A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

SARS-CoV-2 vaccine antibody response and breakthrough infections in transplant recipients.

Rates and modulators of SARS-CoV-2 vaccine nonresponse and breakthrough infections remain unclear in serially vaccinated transplant recipients. In a prospective, mono-centric, observational study, 1878 adult solid organ and hematopoietic cell transplant recipients, with prior SARS-CoV-2 vaccination, were included between March 2021 and February 2022. SARS-CoV-2 anti-spike IgG antibodies were measured at inclusion and details on SARS-CoV-2 vaccine doses and infection were collected. No life-threatening adverse events were reported after a total of 4039 vaccine doses. In transplant recipients without prior SARS-CoV-2 infection (n = 1636), antibody response rates ranged widely, from 47% in lung transplant to 90% in liver transplant and 91% in hematopoietic cell transplant recipients after third vaccine dose. Antibody positivity rate and levels increased after each vaccine dose in all types of transplant recipients. In multivariable analysis, older age, chronic kidney disease and daily dose of mycophenolate and corticosteroids were negatively associated with antibody response rate. Overall rate of breakthrough infections was 25.2% and mainly (90.2%) occurred after third and fourth vaccine dose. Lung transplant recipients had the highest rates of severe breakthrough infection (10.5%) and death (2.5%). In multivariable analysis, older age, daily dose of mycophenolate and corticosteroids were associated with severe breakthrough infection. Transplant recipients with infection before first vaccine dose (n = 160) had higher antibody response rates and levels after each vaccine dose, and a significantly lower overall rate of breakthrough infections compared to those without prior infection. Antibody response after SARS-CoV-2 vaccination and rate of severe breakthrough infections vary largely between different transplant types and are modulated by specific risk factors. The observed heterogeneity supports a tailored approach against COVID-19 in transplant recipients.

A longitudinal perspective on perceived vulnerability to disease during the COVID-19 pandemic in Belgium.

During the coronavirus disease 2019 pandemic, individuals relied heavily on media sources to stay informed about the disease and public health measures. However, differences exist in the type and frequency of news media consumption, which can be linked to their perceived vulnerability to disease. In this longitudinal study, 1000 Flemish (Belgium) individuals were followed from March 2020 until September 2020, focussing on the evolution in perceived vulnerability to disease (i.e. perceived infectability and germ aversion). Media consumption significantly impacts perceived germ aversion; heavy consumers of commercial media reported greater germ aversion than light consumers of these media. The evolution of germ aversion among individuals from March to August depends on their gender, living environment, age and possibility to work from home. Furthermore, the evolution of perceived infectability depends on the age and living environment of the respondent. These findings may interest policy makers and media professionals to anticipate how anxieties regarding contracting an infectious disease evolve over time and how individual characteristics affect this evolution.

A linear mixed model to estimate COVID-19-induced excess mortality.

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.

Do optimism and moralization predict vaccination? A five-wave longitudinal study.

OBJECTIVE: To examine if personal and comparative optimism, perceived effectiveness, and moralization of vaccination predict people's decision to get vaccinated. METHODS: We measured self-reported vaccination decisions in a five-wave longitudinal study (N ≍ 5,000/wave) in Belgium over a six months period (December 2020-May 2021) during the COVID-19 pandemic. Among the predictors were demographic factors, personal and comparative optimism for three aspects of COVID-19 (infection, severe disease, good outcome), perceived effectiveness of vaccination, and the extent to which vaccination is being viewed in prosocial terms (altruism, civic spirit) versus as instrumental in one's self-interest (common sense, concern about one's health). RESULTS: The actual availability of vaccines changed people's outlook on vaccination. Marked differences emerged in vaccination decision between linguistic-cultural regions (Flemish Region, Walloon Region, Brussels Capital Region). Personal and comparative optimism predicted vaccination decisions to different extents depending on participants' age and on whether the optimism was for infection, severe disease, or a good outcome. In older participants, vaccination decision was mostly predicted by personal optimism; in younger participants, it was mostly predicted by comparative optimism. Moralizing vaccination predicted a lower likelihood of a positive vaccination decision, that is, higher vaccine hesitancy or refusal, particularly in older participants. CONCLUSIONS: Assessments of risk perception serving to inform vaccination campaigns should differentiate between expectations concerning the risk of infection and expectations concerning the outcome of an infection. Public health messages should address comparative optimism, particularly when targeting younger populations. Contrary to popular belief, moralizing vaccination may reduce the willingness to get vaccinated. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Fractal dimension based geographical clustering of COVID-19 time series data.

