BACKGROUND With a notification rate of 44.9 per million population in 2024, Belgium was among the countries with the highest measles rate of the European Union/European Economic Area countries. Although the coverage of the first dose of measles–mumps–rubella (MMR) vaccination in Flanders was 96% in 2020, two-dose coverage was below 95%. To minimise the risk of sustained measles transmission in vulnerable settings, such as primary schools with low vaccination coverage or daycares, it is important to quarantine susceptible contacts of a measles case. AIM We aimed to identify policies that balance measles outbreak control with societal as well as educational impact of quarantining children. METHODS Using a simulation model, we evaluated the impact of different quarantine strategies for measles in primary school and daycare settings in Flanders, Belgium. RESULTS We found that shortening the quarantine period from 21 to 18 days only moderately (e.g. 16–37% in primary school) increased the risk of a subsequent infection wave, and the final proportion of later-generation cases remained at most 3.2% of the school or daycare. In settings with a low vaccination coverage (e.g. 20%), multiple independent introductions or a low diagnosis rate (e.g. only half of symptomatic children going to a doctor), the addition of post-exposure vaccination can further reduce transmission risk. CONCLUSION We found that quarantine duration could be shorter than the presumed maximal incubation period of 21 days, without substantially increasing the risk of onward transmission. As such, acceptability and compliance with mandated quarantine periods might also increase.

Data privacy has increasingly become a daunting challenge because it limits data availability, which is essential in estimating statistical models such as generalized linear mixed models. Access to personal data often involves considerable time, effort, and paperwork, which can impede research progress and collaboration. Existing approaches that do not use individual-level data for model estimation are either prone to ecological bias, cannot handle heterogeneity, or require iterative communication. In this paper, we propose an approach to estimate generalized linear mixed models based on summary statistics shared only once. We used linear, logistic, and Poisson mixed models as examples to demonstrate the methodology. Our strategy involves generating pseudo-data whose summary statistics match those of the actual but unavailable data. These pseudo-data are then used for model estimation instead of the actual data. The estimates we achieve are identical (up to the third decimal place) to those derived from actual data and have similar bias, coverage, and prediction performance. Communication and resource efficiency distinguish our approach from existing methods.

BackgroundChickenpox (CP) and herpes zoster (HZ), both caused by the varicella-zoster virus (VZV), present a significant public health burden in unvaccinated populations. Universal CP vaccination has been long debated due to concerns about a potential increase in HZ incidence as a consequence of the exogenous boosting hypothesis.MethodsWe performed a cost-utility analysis on two deterministic compartmental dynamic transmission models, each of which employed a different underlying mechanism of exogenous boosting: temporal or progressive immunity. We considered four vaccination strategies: the current practice of no widespread vaccination and three strategies involving CP and HZ vaccination, either alone or in combination. The CP vaccines considered were Varivax and ProQuad, while the HZ vaccine considered was the recombinant zoster vaccine (RZV), Shingrix. The vaccine prices per dose were as follows: Varivax - €52.52, ProQuad - €73.69, recombinant zoster vaccine - €170.26. The clinical and economic impact of vaccination on both CP and HZ outcomes were evaluated. The main health outcome of interest was the quality-adjusted life year (QALY) which was used to compare the strategy yielding the highest average net monetary benefits (i.e., the optimal strategy) across a range of willingness-to-pay (WTP) values. Costs and health outcomes were discounted at 3.0% and 1.5% annually, respectively. We used 3 time horizons (i.e., 50, 75 & 100 years) and implemented the healthcare payer perspective throughout the analysis.ResultsCP vaccination led to a substantial reduction in CP incidence in both models. In the temporary immunity boosting (Temp) model, strategies that included HZ vaccination showed a decrease in HZ incidence. For the CP vaccination strategy in the Temp model, and for all CP and HZ vaccination strategies in the progressive immunity boosting (Prog) model, we observed both short- and medium-term increases in HZ, followed by a decrease to levels below the no-vaccination scenario. From the healthcare payer's perspective, using a WTP of €40,000 per QALY gained, the Temp model indicated that the three vaccination strategies were cost-effective when considering time horizons of 50, 75, and 100 years. For the Prog model, only strategies combining both CP and HZ vaccination were cost-effective given a 100-year time horizon. Vaccination strategies under the Temp model became cost-effective at lower values of WTP compared to those under the Prog model.ConclusionBoth models predicted that universal CP vaccination would result in significant reductions in the burden of CP disease, however, the HZ disease burden impact varied significantly depending on the assumed boosting mechanism. Hence, the choice of modeled exogenous boosting mechanism leads to different optimal vaccination strategies. Ascertaining the relative accuracy of these structural model choices will require continued research on the mechanism of VZV boosting.

