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Social contact data are essential for understanding the spread of respiratory infectious diseases and designing effective prevention strategies. However, many studies often overlook the heterogeneity in mixing patterns among older age groups and individual frailty levels, assuming homogeneity across these sub-populations. This shortcoming may undermine non-pharmaceutical interventions by not targeting specific contact behaviours, potentially reducing their effectiveness in controlling disease. To address this gap, we conducted a contact survey in Flanders, Belgium (June 2022–June 2023). We collected data from 5995 participants (overall response rates of 19.34%) who recorded 31,375 contacts with distinct individuals. Within this cohort, 14.50% were classified as frail, and 46.85% were classified as non-frail. On average, participants report 5.48 contacts daily, with a median of 4 contacts (IQR: 2–7). These contacts vary based on participants’ age and frailty levels, influenced by the locations of their interactions. Using the collected data, we reconstructed frailty-dependent contact matrices and developed a contact-based mathematical model that integrates participants’ and contactees’ frailty levels to investigate how frailty levels affect transmission dynamics. Incorporating frailty levels into the mathematical model substantially alters the shape of epidemic curves and peak incidences. Such insights might provide useful insights for informing non-pharmaceutical interventions, indicating the potential benefit of similar data collection in different countries.

Background SARS-CoV-2 dynamics are driven by human behaviour. Social contact data are of utmost importance in the context of transmission models of close-contact infections. Methods Using online representative panels of adults reporting on their own behaviour as well as parents reporting on the behaviour of one of their children, we collect contact mixing (CoMix) behaviour in various phases of the COVID-19 pandemic in over 20 European countries. We provide these timely, repeated observations using an online platform: SOCRATES-CoMix. In addition to providing cleaned datasets to researchers, the platform allows users to extract contact matrices that can be stratified by age, type of day, intensity of the contact and gender. These observations provide insights on the relative impact of recommended or imposed social distance measures on contacts and can inform mathematical models on epidemic spread. Conclusion These data provide essential information for policymakers to balance non-pharmaceutical interventions, economic activity, mental health and wellbeing, during vaccine rollout.

Human behaviour is known to be crucial in the propagation of infectious diseases through respiratory or close-contact routes like the current SARS-CoV-2 virus. Intervention measures implemented to curb the spread of the virus mainly aim at limiting the number of close contacts, until vaccine roll-out is complete. Our main objective was to assess the relationships between SARS-CoV-2 perceptions and social contact behaviour in Belgium. Understanding these relationships is crucial to maximize interventions’ effectiveness, e.g. by tailoring public health communication campaigns. In this study, we surveyed a representative sample of adults in Belgium in two longitudinal surveys (survey 1 in April 2020 to August 2020, and survey 2 in November 2020 to April 2021). Generalized linear mixed effects models were used to analyse the two surveys. Participants with low and neutral perceptions on perceived severity made a significantly higher number of social contacts as compared to participants with high levels of perceived severity after controlling for other variables. Our results highlight the key role of perceived severity on social contact behaviour during a pandemic. Nevertheless, additional research is required to investigate the impact of public health communication on severity of COVID-19 in terms of changes in social contact behaviour.

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.

The analysis of hierarchical data that take the form of clusters with random size has received considerable attention. The focus here is on samples that are very large in terms of number of clusters and/or members per cluster, on the one hand, as well as on very small samples (e.g., when studying rare diseases), on the other. Whereas maximum likelihood inference is straightforward in medium to large samples, in samples of sizes considered here it may be prohibitive. We propose sample-splitting (Molenberghs, Verbeke and Iddi (2011)) as a way to replace iterative optimization of a likelihood that does not admit an analytical solution, with closed-form calculations. We use pseudo-likelihood (Molenberghs et al. (2014)), consisting of computing weighted averages over solutions obtained for each cluster size occurring. As a result, the statistical properties of this approach need to be investigated, especially because the minimal sufficient statistics involved are incomplete. The operational characteristics were studied using simulations. Simulations were also done to compare the proposed method to existing techniques developed to circumvent difficulties with unequal cluster sizes, such as multiple imputation. It follows that the proposed non-iterative methods have a strong beneficial impact on computation time; at the same time, the method is the most precise among its competitors considered. The findings are illustrated using data from a developmental toxicity study, where clusters are formed of fetuses within litters.