Understanding the local dynamics of COVID-19 transmission calls for an approach that characterizes the incidence curve in a small geographical unit. Given that incidence curves exhibit considerable day-to-day variation, the fractal structure of the time series dynamics is investigated for the Flanders and Brussels Regions of Belgium. For each statistical sector, the smallest administrative geographical entity in Belgium, fractal dimensions of COVID-19 incidence rates, based on rolling time spans of 7, 14, and 21 days were estimated using four different estimators: box-count, Hall-Wood, variogram, and madogram. We found varying patterns of fractal dimensions across time and location. The fractal dimension is further summarized by its mean, variance, and autocorrelation over time. These summary statistics are then used to cluster regions with different incidence rate patterns using k-means clustering. Fractal dimension analysis of COVID-19 incidence thus offers important insight into the past, current, and arguably future evolution of an infectious disease outbreak.

A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data.

This work presents a joint spatial modeling framework to improve estimation of the spatial distribution of the latent COVID-19 incidence in Belgium, based on test-confirmed COVID-19 cases and crowd-sourced symptoms data as reported in a large-scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID-19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide valuable auxiliary data to optimize spatial COVID-19 incidence estimation based on confirmed cases in situations with limited testing capacity. Furthermore, it is shown that an online survey on COVID-19 symptoms with a sufficiently large sample size per spatial entity is capable of pinpointing the same locations that appear as test-confirmed clusters, approximately 1 week earlier. We conclude that a large-scale online study provides an inexpensive and flexible method to collect timely information of an epidemic during its early phase, which can be used by policy makers in an early phase of an epidemic and in conjunction with other monitoring systems.

Spatio-temporal model to investigate COVID-19 spread accounting for the mobility amongst municipalities.

The rapid spread of COVID-19 worldwide led to the implementation of various non-pharmaceutical interventions to limit transmission and hence reduce the number of infections. Using telecom-operator-based mobility data and a spatio-temporal dynamic model, the impact of mobility on the evolution of the pandemic at the level of the 581 Belgian municipalities is investigated. By decomposing incidence, particularly into within- and between-municipality components, we noted that the global epidemic component is relatively more important in larger municipalities (e.g., cities), while the local component is more relevant in smaller (rural) municipalities. Investigation of the effect of mobility on the pandemic spread showed that reduction of mobility has a significant impact in reducing the number of new infections.

The role of biostatistics in the response to COVID-19: a Belgian and international perspective.

In this commentary to Dattner et al. (Israel J Health Policy Res. 11:22, 2022), we highlight similarities and differences in the role that biostatistics and biostatisticians have been playing in the COVID-19 response in Belgium and Israel. We bring out implications and opportunities for our field and for science. We argue that biostatistics has an important place in the multidisciplinary COVID-19 response, in terms of research, policy advice, and science and public communication. In Belgium, biostatisticians located in various institutes, collaborated with epidemiologists, vaccinologists, infectiologists, immunologists, social scientists, and government policy makers to provide rapid and science-informed policy advice. Biostatisticians, who can easily be mobilized to work together in pandemic response, also played a role in public communication.

Self-uniqueness beliefs and adherence to recommended precautions. A 5-wave longitudinal COVID-19 study.

RATIONALE: Research on health-related self-uniqueness beliefs suggested that these beliefs might predict adherence to precautions against COVID-19. OBJECTIVE: We examined if comparative optimism (believing that one is less at less than others), self-superiority (believing that one already adheres better to precautions than others), and egocentric impact perception (believing that adverse events affect oneself more than others) predicted intended adherence to precautions. METHOD: We measured self-reported intentions, optimism for self and others, perceived past adherence by self and others, and perceived impact of the measures and the disease on self and others in a 5-wave longitudinal study in December 2020-May 2021 (N ≈ 5000/wave). The sample was in key respects representative for the Belgian population. We used joint models to examine the relationship between self-uniqueness beliefs and intended adherence to the precautions. RESULTS: Believing that COVID-19 would affect one's own life more than average (egocentric impact perception) was associated with higher intentions to adhere to precautions, as was believing that the precautions affected one's life less than average (allocentric impact perception). Self-superiority concerning past adherence to precautions and comparative optimism concerning infection with COVID-19 were associated with higher intended adherence, regardless of whether their non-comparative counterparts (descriptive norm, i.e., perceived adherence to precautions by others, and personal optimism, respectively) were controlled for. Comparative optimism for severe disease and for good outcome were associated with lower intended adherence if personal optimism was not controlled for, but with higher intended adherence if it was controlled for. CONCLUSION: Self-uniqueness beliefs predict intended adherence to precautions against COVID-19, but do so in different directions.