The WHO aims to eliminate hepatitis B virus (HBV) as a public health problem by 2030. HBV vaccination has reduced HBV incidence and mortality and is a cornerstone of the WHO elimination strategy. Belgium introduced universal infant HBV vaccination in 1999, with temporary catch-up vaccination for 12-year-olds, thereby covering all individuals born since 1987. This nationwide serosurvey assessed vaccine-induced hepatitis B surface antibody (anti-HBs) prevalence and natural HBV exposure in Belgium in 2020. We analyzed 4955 left-over samples from SARS-CoV-2 sero-epidemiology studies in 2020. Samples from ambulatory patients outside lockdown periods were tested for anti-HBs and hepatitis B core antibodies (anti-HBc) to evaluate vaccine-induced vs. natural exposure-derived anti-HBs responses. Samples were stratified by region, 10-year age band, and sex, and were weighted to reflect the Belgian population. Overall, 47.3

This paper presents a smoothing method to estimate age-specific human contact patterns and their variations over different periods. Specifically, it examines how age-specific contact patterns shift under varying conditions, such as holiday periods and levels of public health intervention. The method uses Bayesian P-splines to smooth age-specific contact rates and leverages Laplace approximations for fast Bayesian inference, significantly reducing computational complexity. The proposed methodology is applied to the CoMix data from Belgium, a social contact survey collected during the COVID-19 pandemic. Results indicate significantly reduced contacts during periods in which strict social policies were in place, particularly among adults, and notable reductions among young individuals during holidays. This research advances our understanding of how human contact adapts in response to varying social and policy conditions, which can guide more realistic and adaptive infectious disease transmission models.

Unlabelled:The COVID-19 pandemic served as an important test case of complementing traditional public health data with nontraditional data, such as mobility traces, social media activity, and wearable data, to inform real-time decision-making. Drawing on an expert workshop and a targeted survey of epidemic modelers in Europe, this study assesses the promise and the persistent limitations of such data in pandemic preparedness and response. We distinguish between "first-mile" challenges (obstacles to accessing and harmonizing data) and "last-mile" challenges (difficulties in translating insights into actionable policy interventions). The expert workshop, convened in March 2024 in Brussels, brought together 50 participants, including public health professionals, data scientists, policymakers, and industry leaders, to reflect on lessons learned and define strategies for better integration of nontraditional data into epidemic modeling and policymaking. The accompanying survey, gathering experiences from 29 modelers, offers empirical evidence of the barriers faced by modelers during the COVID-19 pandemic and highlights areas where key data were unavailable or underused. The experiences collected through the survey and workshop resulted in ten key actions and three overarching recommendations for public entities, data providers, and stakeholders. Our findings reveal ongoing issues with data access, quality, and interoperability, as well as institutional and cognitive barriers to evidence-based decision-making. Approximately 66% of all datasets had at least one access problem, with data sharing reluctance for nontraditional sources being double that of traditional data (30% vs 15%). Only 10% of respondents reported that they could use all the data they needed. These limitations included issues related to timeliness and granularity of data, as well as issues with linkage, comparability, and biases. To overcome these hurdles, we propose a set of enabling mechanisms, including data inventories, standardization protocols, simulation exercises, data stewardship roles, and data collaboratives. For first-mile challenges, solutions focus on technical and legal frameworks for data access. For last-mile challenges, we recommend fusion centers, decision accelerator laboratories, and networks of scientific ambassadors to bridge the gap between analysis and action. We argue that realizing the full value of nontraditional data requires a sustained investment in institutional readiness, cross-sectoral collaboration, and a shift toward a culture of data solidarity. Grounded in the lessons of the COVID-19 pandemic, the study can be used to design a roadmap for using nontraditional data to confront a broader array of public health emergencies, from climate shocks to humanitarian crises.

