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Title: | Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic | Authors: | LOEDY, Neil 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 |
Issue Date: | 2023 | Publisher: | BMC | Source: | BMC PUBLIC HEALTH, 23 (1) (Art N° 1298) | 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 (R-0) 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. | Notes: | Loedy, N (corresponding author), Hasselt Univ, Data Sci Inst, I BioStat, Hasselt, Belgium. neilshan.loedy@uhasselt.be |
Keywords: | Bias assessment;Social contact data;COVID-19;SARS-CoV-2;Survey fatigue;Under-reporting | Document URI: | http://hdl.handle.net/1942/40798 | e-ISSN: | 1471-2458 | DOI: | 10.1186/s12889-023-16193-7 | ISI #: | 001026069700005 | Rights: | The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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