Self-uniqueness beliefs and adherence to recommended precautions. A 5-wave longitudinal COVID-19 study

Rationale. Research on health-related self-uniqueness beliefs suggested that these beliefs might predict adherence to precautions against COVID-19. Objective We examined if comparative optimism (believing that one is less at less than others), self-superiority (believing that one already adheres better to precautions than others), and egocentric impact perception (believing that adverse events affect oneself more than others) predicted intended adherence to precautions. Method We measured self-reported intentions, optimism for self and others, perceived past adherence by self and others, and perceived impact of the measures and the disease on self and others in a 5-wave longitudinal study in December 2020–May 2021 (N ≈ 5000/wave). The sample was in key respects representative for the Belgian population. We used joint models to examine the relationship between self-uniqueness beliefs and intended adherence to the precautions. Results Believing that COVID-19 would affect one's own life more than average (egocentric impact perception) was associated with higher intentions to adhere to precautions, as was believing that the precautions affected one's life less than average (allocentric impact perception). Self-superiority concerning past adherence to precautions and comparative optimism concerning infection with COVID-19 were associated with higher intended adherence, regardless of whether their non-comparative counterparts (descriptive norm, i.e., perceived adherence to precautions by others, and personal optimism, respectively) were controlled for. Comparative optimism for severe disease and for good outcome were associated with lower intended adherence if personal optimism was not controlled for, but with higher intended adherence if it was controlled for. Conclusion Self-uniqueness beliefs predict intended adherence to precautions against COVID-19, but do so in different directions.

The doubling effect of COVID-19 cases on key health indicators.

From the beginning of the COVID-19 pandemic, researchers advised policy makers to make informed decisions towards the adoption of mitigating interventions. Key easy-to-interpret metrics applied over time can measure the public health impact of epidemic outbreaks. We propose a novel method which quantifies the effect of hospitalizations or mortality when the number of COVID-19 cases doubles. Two analyses are used, a country-by-country analysis and a multi-country approach which considers all countries simultaneously. The new measure is applied to several European countries, where the presence of different variants, vaccination rates and intervention measures taken over time leads to a different risk. Based on our results, the vaccination campaign has a clear effect for all countries analyzed, reducing the risk over time. However, the constant emergence of new variants combined with distinct intervention measures impacts differently the risk per country.

Comparing COVID-19 incidences longitudinally per economic sector against the background of preventive measures and vaccination.

In the COVID-19 pandemic, workplace transmission plays an important role. For this type of transmission, the longitudinal 14-day incidence curve of SARS-CoV-2 infections per economic sector is a proxy. In Belgium, a census of confirmed 14-day incidences per NACE-BEL sector level three is available from September 2020 until June 2021, encompassing two waves of infections. However, these high-dimensional data, with a relatively small number of NACE-BEL sectors, are challenging to analyze. We propose a nonlinear Gaussian-Gaussian model that combines parametric and semi-parametric elements to describe the incidence curves with a small set of meaningful parameters. These parameters are further analyzed with conventional statistical methods, such as CCA and linear models, to provide insight into predictive characteristics of the first wave for the second wave. Those nonlinear models classify economic sectors into three groups: sectors with two regular waves of infections, sectors with only a first wave and sectors with a more irregular profile, which may indicate a clear effect of COVID-19 vaccination. The Gaussian-Gaussian model thus allows for analyzing and comparing incidence curves and to bring out key characteristics of such curves. Finally, we consider in which other settings the proposed approach could be applied, together with possible pitfalls.

Risk factors for SARS-CoV-2 transmission in student residences: a case-ascertained study.

BACKGROUND: We aimed to investigate the overall secondary attack rates (SAR) of COVID-19 in student residences and to identify risk factors for higher transmission. METHODS: We retrospectively analysed the SAR in living units of student residences which were screened in Leuven (Belgium) following the detection of a COVID-19 case. Students were followed up in the framework of a routine testing and tracing follow-up system. We considered residence outbreaks followed up between October 30th 2020 and May 25th 2021. We used generalized estimating equations (GEE) to evaluate the impact of delay to follow-up, shared kitchen or sanitary facilities, the presence of a known external infection source and the recent occurrence of a social gathering. We used a generalized linear mixed model (GLMM) for validation. RESULTS: We included 165 student residences, representing 200 residence units (N screened residents = 2324). Secondary transmission occurred in 68 units which corresponded to 176 secondary cases. The overall observed SAR was 8.2%. In the GEE model, shared sanitary facilities (p = 0.04) and the recent occurrence of a social gathering (p = 0.003) were associated with a significant increase in SAR in a living unit, which was estimated at 3% (95%CI 1.5-5.2) in the absence of any risk factor and 13% (95%CI 11.4-15.8) in the presence of both. The GLMM confirmed these findings. CONCLUSIONS: Shared sanitary facilities and the occurrence of social gatherings increase the risk of COVID-19 transmission and should be considered when screening and implementing preventive measures.

