Vaccine effectiveness against influenza hospitalisation in adults during the 2022/2023 mixed season of influenza A(H1N1)pdm09, A(H3N2) and B circulation, Europe: VEBIS SARI VE hospital network.

We conducted a multicentre hospital-based test-negative case-control study to measure vaccine effectiveness (VE) against PCR-confirmed influenza in adult patients with severe acute respiratory infection (SARI) during the 2022/2023 influenza season in Europe. Among 5547 SARI patients ≥18 years, 2963 (53%) were vaccinated against influenza. Overall VE against influenza A(H1N1)pdm09 was 11% (95% CI: -23-36); 20% (95% CI: -4-39) against A(H3N2) and 56% (95% CI: 22-75) against B. During the 2022/2023 season, while VE against hospitalisation with influenza B was >55%, it was ≤20% for influenza A subtypes. While influenza vaccination should be a priority for future seasons, improved vaccines against influenza are needed.

Vaccine effectiveness against COVID-19 hospitalisation in adults (≥ 20 years) during Alpha- and Delta-dominant circulation: I-MOVE-COVID-19 and VEBIS SARI VE networks, Europe, 2021.

IntroductionTwo large multicentre European hospital networks have estimated vaccine effectiveness (VE) against COVID-19 since 2021.AimWe aimed to measure VE against PCR-confirmed SARS-CoV-2 in hospitalised severe acute respiratory illness (SARI) patients ≥ 20 years, combining data from these networks during Alpha (March-June)- and Delta (June-December)-dominant periods, 2021.MethodsForty-six participating hospitals across 14 countries follow a similar generic protocol using the test-negative case-control design. We defined complete primary series vaccination (PSV) as two doses of a two-dose or one of a single-dose vaccine ≥ 14 days before onset.ResultsWe included 1,087 cases (538 controls) and 1,669 cases (1,442 controls) in the Alpha- and Delta-dominant periods, respectively. During the Alpha period, VE against hospitalisation with SARS-CoV2 for complete Comirnaty PSV was 85% (95% CI: 69-92) overall and 75% (95% CI: 42-90) in those aged ≥ 80 years. During the Delta period, among SARI patients ≥ 20 years with symptom onset ≥ 150 days from last PSV dose, VE for complete Comirnaty PSV was 54% (95% CI: 18-74). Among those receiving Comirnaty PSV and mRNA booster (any product) ≥ 150 days after last PSV dose, VE was 91% (95% CI: 57-98). In time-since-vaccination analysis, complete all-product PSV VE was > 90% in those with their last dose < 90 days before onset; ≥ 70% in those 90-179 days before onset.ConclusionsOur results from this EU multi-country hospital setting showed that VE for complete PSV alone was higher in the Alpha- than the Delta-dominant period, and addition of a first booster dose during the latter period increased VE to over 90%.

Vaccine effectiveness against COVID-19 hospitalisation in adults (≥ 20 years) during Omicron-dominant circulation: I-MOVE-COVID-19 and VEBIS SARI VE networks, Europe, 2021 to 2022.

IntroductionThe I-MOVE-COVID-19 and VEBIS hospital networks have been measuring COVID-19 vaccine effectiveness (VE) in participating European countries since early 2021.AimWe aimed to measure VE against PCR-confirmed SARS-CoV-2 in patients ≥ 20 years hospitalised with severe acute respiratory infection (SARI) from December 2021 to July 2022 (Omicron-dominant period).MethodsIn both networks, 46 hospitals (13 countries) follow a similar test-negative case-control protocol. We defined complete primary series vaccination (PSV) and first booster dose vaccination as last dose of either vaccine received ≥ 14 days before symptom onset (stratifying first booster into received < 150 and ≥ 150 days after last PSV dose). We measured VE overall, by vaccine category/product, age group and time since first mRNA booster dose, adjusting by site as a fixed effect, and by swab date, age, sex, and presence/absence of at least one commonly collected chronic condition.ResultsWe included 2,779 cases and 2,362 controls. The VE of all vaccine products combined against hospitalisation for laboratory-confirmed SARS-CoV-2 was 43% (95% CI: 29-54) for complete PSV (with last dose received ≥ 150 days before onset), while it was 59% (95% CI: 51-66) after addition of one booster dose. The VE was 85% (95% CI: 78-89), 70% (95% CI: 61-77) and 36% (95% CI: 17-51) for those with onset 14-59 days, 60-119 days and 120-179 days after booster vaccination, respectively.ConclusionsOur results suggest that, during the Omicron period, observed VE against SARI hospitalisation improved with first mRNA booster dose, particularly for those having symptom onset < 120 days after first booster dose.