BackgroundBelgium experienced two SARS-CoV-2 epidemic waves in 2020, in spring and autumn. Due to limited testing capacity, restrictive case definitions, asymptomatic infections, and incomplete testing compliance, case counts represent only a lower bound of SARS-CoV-2 infection incidence. We estimated this incidence from February 2020 to January 2021 by jointly modelling seroprevalence and surveillance data.MethodsWe developed a hierarchical Bayesian model that jointly fits seroprevalence, hospitalization, and mortality data to a shared latent incidence curve, represented by a spline. The model accounts for time-varying serological test sensitivity (reflecting seroconversion and seroreversion) using informative priors, and simultaneously estimates test specificity, infection-to-event distributions, and time-varying infection hospitalization rates (IHR) and infection fatality rates (IFR). Seroprevalence data comprised 37,235 samples from two repeated cross-sectional studies: residual laboratory samples tested with the EuroImmun IgG ELISA and blood donor samples tested with the Wantai Ab ELISA. Hospitalization and mortality counts were obtained from national COVID-19 surveillance.ResultsBy early 2021, an estimated 19.0% (95% Credible Interval (CrI) 17.4-20.7), 13.6% (CrI 11.5-15.8) and 10.8% (CrI 8.7-13.2) of the Belgian 18-49, 50-64 and 65-74 year-olds had been infected with SARS-CoV-2. The first wave mostly affected the younger age group, with a peak weekly incidence of 2.0% (CrI 1.7-2.3) late March 2020. The second wave peaked late October 2020 with weekly incidences of 1.6% (CrI 1.2-2.1) among 65-74 year-olds and 2.8% (CrI 2.4-3.3) among 18-49 year-olds. IHR and IFR were considerably higher in older age groups and declined over time. Among 65-74 year-olds IHR declined from 9.9% (CrI 7.3-14.2) to 5.0% (CrI 3.5-7.1) and IFR from 2.8% (CrI 2.0-4.0) to 1.2% (CrI 0.9-1.7).ConclusionAn estimated 16.3% (CrI 15.1-17.4) of the Belgian adult population had been infected with SARS-CoV-2 by early 2021. Joint modelling of seroprevalence and surveillance data provides a framework for estimating infection burden.

Estimating COVID-19 vaccine effectiveness (VE) by time since vaccination (TSV) is essential for understanding how protection may change over time and enables meaningful comparisons across studies. This is important for accurate comparisons of VE against different SARS-CoV-2 variants/sublineages, across age groups, during different periods post vaccination campaign, or by vaccine type/brand. We provide recommendations for case-control VE studies on estimating and reporting VE analyses by TSV, with the aim of improving quality of these estimates. Our recommendations cover study design and pre-analysis considerations, descriptive analyses, choice of categories of TSV, categorical and continuous modeling approaches, and best practices for reporting VE by TSV. Using a real-life case-control study, we apply these recommendations and include accompanying statistical scripts in R and Stata. These recommendations will serve as a practical resource for researchers conducting VE analyses by TSV. We encourage ongoing refinement of them through input from other study groups.

Respiratory infections remain a major global health burden, causing substantial morbidity and mortality worldwide. The responsible viruses circulate concurrently, potentially affecting each other’s dynamics, yet the extent and direction of such interactions remain poorly understood. Characterising these cross-pathogen effects at the population level is essential for elucidating transmission dynamics and guiding mitigation strategies. Using incidence data from a participatory syndromic surveillance system with multiplex PCR (polymerase chain reaction) confirmation of specific pathogens, we applied complementary statistical approaches, including multivariate regression, endemic–epidemic, and distributed-lag models, to characterise immediate and delayed associations among seven major respiratory diseases. We show that these pathogens form a connected system of temporal associations in which some pairs, such as SARS-CoV-2 and human seasonal coronaviruses, exhibit positive associations in their temporal incidence patterns, primarily from SARS-CoV-2 to human seasonal coronaviruses, whereas others, such as influenza and rhinovirus or parainfluenza virus show negative associations in circulation dynamics. Associations were often directional rather than reciprocal: for instance, rhinovirus was negatively associated with subsequent human seasonal coronaviruses, whereas the reverse pattern was not observed, while positive bidirectional associations between human metapneumovirus and parainfluenza virus were observed in several models. Temporal association patterns were largely consistent across analytical frameworks, suggesting persistent co-circulation dynamics among the studied respiratory viruses. By integrating multiple analytic frameworks, our study provides a comprehensive, data-driven view of patterns of co-circulation and statistical association among respiratory viruses, offering crucial insights for improved epidemic forecasting and mitigation strategies.