The Effect of Transborder Mobility on COVID-19 Incidences in Belgium.

Belgium is a geographically small country bordered by The Netherlands, France, Germany, and Luxembourg, with intense transborder mobility, defined as mobility in the border regions with neighboring countries. It is therefore of interest to examine how the 14-day COVID-19 confirmed case incidence in the border regions is influenced by that of the adjacent regions in the neighboring countries and thus, whether and how it differs from that in the adjacent non-border regions within Belgium. To this end, the 14-day COVID-19 confirmed case incidence is studied at the level of Belgian provinces, well-defined border areas within Belgium, and adjacent regions in the neighboring countries. Auxiliary information encompasses work-related border traffic, travel rates, the proportion of people with a different nationality, the stringency index of the non-pharmaceutical interventions, and the degree of urbanization at the level of the municipality. Especially in transnational urbanized areas such as between the Belgian and Dutch provinces of Limburg and between the Belgian province of Antwerp and the Dutch province of North Brabant, the impact on incidence is visible, at least at some points in time, especially when the national incidences differ between neighboring countries. In contrast, the intra-Belgian language border regions show very little transborder impact on the incidence curves, except around the Brussels capital region, leading to various periods where the incidences are very different in the Dutch-speaking north and the French-speaking south of Belgium. Our findings suggest that while travel restrictions may be needed at some points during a pandemic, a more fine-grained approach than merely closing national borders may be considered. At the same time, in border regions with considerable transborder mobility, it is recommended to coordinate the non-pharmaceutical interventions between the authorities of the various countries overlapping with the border region. While this seems logical, there are clear counterexamples, e.g., where non-essential shops, restaurants, and bars are closed in one country but not in the neighboring country.

Evaluation of Screening Program and Phylogenetic Analysis of SARS-CoV-2 Infections among Hospital Healthcare Workers in Liège, Belgium.

Healthcare workers (HCWs) are known to be at higher risk of developing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections although whether these risks are equal across all occupational roles is uncertain. Identifying these risk factors and understand SARS-CoV-2 transmission pathways in healthcare settings are of high importance to achieve optimal protection measures. We aimed to investigate the implementation of a voluntary screening program for SARS-CoV-2 infections among hospital HCWs and to elucidate potential transmission pathways though phylogenetic analysis before the vaccination era. HCWs of the University Hospital of Liège, Belgium, were invited to participate in voluntary reverse transcriptase-polymerase chain reaction (RT-PCR) assays performed every week from April to December 2020. Phylogenetic analysis of SARS-CoV-2 genomes were performed for a subgroup of 45 HCWs. 5095 samples were collected from 703 HCWs. 212 test results were positive, 15 were indeterminate, and 4868 returned negative. 156 HCWs (22.2%) tested positive at least once during the study period. All SARS-CoV-2 test results returned negative for 547 HCWs (77.8%). Nurses (p < 0.05), paramedics (p < 0.05), and laboratory staff handling respiratory samples (p < 0.01) were at higher risk for being infected compared to the control non-patient facing group. Our phylogenetic analysis revealed that most positive samples corresponded to independent introduction events into the hospital. Our findings add to the growing evidence of differential risks of being infected among HCWs and support the need to implement appropriate protection measures based on each individual's risk profile to guarantee the protection of both HCWs and patients. Furthermore, our phylogenetic investigations highlight that most positive samples correspond to distinct introduction events into the hospital.

Updating 'Perceptions and opinions on the COVID-19 pandemic in Flanders, Belgium' with data of two additional waves of a longitudinal study.

Adding to longitudinal data of three waves that were presented in an original dataset on perceptions and behaviours regarding government measures, fear of getting ill, and media use during the COVID-19 pandemic in Flanders (Belgium), this article presents information on two additional waves that were collected at two key moments in the pandemic in the same region: in late August 2020 (W4; as infection rates increased again; N = 505) and in the middle of March 2021, exactly one year after the first data collection (W5; N = 408). In W4 and W5, new respondents were added to the longitudinal sample to strengthen cross-sectional analyses. Additional information on informal care and physical activity was also collected. These data may be of interest to researchers who wish to explore dynamics of fear and attitudes towards public health measures during this particularly challenging time.

