Stochastic numerical simulations show that holidays considerably delay the peak of the season and mitigate its impact. Changes in mixing patterns are responsible for the observed effects, whereas changes in travel behavior do not alter the epidemic. Weekends are important in slowing down the season by periodically dampening transmission. Christmas holidays have the largest impact on the epidemic, however later school breaks may help in reducing the epidemic size, stressing the importance of considering the full calendar. An extension of the Christmas holiday of 1 week may further mitigate the epidemic. Changes in the way individuals establish contacts during holidays are the key ingredient explaining the mitigating effect of regular school closure. Our findings highlight the need to quantify these changes in different demographic and epidemic contexts in order to provide accurate and reliable evaluations of closure effectiveness. They also suggest strategic policies in the distribution of holiday periods to minimize the epidemic impact.

Abstract

European healthcare systems face extreme pressure from coronavirus disease (COVID-19). We relate country-specific accumulated COVID-19 deaths (intensity approach) and active COVID-19 cases (magnitude approach) to measures of healthcare system capacity: hospital beds, healthcare workers and healthcare expenditure. Modelled by the intensity approach with a composite measure for healthcare capacity, the countries experiencing the highest pressure on 25 March 2020 - relative to Italy on 11 March - were Italy, Spain, the Netherlands and France (www.covid-hcpressure.org).

Abstract

There are different patterns in the COVID-19 outbreak in the general population and amongst nursing home patients. We investigate the time from symptom onset to diagnosis and hospitalization or the length of stay (LoS) in the hospital, and whether there are differences in the population. Sciensano collected information on 14,618 hospitalized patients with COVID-19 admissions from 114 Belgian hospitals between 14 March and 12 June 2020. The distributions of different event times for different patient groups are estimated accounting for interval censoring and right truncation of the time intervals. The time between symptom onset and hospitalization or diagnosis are similar, with median length between symptom onset and hospitalization ranging between 3 and 10.4 days, depending on the age of the patient (longest delay in age group 20–60 years) and whether or not the patient lives in a nursing home (additional 2 days for patients from nursing home). The median LoS in hospital varies between 3 and 10.4 days, with the LoS increasing with age. The hospital LoS for patients that recover is shorter for patients living in a nursing home, but the time to death is longer for these patients. Over the course of the first wave, the LoS has decreased.

Abstract

**Objectives** The standard framework of economic evaluation of health programs, which is increasingly used for policy funding decisions, is insufficiently equipped to reflect the full range of health and economic benefits conferred by vaccines and thus undervalues vaccination. **Methods** In 2019, a group of Belgian health economic and clinical experts, supported by 2 senior international vaccination experts (1 American, 1 Belgian), convened 4 roundtable meetings to highlight which particular value elements of vaccination remain neglected in economic evaluations. **Results** They concluded that the standard economic evaluation framework fails to reflect the full value of vaccination with respect to prevention of complications linked to some vaccine-preventable diseases, health gains for caregivers, herd effects, changes in exposure to and distribution of serotypes, the effect on antimicrobial resistance, productivity gains for caregivers and patients, and the distributive implications of vaccination programs. **Conclusions** Here, suggestions are made regarding how these shortcomings can be addressed in future economic evaluations of vaccines and how a more level playing field between vaccines and other health programs can be created.

Results Closing schools during the first lockdown probably resulted in a large learning delay and possibly led to more cases of child abuse. We are uncertain about the effect on the infection rate, hospitalisations, transmission rates, mental health of children, teachers and parents. The panel concluded that the balance of benefits and harms of closing schools clearly shifts against closing schools. Detrimental effects are even worse for vulnerable children. This recommendation is affected by the local virus circulation. Conclusion The guideline panel issues a strong recommendation against closing schools when the virus circulation is low to moderate, and a weak recommendation against closing schools when the virus circulation is high. It does not apply when the school system cannot function due to lack of teachers, too many children who are at home or a shortage of support services. As the results of international studies are consistent with Belgian study results, this recommendation may also be relevant internationally.