The COVID-19 pandemic has exhibited complex, multiwave dynamics with substantial spatial and temporal heterogeneity. In South Africa, repeated waves, driven by variant emergence, shifting public health policies, and uneven vaccine uptake, posed significant challenges to real-time surveillance and predictive modeling. There is a growing need for statistical frameworks that can capture these dynamics while offering interpretable insights for public health planning. We applied a spatio-temporal endemic-epidemic model to daily COVID-19 case counts across nine South African provinces from March 2020 to July 2022. The final model included fixed effects for time trends, seasonality, variant dominance, lagged vaccination coverage, government stringency, and weekend reporting patterns. Spatial transmission was modeled using power-law distance weights, and province-specific random intercepts were included in all components. Transmission was decomposed into endemic (background), autoregressive (within-province), and neighbourhood (interprovincial) contributions. Model validation involved 14-day internal forecasting, with predictive accuracy evaluated using 95

Wastewater-based epidemiology has garnered increasing attention during the COVID-19 pandemic due to its potential for accurate and cost-effective population-level surveillance. In this study, we analyzed wastewater samples collected from six wastewater treatment plants in Tuscany, Italy, between April 2022 and March 2023. We compared SARS-CoV-2 RNA concentrations in wastewater with the number of positive COVID-19 tests provided by the Italian Ministry of Health and observed significant discrepancies between the two throughout the whole time window considered, with viral load ranging from 4 up to 8 orders of magnitude higher that clinical tests. These inconsistencies tend to increase with time by 1-2 orders of magnitude. To investigate the underlying causes of these discrepancies, we developed a Generalized Additive Mixed Model incorporating both clinical testing intensity (using the number of tests performed and the positivity ratio as proxies for testing accuracy) and viral subvariant prevalence. Our results indicate that variations in clinical testing intensity introduce changes in the relationship between their estimates and the wastewater-based time series, with an effect that is more than double the impact of Omicron subvariants. Shifts in viral subvariants produce systematic changes in the wastewater signal with an effect more than double the one of clinical tests. When not taken properly into account, they effectively act as a bias in the relationship between measured concentrations and case numbers.

Abstract Background The COVID-19 pandemic underscored the importance of integrating human behaviour in infectious disease modelling approaches, yet an in-depth assessment of how behavioural components are incorporated remains limited. We conducted a scoping review of COVID-19 models applied to Belgian data to examine how behavioural dynamics, both voluntary and policy-driven, were represented within model structures. Our aim was to identify current practices, highlight methodological gaps, and provide recommendations for the development of behaviourally integrated epidemiological models. Methods Using Scopus and PubMed, we identified 98 studies published between March 2020 and October 2024, describing 105 models in total. Models were classified by model class (mathematical, statistical, or ensemble), objectives, approaches used to incorporate behavioural factors, and types of behaviour data employed. Results Behavioural integration was confined to specific modelling contexts, with only half of the 105 models incorporating behavioural components. Mechanistic models, particularly compartmental models, were the most likely to include behavioural features, especially in studies assessing non-pharmaceutical interventions or conducting long-term forecasts and scenario analyses. Behavioural change was most commonly represented through modifications to transmission parameters or contact matrices. These adjustments were frequently informed by social contact surveys or mobility data derived from various sources. Conclusions In contrast to previous reviews that focused exclusively on behavioural models, this study evaluates the full landscape of Belgian COVID-19 models, offering a comprehensive perspective on how behavioural representation varies across modelling approaches. Our findings recommend that effective behavioural integration relies on timely, routine, and disaggregated surveillance and behaviour data, alongside the use of flexible mechanistic models.