COVID-19 mortality, excess mortality, deaths per million and infection fatality ratio, Belgium, 9 March 2020 to 28 June 2020.

BackgroundCOVID-19 mortality, excess mortality, deaths per million population (DPM), infection fatality ratio (IFR) and case fatality ratio (CFR) are reported and compared for many countries globally. These measures may appear objective, however, they should be interpreted with caution.AimWe examined reported COVID-19-related mortality in Belgium from 9 March 2020 to 28 June 2020, placing it against the background of excess mortality and compared the DPM and IFR between countries and within subgroups.MethodsThe relation between COVID-19-related mortality and excess mortality was evaluated by comparing COVID-19 mortality and the difference between observed and weekly average predictions of all-cause mortality. DPM were evaluated using demographic data of the Belgian population. The number of infections was estimated by a stochastic compartmental model. The IFR was estimated using a delay distribution between infection and death.ResultsIn the study period, 9,621 COVID-19-related deaths were reported, which is close to the excess mortality estimated using weekly averages (8,985 deaths). This translates to 837 DPM and an IFR of 1.5% in the general population. Both DPM and IFR increase with age and are substantially larger in the nursing home population.DiscussionDuring the first pandemic wave, Belgium had no discrepancy between COVID-19-related mortality and excess mortality. In light of this close agreement, it is useful to consider the DPM and IFR, which are both age, sex, and nursing home population-dependent. Comparison of COVID-19 mortality between countries should rather be based on excess mortality than on COVID-19-related mortality.

The COVID-19 wave in Belgium during the Fall of 2020 and its association with higher education.

Soon after SARS-CoV-2 emerged in late 2019, Belgium was confronted with a first COVID-19 wave in March-April 2020. SARS-CoV-2 circulation declined in the summer months (late May to early July 2020). Following a successfully trumped late July-August peak, COVID-19 incidence fell slightly, to then enter two successive phases of rapid incline: in the first half of September, and then again in October 2020. The first of these coincided with the peak period of returning summer travelers; the second one coincided with the start of higher education's academic year. The largest observed COVID-19 incidence occurred in the period 16-31 October, particularly in the Walloon Region, the southern, French-speaking part of Belgium. We examine the potential association of the higher education population with spatio-temporal spread of COVID-19, using Bayesian spatial Poisson models for confirmed test cases, accounting for socio-demographic heterogeneity in the population. We find a significant association between the number of COVID-19 cases in the age groups 18-29 years and 30-39 years and the size of the higher education student population at the municipality level. These results can be useful towards COVID-19 mitigation strategies, particularly in areas where virus transmission from higher education students into the broader community could exacerbate morbidity and mortality of COVID-19 among populations with prevalent underlying conditions associated with more severe outcomes following infection.

Confirmed COVID-19 Cases per Economic Activity during Autumn Wave in Belgium.

Some occupational sectors, such as human health and care, food service, cultural and sport activities, have been associated with a higher risk of SARS-CoV-2 infection than other sectors. To curb the spread of SARS-CoV-2, it is preferable to apply targeted non-pharmaceutical interventions on selected economic sectors, rather than a full lockdown. However, the effect of these general and sector-specific interventions on the virus circulation has only been sparsely studied. We assess the COVID-19 incidence under different levels of non-pharmaceutical interventions per economic activity during the autumn 2020 wave in Belgium. The 14-day incidence of confirmed COVID-19 cases per the Statistical Classification of Economic Activities in the European Community (NACE-BEL) sector is modelled by a longitudinal Gaussian-Gaussian two-stage approach. This is based on exhaustive data on all employees in all sectors. In the presence of sanitary protocols and minimal non-pharmaceutical interventions, many sectors with close contact with others show considerably higher COVID-19 14-day incidences than other sectors. The effect of stricter non-pharmaceutical interventions in the general population and non-essential sectors is seen in the timing of the peak incidence and the width and height of the post-peak incidence. In most sectors incidences returned to higher levels after the peak than before and this decrease took longer for the health and care sector. Sanitary protocols for close proximity occupations may be sufficient during periods of low-level virus circulation, but progressively less with increasing circulation. Stricter general and sector-specific non-pharmaceutical interventions adequately decrease COVID-19 incidences, even in close proximity in essential sectors under solely sanitary protocols.