pasteur, Paris, France.
  • Beutels P; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom.
  • Faes C; Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
  • Hens N; L-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium.
BMC Public Health ; 23(1): 1298, 2023 07 06.
Article en En | MEDLINE | ID: mdl-37415096
ABSTRACT

BACKGROUND:

During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants' "survey fatigue", which may impact inferences.

METHODS:

A negative binomial generalized additive model for location, scale, and shape (NBI GAMLSS) was adopted to estimate the number of contacts reported between age groups and to deal with under-reporting due to fatigue within the study. The dropout process was analyzed with first-order auto-regressive logistic regression to identify factors that influence dropout. Using the so-called next generation principle, we calculated the effect of under-reporting due to fatigue on estimating the reproduction number.

RESULTS:

Fewer contacts were reported as people participated longer in the survey, which suggests under-reporting due to survey fatigue. Participant dropout is significantly affected by household size and age categories, but not significantly affected by the number of contacts reported in any of the two latest waves. This indicates covariate-dependent missing completely at random (MCAR) in the dropout pattern, when missing at random (MAR) is the alternative. However, we cannot rule out more complex mechanisms such as missing not at random (MNAR). Moreover, under-reporting due to fatigue is found to be consistent over time and implies a 15-30% reduction in both the number of contacts and the reproduction number ([Formula see text]) ratio between correcting and not correcting for under-reporting. Lastly, we found that correcting for fatigue did not change the pattern of relative incidence between age groups also when considering age-specific heterogeneity in susceptibility and infectivity.

CONCLUSIONS:

CoMix data highlights the variability of contact patterns across age groups and time, revealing the mechanisms governing the spread/transmission of COVID-19/airborne diseases in the population. Although such longitudinal contact surveys are prone to the under-reporting due to participant fatigue and drop-out, we showed that these factors can be identified and corrected using NBI GAMLSS. This information can be used to improve the design of similar, future surveys.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País como asunto: Europa Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País como asunto: Europa Idioma: En Año: 2023 Tipo del documento: Article