BACKGROUND:Tetanus, diphtheria, acellular pertussis (Tdap) vaccination in pregnancy protects newborns against pertussis, but the influence of gestational age (GA) at vaccination on maternal and neonatal immune profiles remains incompletely understood. This study aimed to characterize the effect of GA at Tdap vaccination on several antibody features during pregnancy and at birth, and to assess transplacental antibody transfer. METHODS:96 pregnant women received Tdap at different GAs between week 16 and 32 within a Belgian, prospective non-randomized controlled trial. Maternal blood was collected pre-vaccination, at multiple timepoints post-vaccination, and at delivery, alongside cord blood at birth. Tdap-specific total IgG and IgG subclasses were evaluated alongside Fc-mediated effector functions. Multivariate analyses were applied to define composite immune patterns. RESULTS:Post-vaccination, robust immune responses were observed across cohorts. At delivery, maternal total IgG and IgG1 against PRN, DT, and TT were higher with later vaccination, whereas other subclasses and functional responses were largely comparable. Cord blood profiles partially paralleled maternal patterns without reaching significance, and no significant effect of vaccination-to-delivery interval was detected. IgG transfer ratios generally declined with advancing GA; functional antibody transfer was largely unaffected. Multivariate analyses highlighted higher maternal antibody response profiles at delivery with later vaccination, while cord blood profiles were generally unaffected. CONCLUSION:Maternal Tdap antibody response profiles at delivery are influenced by vaccination timing, while neonatal antibody profiles at birth appear largely comparable within the limits of the study. These findings support current recommendations for Tdap administration between 16 and 32 weeks of gestation and underscore the flexibility of this window for routine antenatal care.

In 2023 the European Centre for Disease Prevention and Control (ECDC) launched RespiCast, the first European Respiratory Diseases Forecasting Hub, to provide probabilistic forecasts for influenza-like illness (ILI) and acute respiratory infection (ARI) incidence across 26 European countries. During the 2023/24 and 2024/25 winter seasons, RespiCast collected one- to four-week-ahead forecasts from multiple models contributed by different international teams and combined them into an ensemble. Our analysis shows that, when evaluated using the weighted interval score (WIS) and the absolute error (AE), the ensemble consistently outperformed the baseline model (defined as a persistence model that projects the last observed value forward) as well as individual models across most countries and forecasting rounds for both ILI and ARI incidence in the two seasons. Analysis of ensemble coverage (defined as the proportion of times observed values fall within the specified prediction intervals) indicated that forecast prediction intervals were reliable, although a general overconfidence trend (i.e., prediction intervals that are too narrow) was observed, particularly in specific countries. The relative performance of the ensemble declined in certain weeks, likely due to reduced participation from modelling teams, epidemic dynamics, higher data noise, and reporting delays. Forecast scores varied across countries, with some exhibiting consistently higher relative errors than others. Overall, the findings highlight the strengths of ensemble approaches in improving the accuracy and reliability of epidemiological forecasts while identifying areas for improvement, such as managing overconfidence and addressing variability in performance across countries and over time.

The intensifying outbreaks of the novel monkeypox virus clade Ib in the Democratic Republic of the Congo have raised global concern about the potential for wider epidemic spread. Some clade Ib mpox outbreaks have shown a distinct transmission pattern in which transmission associated with both sexual and nonsexual contacts coexist. Here, we characterize these outbreaks in a network epidemic model, which incorporates sexual and nonsexual contacts, and project age- and route-specific transmission potentials under a wide range of scenarios. Our analyses suggest that the dominant route of transmission may shift over time from sexual to nonsexual contacts, which leads to larger epidemics. The age groups contributing most to overall infections and mortality also change over time, suggesting that target groups for intervention should be adjusted accordingly. For countries at risk of travel-associated mpox outbreaks, these findings highlight the importance of monitoring evolving monkeypox virus transmission patterns and interacting transmission routes to support timely and effective control measures.

