Predictors of moderate-to-severe side-effects following COVID-19 mRNA booster vaccination: A prospective cohort study among primary health care providers in Belgium. (preprint)

Background COVID-19 vaccine effectiveness declines months after vaccination. Therefore, it is likely that during the next few years, people may be repeatedly offered a booster vaccine to enhance humoral immunity levels. A growing number of people are questioning whether the benefits of a booster vaccine outweigh the side-effects.Objective This study aims (1) to identify the most frequently reported side-effects after different doses of COVID-19 mRNA vaccines, (2) and the longest lasting symptoms; and (3) to predict the likelihood of having moderate-to-severe side-effects after a booster COVID-19 mRNA vaccine given individual- and vaccine-specific characteristics.Design, setting, and participants : Secondary analysis of a prospective cohort study in primary health care providers (PHCPs) in Belgium conducted between December 2020 and December 2021, and in February-March 2023.Methods In nine subsequent surveys over a period of 2 years vaccine dose-number and side-effects after COVID-19 vaccines were collected. A Generalized Estimation Equations approach on the data of the first and second booster dose was used to investigate the probability of having moderate-to-severe side-effects after mRNA booster vaccination. Predictive performance of a binary classifier was assessed by looking at discrimination (i.e., quantified in terms of the area under the receiver operating characteristic curve). The final prediction model was validated using data with regard to the third booster by assessing misclassification rate, sensitivity and specificity.Results In total, 11% of the PHCPs had moderate-to-severe side-effects after their booster COVID-19 mRNA vaccine. The most common side-effects of COVID-19 mRNA doses included fatigue, local pain at the injection site, general pains, and headache. These side-effects typically lasted for a median of 1 to 2 days. The final model included five predictors: sex, alcohol consumption, history of moderate-to-severe side-effects after any previous dose, recent COVID-19 infection, and the booster dose-number (first, second). Having experienced moderate-to-severe side-effects after any previous dose was the strongest predictor of moderate-to-severe side-effects following an mRNA vaccine booster, with an odds ratio (OR) of 3.64 (95% CI: 2.80–4.75). The OR for female sex was 1.49 (95% CI: 1.21–1.84) implying that females have a higher odds of moderate-to-severe side-effects following booster vaccination. The differences in effect for booster dose-number, alcohol consumption and recent COVID-19 infection was not significant.Conclusion and Relevance: COVID-19 mRNA booster vaccination implies a low prevalence of moderate-to-severe side-effects among PHCPs, with a short median duration of symptoms if any. The strongest predictors are a history of moderate-to-severe side-effects after any previous dose and being female. These reassuring findings can help addressing concerns about booster vaccination and encourage their uptake.Trial Registration: NCT04779424

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning (preprint)

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.

Different forms of superspreading lead to different outcomes: heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2 (preprint)

Superspreading events play an important role in the spread of SARS-CoV-2 and several other pathogens. Hence, while the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness and susceptibility, variations in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, our understanding of the effect that these different forms of superspreading have on the spread of pathogens and the effectiveness of control measures remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in the Belgian population. We considered its impact on viral spread as well as on the effectiveness of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are very different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in less outbreaks occurring following the introduction of one infected individual. Outbreaks were also slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown 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 in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. Determining the contribution of both source of heterogeneity is therefore important but left to be explored further. Author summaryTo 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.

Seroconversion rate after primary vaccination with two doses of BNT162b2 versus mRNA-1273 in solid organ transplant recipients: a systematic review and meta-analysis (preprint)

In the general population, the seroconversion rate after primary vaccination with two doses of anti-SARS-CoV-2 mRNA vaccine reaches nearly 100%, with significantly higher antibody titers after mRNA-1273 vaccination compared to BNT162b2 vaccination. Here, we performed a systematic review and meta-analysis to compare the antibody response after two-dose mRNA-1273 versus BNT162b2 vaccination in solid organ transplant (SOT) recipients. A systematic literature research was performed in Pubmed, Web of Science, and the Cochrane library and original research papers were included for a meta-analysis to calculate vaccine-specific seroconversion rates for each of the mRNA vaccines. Next, the pooled relative seroconversion rate was estimated. Six studies that described the development of antibodies against receptor-binding domain (RBD) and/or S1 subunit of the spike protein were eligible for meta-analysis. Two of them also reported antibody titers. The meta-analysis revealed lower seroconversion rates in SOT recipients vaccinated with two doses of BNT162b2 (45.2%; 95% confidence interval (CI) 32.5%-58.3%) than patients vaccinated with two doses of mRNA-1273 (60.4%; 95% CI 47.4%-72.7%. The relative seroconversion rate amounted 0.79 (95% CI 0.71-0.88). This systematic review and meta-analysis indicates that, in SOT recipients, higher seroconversion rates were observed after vaccination with mRNA-1273 compared to BNT162b2.