As pre-symptomatic transmission is an important driver of COVID-19 epidemics (i.e., the virus is transmitted before the infected individual is aware of its infection), contact tracing efforts struggle to fully control SARS-CoV-2 epidemics. For this reason, the use of universal testing, where each individual of the community is tested on a regular basis, has been suggested. However, the large amount of PCR tests that is required to facilitate this approach, remains an important impediment. Therefore, we propose a new universal testing procedure that is feasible with the current testing capacity, where we rely on PCR test pooling of individuals that belong to the same households. We evaluate this universal testing procedure in a fine-grained epidemiological model (i.e., an individual-based model) that covers the Belgian population. Through this evaluation, we assess the procedure’s performance to keep the epidemic under control, while allowing for various contact reductions. We assess the robustness of the model, by analysing different levels of community compliance, and we show that weekly universal testing could prove a successful strategy to control SARS-CoV-2 outbreaks.

Abstract

The COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., “household bubbles”. The burden of COVID-19 is impacted by both the intensity and frequency of physical contacts, and therefore, household bubbles have the potential to reduce hospital admissions by 90%. In addition, we find that it is crucial to complete contact tracing 4 days after symptom onset. Assumptions on the susceptibility of children affect the impact of school reopening, though we find that business and leisure-related social mixing patterns have more impact on COVID-19 associated disease burden. An optimal deployment of the mitigation policies under study require timely compliance to physical distancing, testing and self-isolation.

Highlights

- Stochastic age-structured discrete time compartmental model - Assess the impact of the lockdown as implemented on March 13, 2020 - Conduct a scenario analysis estimating the impact of possible exit strategies - Model fitted to hospital admission, mortality and serial serological data - A lot of uncertainty remains about the evolution of the epidemic in the next months

Abstract

The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, p80%, while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.

Abstract

Although the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is lasting for more than 1 year, the exposition risks of health-care providers are still unclear. Available evidence is conflicting. We investigated the prevalence of antibodies against SARS-CoV-2 in the staff of a large public hospital with multiple sites in the Antwerp region of Belgium. Risk factors for infection were identified by means of a questionnaire and human resource data. We performed hospital-wide serology tests in the weeks following the first epidemic wave (16 March to the end of May 2020) and combined the results with the answers from an individual questionnaire. Overall seroprevalence was 7.6%. We found higher seroprevalences in nurses [10.0%; 95% confidence interval (CI) 8.9–11.2] than in physicians 6.4% (95% CI 4.6–8.7), paramedical 6.0% (95% CI 4.3–8.0) and administrative staff (2.9%; 95% CI 1.8–4.5). Staff who indicated contact with a confirmed coronavirus disease 2019 (COVID-19) colleague had a higher seroprevalence (12.0%; 95% CI 10.7–13.4) than staff who did not (4.2%; 95% CI 3.5–5.0). The same findings were present for contacts in the private setting. Working in general COVID-19 wards, but not in emergency departments or intensive care units, was also a significant risk factor. Since our analysis points in the direction of active SARS-CoV-2 transmission within hospitals, we argue for implementing a stringent hospital-wide testing and contact-tracing policy with special attention to the health care workers employed in general COVID-19 departments. Additional studies are needed to establish the transmission dynamics.

Background

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Revelance

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It is not yet clear to what extent SARS-CoV-2 infection rates in children reflect community transmission, nor whether infection rates differ between primary schoolchildren and young teenagers. A cross-sectional serosurvey compared the SARS-CoV2 attack-rate in a sample of 362 children recruited from September 21 to October 6, 2020, in primary (ages 6–12) or lower secondary school (ages 12–15) in a municipality with low community transmission (Pelt) to a municipality with high community transmission (Alken) in Belgium. Children were equally distributed over grades and regions. Blood samples were tested for the presence of antibodies to SARS-CoV-2 with an enzyme-linked immunosorbent assay. We found anti-SARS-CoV-2 antibodies in 4.4% of children in the low transmission region and in 14.4% of children in the high transmission region. None of the primary schoolchildren were seropositive in the low transmission region, whereas the seroprevalence among primary and secondary schoolchildren did not differ significantly in the high transmission region. None of the seropositive children suffered from severe disease. Children who were in contact with a confirmed case (RR 2.9; 95%CI 1.6–4.5), who participated in extracurricular activities (RR 5.6; 95%CI 1.2–25.3), or whose caregiver is a healthcare worker who had contact with COVID-19 patients (RR 2.2; 95%CI 1.0–4.6) were at higher risk of seropositivity. If SARS-CoV2 circulation in the community is high, this will be reflected in the pediatric population with similar infection rates in children aged 6–12 years and 12–15 years.