Genomic monitoring of SARS-CoV-2 variants using sentinel SARI hospital surveillance.

Background: To support the COVID-19 pandemic response, many countries, including Belgium, implemented baseline genomic surveillance (BGS) programs aiming to early detect and characterize new SARS-CoV-2 variants. In parallel, Belgium maintained a sentinel network of six hospitals that samples patients with severe acute respiratory infections (SARI) and integrated SARS-CoV-2 detection within a broader range of respiratory pathogens. We evaluate the ability of the SARI surveillance to monitor general trends and early signals of viral genetic evolution of SARS-CoV-2 and compare it with the BGS as a reference model. Methods: Nine-hundred twenty-five SARS-CoV-2 positive samples from patients fulfilling the Belgian SARI definition between January 2020 and December 2022 were sequenced using the ARTIC Network amplicon tiling approach on a MinION platform. Weekly variant of concern (VOC) proportions and types were compared to those that were circulating between 2021 and 2022, using 96,251 sequences of the BGS. Results: SARI surveillance allowed timely detection of the Omicron (BA.1, BA.2, BA.4, and BA.5) and Delta (B.1.617.2) VOCs, with no to 2 weeks delay according to the start of their epidemic growth in the Belgian population. First detection of VOCs B.1.351 and P.1 took longer, but these remained minor in Belgium. Omicron BA.3 was never detected in SARI surveillance. Timeliness could not be evaluated for B.1.1.7, being already major at the start of the study period. Conclusions: Genomic surveillance of SARS-CoV-2 using SARI sentinel surveillance has proven to accurately reflect VOCs detected in the population and provides a cost-effective solution for long-term genomic monitoring of circulating respiratory viruses.

Influenza versus other respiratory viruses - assessing severity among hospitalised children, Belgium, 2011 to 2020.

BackgroundKnowledge on the burden attributed to influenza viruses vs other respiratory viruses in children hospitalised with severe acute respiratory infections (SARI) in Belgium is limited.AimThis observational study aimed at describing the epidemiology and assessing risk factors for severe disease.MethodsWe retrospectively analysed data from routine national sentinel SARI surveillance in Belgium. Respiratory specimens collected during winter seasons 2011 to 2020 were tested by multiplex real-time quantitative PCR (RT-qPCR) for influenza and other respiratory viruses. Demographic data and risk factors were collected through questionnaires. Patients were followed-up for complications or death during hospital stay. Analysis focused on children younger than 15 years. Binomial logistic regression was used to identify risk factors for severe disease in relation to infection status.ResultsDuring the winter seasons 2011 to 2020, 2,944 specimens met the study case definition. Complications were more common in children with underlying risk factors, especially asthma (adjusted risk ratio (aRR): 1.87; 95% confidence interval (CI): 1.46-2.30) and chronic respiratory disease (aRR: 1.88; 95% CI: 1.44-2.32), regardless of infection status and age. Children infected with non-influenza respiratory viruses had a 32% higher risk of complications (aRR: 1.32; 95% CI: 1.06-1.66) compared with children with influenza only.ConclusionMulti-virus testing in children with SARI allows a more accurate assessment of the risk of complications and attribution of burden to respiratory viruses beyond influenza. Children with asthma and respiratory disease should be prioritised for clinical care, regardless of their virological test result and age, and targeted for prevention campaigns.

Effect of neuraminidase inhibitor (oseltamivir) treatment on outcome of hospitalised influenza patients, surveillance data from 11 EU countries, 2010 to 2020.