mRNA-1273 vaccine (Moderna): a better option than BNT162b2 (Pfizer) in kidney transplant recipients and dialysis patients? (preprint)

In this study of the humoral immune response after the first dose of SARS-CoV-2 mRNA vaccine, low seroconversion rates were noted in both kidney transplant recipients and dialysis patients. However, vaccination with the mRNA-1273 vaccine (Moderna) resulted in both higher seroconversion rates and mean antibody titers compared to BNT162b2 (Pfizer).

Time between Symptom Onset, Hospitalisation and Recovery or Death: a Statistical Analysis of Different Time-Delay Distributions in Belgian COVID-19 Patients (preprint)

BackgroundThere are different patterns in the COVID-19 outbreak in the general population and amongst nursing home patients. Different age-groups are also impacted differently. However, it remains unclear whether the time from symptom onset to diagnosis and hospitalization or the length of stay in the hospital is different for different age groups, gender, residence place or whether it is time dependent. MethodsSciensano, the Belgian Scientific Institute of Public Health, collected information on hospitalized patients with COVID-19 hospital admissions from 114 participating hospitals in Belgium. Between March 14, 2020 and June 12, 2020, a total of 14,618 COVID-19 patients were registered. The time of symptom onset, time of COVID-19 diagnosis, time of hospitalization, time of recovery or death, and length of stay in intensive care are recorded. The distributions of these different event times for different age groups are estimated accounting for interval censoring and right truncation in the observed data. ResultsThe truncated and interval-censored Weibull regression model is the best model for the time between symptom onset and diagnosis/hospitalization best, whereas the length of stay in hospital is best described by a truncated and interval-censored lognormal regression model. ConclusionsThe time between symptom onset and hospitalization and between symptom onset and diagnosis are very similar, with median length between symptom onset and hospitalization ranging between 3 and 10.4 days, depending on the age of the patient and whether or not the patient lives in a nursing home. Patients coming from a nursing home facility have a slightly prolonged time between symptom onset and hospitalization (i.e., 2 days). The longest delay time is observed in the age group 20-60 years old. The time from symptom onset to diagnosis follows the same trend, but on average is one day longer as compared to the time to hospitalization. The median length of stay in hospital varies between 3 and 10.4 days, with the length of stay increasing with age. However, a difference is observed between patients that recover and patients that die. While the hospital length of stay for patients that recover increases with age, we observe the longest time between hospitalization and death in the age group 20-60. And, while the hospital length of stay for patients that recover is shorter for patients living in a nursing home, the time from hospitalization to death is longer for these patients. But, over the course of the first wave, the length of stay has decreased, with a decrease in median length of stay of around 2 days.

The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19: an individual-based modelling study (preprint)

Background. The rising COVID-19 pandemic caused many governments to impose policies restricting social interactions. These policies have slowed down the spread of the SARS-CoV-2 virus to the extent that restric- tions can be gradually lifted. Models can be useful to assess the consequences of deconfinement strategies with respect to business, school and leisure activities. Methods. We adapted the individual-based model "STRIDE" to simulate interactions between the 11 million inhabitants of Belgium at the levels of households, workplaces, schools and communities. We calibrated our model to observed hospital incidence and seroprevalence data. STRIDE can explore contact tracing options and account for repetitive leisure contacts in extended household settings (so called "household bubbles") with varying levels of connectivity. Findings. Household bubbles have the potential to reduce the number of COVID-19 hospital admissions by up to 90%. The effectiveness of contact tracing depends on its timing, as it becomes futile more than 4 days after the index case developed symptoms. Assuming that children have a lower level of susceptibility and lower probability to experience symptomatic SARS-CoV-2 infection, (partial) school closure options have relatively little impact on COVID-19 burden. Interpretation. Not only the absolute number and intensity of physical contacts drive the transmission dynamics and COVID-19 burden, also their repetitiveness is influential. Contact tracing seems essential for a controlled and persistent release of lockdown measures, but requires timely compliance to testing, reporting and self-isolation. Rapid tracing and testing, and communication ensuring continued involvement of the population are therefore essential.

Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories (preprint)

Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months.

Belgian Covid-19 Mortality, Excess Deaths, Number of Deaths per Million, and Infection Fatality Rates (8 March - 9 May 2020) (preprint)

Objective. Scrutiny of COVID-19 mortality in Belgium over the period 8 March-9 May 2020 (Weeks 11-19), using number of deaths per million, infection fatality rates, and the relation between COVID-19 mortality and excess death rates. Data. Publicly available COVID-19 mortality (2020); overall mortality (2009-2020) data in Belgium and demographic data on the Belgian population; data on the nursing home population; results of repeated sero-prevalence surveys in March-April 2020. Statistical methods. Reweighing, missing-data handling, rate estimation, visualization. Results. Belgium has virtually no discrepancy between COVID-19 reported mortality (confirmed and possible cases) and excess mortality. There is a sharp excess death peak over the study period; the total number of excess deaths makes April 2020 the deadliest month of April since WWII, with excess deaths far larger than in early 2017 or 2018, even though influenza-induced January 1951 and February 1960 number of excess deaths were similar in magnitude. Using various sero-prevalence estimates, infection fatality rates (IFRs; fraction of deaths among infected cases) are estimated at 0.38-0.73% for males and 0.20-0.39% for females in the non-nursing home population (non-NHP), and at 0.79-1.52% for males and 0.88-1.31% for females in the entire population. Estimates for the NHP range from 38 to 73% for males and over 22 to 37% for females. The IFRs rise from nearly 0% under 45 years, to 4.3% and 13.2% for males in the non-NHP and the general population, respectively, and to 1.5% and 11.1% for females in the non-NHP and general population, respectively. The IFR and number of deaths per million is strongly influenced by extensive reporting and the fact that 66.0% of the deaths concerned NH residents. At 764 (our re-estimation of the figure 735, presented by "Our World in Data"), the number of COVID-19 deaths per million led the international ranking on May 9, 2020, but drops to 262 in the non-NHP. The NHP is very specific: age-related increased risk; highly prevalent comorbidities that, while non-fatal in themselves, exacerbate COVID-19; larger collective households that share inadvertent vectors such as caregivers and favor clustered outbreaks; initial lack of protective equipment, etc. High-quality health care countries have a relatively older but also more frail population [1], which is likely to contribute to this result.

Seroprevalence of IgG antibodies against SARS coronavirus 2 in Belgium: a prospective cross-sectional study of residual samples (preprint)

Background In the first weeks of the COVID-19 epidemic in Belgium, a repetitive national serum collection was set up to monitor age-related exposure through emerging SARS-CoV-2 antibodies. First objective was to estimate the baseline seroprevalence and seroincidence using serial survey data that covered the start of a national lock-down period installed soon after the epidemic was recognized. Methods A prospective serial cross-sectional seroprevalence study, stratified by age, sex and region, started with two collections in April 2020. In residual sera taken outside hospitals and collected by diagnostic laboratories, IgG antibodies against S1 proteins of SARS-CoV-2 were measured with a semi-quantitative commercial ELISA. Seropositivity (cumulative, by age category and sex) and seroincidence over a 3 weeks period were estimated for the Belgian population. Findings In the first collection, IgG antibodies were detected in 100 out of 3910 samples, whereas in the second collection 193 out of 3391 samples were IgG positive. The weighted overall seroprevalence increased from 2.9% (95% CI 2.3 to 3.6) to 6.0% (95% CI 5.1 to 7.1), reflected in a seroincidence estimate of 3.1% (95% CI 1.9 to 4.3). Age-specific seroprevalence significantly increased in the age categories 20-30, 80-90 and [≥]90. No significant sex effect was observed. Interpretation During the start of epidemic mitigation by lockdown, a small but increasing fraction of the Belgian population showed serologically detectable signs of exposure to SARS-CoV-2. Funding This independent researcher-initiated study acknowledges financial support from the Antwerp University Fund, the Flemish Research Fund, and European Horizon 2020.