Abstract

Using publicly available data on the number of new hospitalisations we use a newly developed statistical model to produce a phase portrait to monitor the epidemic allowing for assessing whether or not intervention measures are needed to keep hospital capacity under control. The phase portrait is called a cliquets’ diagram, referring to the discrete alarm phases it points to. Using this cliquets’ diagram we show that intervention measures were associated with an effective mitigation of a Summer resurgence but that too little too late was done to prevent a large autumn wave in Belgium.

Abstract

In Belgium, high-risk contacts of an infected person were offered PCR-testing irrespective of their vaccination status. We estimated vaccine effectiveness (VE) against infection and onwards transmission, controlling for previous infections, household-exposure and temporal trends. We included 301,741 tests from 25 January to 24 June 2021. Full-schedule vaccination was associated with significant protection against infection. In addition, mRNA-vaccines reduced onward transmission: VE-estimates increased to >90% when index and contact were fully vaccinated. The small number of viral-vector vaccines included limited interpretability.

What is the possible role of children in SARS-CoV-2 transmission? This cohort study including 63 children and 118 adults found no significant difference between the number of children and the number of adults testing positive for SARS-CoV-2 infection during the study period; children were asymptomatic significantly more often compared with adults (46% vs 13%). In addition, a reconstruction of the outbreak showed that most transmission events originated from within the school. These results suggest that children may play a larger role in the transmission of SARS-CoV-2 than previously assumed.

Abstract

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.

Abstract

Phenomenological models are popular for describing the epidemic curve. We present how they can be used at different phases in the epidemic, by modelling the daily number of new hospitalisations (or cases). As real-time prediction of the hospital capacity is important, a joint model of the new hospitalisations, number of patients in hospital and in intensive care unit (ICU) is proposed. This model allows estimation of the length of stay in hospital and ICU, even if no (or limited) individual level information on length of stay is available. Estimation is done in a Bayesian framework. In this framework, real-time alarms, defined as the probability of exceeding hospital capacity, can be easily derived. The methods are illustrated using data from the COVID-19 pandemic in March–June 2020 in Belgium, but are widely applicable.

Abstract

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.

Summary

Basic transmission dynamic characteristics of SARS-CoV-2, such as the probability of acquiring infection when exposed (“susceptibility”), and the probability of transmitting infection when infected (“infectiousness”) may be age-dependent. We present a computational method to estimate these age-specific characteristics using Belgian social contact and surveillance data. We found that children are less susceptible to infection than adults, with the former experiencing 20% to 50% of the susceptibility in adults, while the infectiousness is more difficult to discern. The force of infection (probability of acquiring infection per unit time) decreases over time for the oldest age groups first, following the roll-out of the vaccination campaign which targeted the elderly first.

Abstract

Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models. Current research focuses on optimizing policies w.r.t. a single objective, such as the pathogen's attack rate. However, as the mitigation of epidemics involves distinct, and possibly conflicting criteria (i.a., prevalence, mortality, morbidity, cost), a multi-objective approach is warranted to learn balanced policies. To lift this decision-making process to real-world epidemic models, we apply deep multi-objective reinforcement learning and build upon a state-of-the-art algorithm, Pareto Conditioned Networks (PCN), to learn a set of solutions that approximates the Pareto front of the decision problem. We consider the first wave of the Belgian COVID-19 epidemic, which was mitigated by a lockdown, and study different deconfinement strategies, aiming to minimize both COVID-19 cases (i.e., infections and hospitalizations) and the societal burden that is induced by the applied mitigation measures. We contribute a multi-objective Markov decision process that encapsulates the stochastic compartment model that was used to inform policy makers during the COVID-19 epidemic. As these social mitigation measures are implemented in a continuous action space that modulates the contact matrix of the age-structured epidemic model, we extend PCN to this setting. We evaluate the solution returned by PCN, and observe that it correctly learns to reduce the social burden whenever the hospitalization rates are sufficiently low. In this work, we thus show that multi-objective reinforcement learning is attainable in complex epidemiological models and provides essential insights to balance complex mitigation policies.