BACKGROUNDThe World Health Organization aims to eliminate hepatitis B virus (HBV) by 2030 through reducing incidence and mortality. Accurate prevalence estimates are crucial to guide policies and monitor progress towards HBV elimination. However, HBV prevalence can be overestimated when relying solely on hepatitis B surface antigen (HBsAg) because of unconfirmed or false-positive results. Robust screening algorithms to improve diagnostic accuracy and minimise false positives are required.AIMWe conducted a nationwide, population-based serosurvey to estimate HBV prevalence in Belgium by using HBsAg alone or combined with hepatitis B core antibody (anti-HBc) positivity as infection criterion.METHODSWe analysed HBsAg and anti-HBc in a total of 4,955 left-over serum samples from severe acute respiratory syndrome coronavirus 2 sero-epidemiology studies in 2020. Samples were stratified per region, 10-year age band and sex. A confirmatory anti-HBc neutralisation assay was performed in discordant samples.RESULTSWe detected HBsAg in 0.75% (37/4,955) of samples, of which 62.2% (23/37) were anti-HBc-negative and showed no specific anti-HBc signal in the neutralisation assay. None of the samples from ≤ 5-year-olds (n = 87) were double-positive. Weighted analysis estimated HBsAg seroprevalence at 0.74% (95% confidence interval (CI): 0.50-1.04). However, considering double HBsAg and anti-HBc positivity, an HBV prevalence of 0.25% (95% CI: 0.13-0.42) was retained. The HBsAg/anti-HBc prevalence in ≤ 33-year-olds was lower than in older adults (0.079% vs 0.36%; p = 0.015), consistent with Belgium's vaccination policy.CONCLUSIONThis serosurvey reinforces the importance of confirmatory anti-HBc testing in HBsAg-positive samples, particularly in low-endemic countries. Incorporating anti-HBc testing improves the correctness of prevalence estimates.

Human mobility within and between localities is a key determinant influencing the spatio-temporal spread of epidemic diseases. To investigate the role of the global mobility flows in shaping the cross-border transmission of COVID-19 in 30 EU/EEA countries, we analyzed individual mobility from Facebook Travel Patterns and integrated it into a multivariate endemic-epidemic model with fixed and random effects. The models were applied to country-specific surveillance data of weekly reported COVID-19 cases from 23 March 2020 to 18 December 2022. Additional datasets were also considered, such as the Stringency Index, vaccination coverage with assumed waning immunity of primary course and three booster doses, circulating SARS-CoV-2 variants of concern, and country population data. The study found that global mobility derived from empirical data has a valuable role in defining the spatio-temporal spread of COVID-19 during the emergency phase of the pandemic, that is, the diffusion of SARS-CoV-2 between European countries was likely driven by high-flux commuting between neighboring countries. The present approach offered a detailed understanding of disease transmission and enabled the study of fine-grained nuances in a unified framework that is both more pragmatic and less reliant on rigid assumptions.

Individual-based epidemiological models support the study of fine-grained preventive measures, such as tailored vaccine allocation policies, in silico. As individual-based models are computationally intensive, it is pivotal to identify optimal strategies within a reasonable computational budget. Moreover, due to the high societal impact associated with the implementation of preventive strategies, uncertainty regarding decisions should be communicated to policy makers, which is naturally embedded in a Bayesian approach. We present a novel technique for evaluating vaccine allocation strategies using a multi-armed bandit framework in combination with a Bayesian anytime m-top exploration algorithm. m-top exploration allows the algorithm to learn m policies for which it expects the highest utility, enabling experts to inspect this small set of alternative strategies, along with their quantified uncertainty. The anytime component provides policy advisors with flexibility regarding the computation time and the desired confidence, which is important as it is difficult to make this trade-off beforehand. We consider the Belgian COVID-19 epidemic using the individual-based model STRIDE, where we learn a set of vaccination policies that minimize the number of infections and hospitalisations. Through experiments we show that our method can efficiently identify the m-top policies, which is validated in a scenario where the ground truth is available. Finally, we explore how vaccination policies can best be organised under different contact reduction schemes. Through these experiments, we show that the top policies follow a clear trend regarding the prioritised age groups and assigned vaccine type, which provides insights for future vaccination campaigns.