Completeness means that any measurable function of a sufficient statistic that has zero expectation for every value of the parameter indexing the parametric model class is the zero function almost everywhere. The property is satisfied in many simple situations in view of parameters of direct scientific interest, such as in regression models fitted to data from a random sample with fixed size. A random sample is not always of a fixed, a prioridetermined size. Examples include sequential sampling and stopping rules, missing data and clusters with random size. Often, there then is no complete sufficient statistic. A simple characterisation of incompleteness is given for the exponential family in terms of the mapping between the sufficient statistic and the parameter, based upon the implicit function theorem. Essentially, this is a comparison of the dimension of the sufficient statistic with the length of the parameter vector. This results in an easy verifiable criterion for incompleteness, clear and simple to use, even for complex settings as is shown for missing data and clusters of random size. This tutorial exemplifies the (in)completeness property of a sufficient statistic, thereby illustrating our proposed characterisation. The examples are organised from more classical, simple examples to gradually more advanced settings.

The pandemic served as an important test case of complementing traditional public health data with non-traditional data (NTD) such as mobility traces, social media activity, and wearables data to inform decision-making. Drawing on an expert workshop and a targeted survey of European modelers, we assess the promise and persistent limitations of such data in pandemic preparedness and response. We distinguish between \"first-mile\" (accessing and harmonizing data) and \"last-mile\" challenges (translating insights into actionable interventions). The expert workshop held in 2024 brought together participants from public health, academia, policymakers, and industry to reflect on lessons learned and define strategies for translating NTD insights into policy making. The survey offers evidence of the barriers faced during COVID-19 and highlights key data unavailability and underuse. Our findings reveal ongoing issues with data access, quality, and interoperability, as well as institutional and cognitive barriers to evidence-based decision-making. Around 66% of datasets suffered access problem, with data sharing reluctance for NTD being double that of traditional data (30% vs 15%). Only 10% reported they could use all the data they needed. We propose a set of recommendations: for first-mile challenges, solutions focus on technical and legal frameworks for data access.; for last-mile challenges, we recommend fusion centers, decision accelerator labs, and networks of scientific ambassadors to bridge the gap between analysis and action. Realizing the full value of NTD requires a sustained investment in institutional readiness, cross-sectoral collaboration, and a shift toward a culture of data solidarity. Grounded in the lessons of COVID-19, the article can be used to design a roadmap for using NTD to confront a broader array of public health emergencies, from climate shocks to humanitarian crises.

Our study examines the effects of drought on livelihoods and human migration in the rural highlands of northern Ethiopia, one of the most affected regions during the 2015 drought. We conducted a household survey contextualized by focus group discussions in two rural sending areas. Drought intensity was similar in both areas, but drought impacts and farmer’s response strategies differed. Overall, we observed significant strategy changes, including a drastic shift from subsistence crop production to livestock sale among farmers being dependent on the March–June rainfall (belg season). Our results suggest that drought increases mobility, primarily through triggering short-term migration to closer destinations to cover immediate needs like food shortages. Four out of ten households in both regions engaged in migration. Nonetheless, migration tends to be context specific with respect to barriers and opportunities for participation, with distance, duration, and perceptions of migration as well as the underlying motives being region-specific. We conclude that understanding livelihood strategy changes requires an embedding in a larger context rather than focusing on one particular driver. Migration—one important livelihood strategy in northern Ethiopia—is the result of a complex interplay of factors, drought perhaps being only one of them. Based on our finding, we reason the decision to migrate is strongly moderated by the drought rather than it is directly driven by it.