Abstract

The vaccination coverage in Flanders is high, but some regions show lower vaccination willingness as compared to the overall vaccination coverage. Beginning November of 2021, the vaccination rate in Flanders was above 93% in age groups above 45 years, and around 85% in the age groups 12 to 44 years. Apart from Flanders as a whole, focus here is on the health sector Maasland, which has a slightly lower vaccination rate, especially in the age groups 12 to 44 years. In the Maasland region, located on the eastern border of Flanders, there are between 1% and 10% less vaccinated individuals than expected according to the vaccination rate in the whole of Flanders, with lowest vaccination rates in the south of the Maasland region. We study the impact of ethnic diversity in the population, population composition with respect to the ethnicity of individuals (in the sense of how the local population composition differs from the Flemish average), and socio-economic status on the vaccination rate at the level of the statistical sector, apart from the effect of age. We explain the statistical methods to investigate geographical differences and illustrate how one can deal with incomplete information in vaccination registries. Ethnic diversity in a region is associated with lower vaccination rates, as is a lower regional socio-economic status. The composition of the population in Maasland is associated with a 35% reduction in the odds to get vaccinated as compared to the overall Flemish population.

Summary

To investigate the effect of different sources of superspreading on disease dynamics, we implemented superspreading driven by heterogeneity in infectiousness and heterogeneity in contact behavior into an individual-based model for the transmission of SARS-CoV-2 in the Belgian population. We compared the impact of both forms of superspreading in a scenario without interventions as well as in a scenario in which a period of strict social distancing (i.e. a lockdown) is followed by a period of partial release. We found that both forms of superspreading have very different effects. On the one hand, increasing the level of infectiousness-related heterogeneity led to less outbreaks being observed following the introduction of one infected individual in the population. Furthermore, final outbreak sizes decreased, and outbreaks became slower, with lower and later peaks, and a lower herd immunity threshold. Finally, the risk for resurgence of an outbreak following a period of lockdown also decreased. On the other hand, when contact-related heterogeneity was high, this also led to smaller final sizes, but caused outbreaks to be more explosive regarding other aspects (such as higher peaks that occurred earlier). The herd immunity threshold also increased, as did the risk of resurgence of outbreaks.

Abstract

We used a network model to simulate a mpox epidemic among men who have sex with men. Our findings suggest that unrecognized infections have an important impact on the epidemic, and that vaccination of individuals at highest risk of infection reduces epidemic size more than post-exposure vaccination of sexual partners.

Abstract

Excess mortality, rather than reported COVID-19 deaths has been suggested to evaluate the impact of the SARS-CoV-2 induced Corona Virus Disease (COVID-19) pandemic on mortality. However, the relationship between excess mortality and COVID-19 mortality is perturbed by seasonal phenomena, such as extreme temperatures and seasonal influenza. Models used to estimate excess mortality often ignore these underlying patterns. We propose a dynamic linear state-space model to estimate all-cause mortality, which accounts for extreme temperatures above 25°C and seasonal influenza via the Goldstein index. The state-space model prediction of the excess mortality that is not explained by heat waves and seasonal influenza coincides with the reported COVID-19 mortality in the year 2020 in Belgium.

Highlights

- COVID-19 outbreaks in long term care facilities declined shortly after vaccine introduction. - This decline is observed despite increasing incidence rates in the general population. - Outbreaks after vaccination were shorter and involved fewer residents. - Unvaccinated healthcare workers were more often involved in COVID-19 outbreaks than vaccinated ones.

Abstract

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.