Inferring age-specific differences in susceptibility to and infectiousness upon SARS-CoV-2 infection based on Belgian social contact data

Franco, Nicolas; Coletti, Pietro; Willem, Lander; Angeli, Leonardo; Lajot, Adrien; Abrams, Steven; Beutels, Philippe; Faes, Christel; Hens, Niel

doi:10.1101/2021.10.10.21264753

Cited by 5 publications

(8 citation statements)

References 36 publications

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“…Our findings suggest that the contact data collected in the CoMix survey describe trends in contact behaviour in the general population. As such it can be useful in monitoring the effect of control measures that are aimed at contact reduction 17,25,26 , describe determinants for contact behaviour 27,28 , or serve as input for infectious disease models 29–31 . These results can help guide policy in future waves of COVID-19 or other emerging respiratory diseases by monitoring the interaction between contact behaviour, control measures and complicance, and emphasize that contact surveys such as the CoMix study are indispensable for providing a quantitative basis to public health policy.…”

Section: Discussionmentioning

confidence: 99%

Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands

Backer

Bogaardt

Beutels

et al. 2022

Preprint

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The COVID-19 pandemic was in 2020 and 2021 for a large part mitigated by reducing contacts in the general population. To monitor how these contacts changed over the course of the pandemic in the Netherlands, a longitudinal survey was conducted where participants reported on their at-risk contacts every two weeks, as part of the European CoMix survey. The survey included 1659 participants from April to August 2020 and 2514 participants from December 2020 to September 2021.We categorized the number of unique contacted persons excluding household members, reported per participant per day into six activity levels, defined as 0, 1, 2, 3-4, 5-9 and 10 or more reported contacts. After correcting for age, vaccination status, risk status for severe outcome of infection, and frequency of participation, activity levels increased over time, coinciding with relaxation of COVID-19 control measures.

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Section: Discussionmentioning

confidence: 99%

Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands

Backer

Bogaardt

Beutels

et al. 2022

Preprint

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“…al. [44,45] which used the Belgian arm of the CoMix study to estimate age-specific infectiousness and susceptibility independently to this study.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the use of social contact data to produce age-specific forecasts of SARS-CoV-2 incidence

Munday

Abbott

Meakin

et al. 2022

Preprint

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Short-term forecasts can provide predictions of how an epidemic will change in the near future and form a central part of outbreak mitigation and control. Renewal-equation based models are increasingly popular. They infer key epidemiological parameters from historical epidemiological data and forecast future epidemic dynamics without requiring complex mechanistic assumptions. However, these models typically ignore interaction between age-groups, partly due to challenges in parameterising a time varying interaction matrix. Social contact data collected regularly by the CoMix survey during the COVID-19 epidemic in England, provide a means to inform interaction between age-groups in real-time. We developed an age-specific forecasting framework and applied it to two age-stratified time-series: incidence of SARS-CoV-2 infection, estimated from a national infection and antibody prevalence survey; and, reported cases according to the UK national COVID-19 dashboard. Jointly fitting our model to social contact data from the CoMix study, we inferred a time-varying next generation matrix which we used to project infections and cases in the four weeks following each of 29 forecast dates between October 2021 and November 2022. We evaluated the forecasts using proper scoring rules and compared performance with three other models with alternative data and specifications alongside two naive baseline models. Overall, incorporating age-interaction improved forecasts of infections and the CoMix-data-informed model was the best performing model at time horizons between two and four weeks. However, this was not true when forecasting cases. We found that age-group-interaction was most important for predicting cases in children and older adults. The contact-data-informed models performed best during the winter months of 2020 - 2021, but performed comparatively poorly in other periods. We highlight challenges regarding the incorporation of contact data in forecasting and offer proposals as to how to extend and adapt our approach, which may lead to more successful forecasts in future.

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“…The vectors a i and h j , however, only have a relative interpretation because of the presence of q. For the purpose of this study, we use a i = (0.39, 0.83, 0.74) and h j = (0.55, 0.79, 0.99) as the average a i and h j used in Franco et al [49] for Children, Adults, and Elderly age categories, respectively.…”

Section: Next Generation Principlementioning

confidence: 99%

“…Moreover, the ratio of the eigenvalues of two next generation matrices can be used to compare the relative temporal change in the reproduction number (R t ) using the next generation approach [49]. R t was used and calculated from the daily number of cases, and hospitalizations [50, 51] as a comparison by considering the time delays associated with the COVID-19 disease burden.…”

Section: Next Generation Principlementioning

confidence: 99%

Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic

Loedy

Coletti

Wambua

et al. 2023

Preprint

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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 (R0) 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 bias, 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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Inferring age-specific differences in susceptibility to and infectiousness upon SARS-CoV-2 infection based on Belgian social contact data

Cited by 5 publications

References 36 publications

Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands

Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands

Evaluating the use of social contact data to produce age-specific forecasts of SARS-CoV-2 incidence

Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic

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