BackgroundTimely treatment with neuraminidase inhibitors (NAI) can reduce severe outcomes in influenza patients.AimWe assessed the impact of antiviral treatment on in-hospital deaths of laboratory-confirmed influenza patients in 11 European Union countries from 2010/11 to 2019/20.MethodsCase-based surveillance data from hospitalised patients with known age, sex, outcome, ward, vaccination status, timing of antiviral treatment, and hospitalisation were obtained. A mixed effect logistic regression model using country as random intercept was applied to estimate the adjusted odds ratio (aOR) for in-hospital death in patients treated with NAIs vs not treated.ResultsOf 19,937 patients, 31% received NAIs within 48 hours of hospital admission. Older age (60-79 years aOR 3.0, 95% CI: 2.4-3.8; 80 years 8.3 (6.6-10.5)) and intensive care unit admission (3.8, 95% CI: 3.4-4.2) increased risk of dying, while early hospital admission after symptom onset decreased risk (aOR 0.91, 95% CI: 0.90-0.93). NAI treatment initiation within 48 hours and up to 7 days reduced risk of dying (0-48 hours aOR 0.51, 95% CI: 0.45-0.59; 3-4 days 0.59 (0.51-0.67); 5-7 days 0.64 (0.56-0.74)), in particular in patients 40 years and older (e.g. treatment within 48 hours: 40-59 years aOR 0.43, 95% CI: 0.28-0.66; 60-79 years 0.50 (0.39-0.63); ≥80 years 0.51 (0.42-0.63)).ConclusionNAI treatment given within 48 hours and possibly up to 7 days after symptom onset reduced risk of in-hospital death. NAI treatment should be considered in older patients to prevent severe outcomes.

Mycoplasma pneumoniae detections before and during the COVID-19 pandemic: results of a global survey, 2017 to 2021.

BackgroundMycoplasma pneumoniae respiratory infections are transmitted by aerosol and droplets in close contact.AimWe investigated global M. pneumoniae incidence after implementation of non-pharmaceutical interventions (NPIs) against COVID-19 in March 2020.MethodsWe surveyed M. pneumoniae detections from laboratories and surveillance systems (national or regional) across the world from 1 April 2020 to 31 March 2021 and compared them with cases from corresponding months between 2017 and 2020. Macrolide-resistant M. pneumoniae (MRMp) data were collected from 1 April 2017 to 31 March 2021.ResultsThirty-seven sites from 21 countries in Europe, Asia, America and Oceania submitted valid datasets (631,104 tests). Among the 30,617 M. pneumoniae detections, 62.39% were based on direct test methods (predominantly PCR), 34.24% on a combination of PCR and serology (no distinction between methods) and 3.37% on serology alone (only IgM considered). In all countries, M. pneumoniae incidence by direct test methods declined significantly after implementation of NPIs with a mean of 1.69% (SD ± 3.30) compared with 8.61% (SD ± 10.62) in previous years (p < 0.01). Detection rates decreased with direct but not with indirect test methods (serology) (-93.51% vs + 18.08%; p < 0.01). Direct detections remained low worldwide throughout April 2020 to March 2021 despite widely differing lockdown or school closure periods. Seven sites (Europe, Asia and America) reported MRMp detections in one of 22 investigated cases in April 2020 to March 2021 and 176 of 762 (23.10%) in previous years (p = 0.04).ConclusionsThis comprehensive collection of M. pneumoniae detections worldwide shows correlation between COVID-19 NPIs and significantly reduced detection numbers.

A systematic review and meta-analysis of host genetic factors associated with influenza severity.

BACKGROUND: The severity of influenza disease can range from mild symptoms to severe respiratory failure and can partly be explained by host genetic factors that predisposes the host to severe influenza. Here, we aimed to summarize the current state of evidence that host genetic variants play a role in the susceptibility to severe influenza infection by conducting a systematic review and performing a meta-analysis for all markers with at least three or more data entries. RESULTS: A total of 34 primary human genetic association studies were identified that investigated a total of 20 different genes. The only significant pooled ORs were retrieved for the rs12252 polymorphism: an overall OR of 1.52 (95% CI [1.06-2.17]) for the rs12252-C allele compared to the rs12252-T allele. A stratified analysis by ethnicity revealed opposite effects in different populations. CONCLUSION: With exception for the rs12252 polymorphism, we could not identify specific genetic polymorphisms to be associated with severe influenza infection in a pooled meta-analysis. This advocates for the use of large, hypothesis-free, genome-wide association studies that account for the polygenic nature and the interactions with other host, pathogen and environmental factors.

Spotlight influenza: Extending influenza surveillance to detect non-influenza respiratory viruses of public health relevance: analysis of surveillance data, Belgium, 2015 to 2019.

BackgroundSeasonal influenza-like illness (ILI) affects millions of people yearly. Severe acute respiratory infections (SARI), mainly influenza, are a leading cause of hospitalisation and mortality. Increasing evidence indicates that non-influenza respiratory viruses (NIRV) also contribute to the burden of SARI. In Belgium, SARI surveillance by a network of sentinel hospitals has been ongoing since 2011.AimWe report the results of using in-house multiplex qPCR for the detection of a flexible panel of viruses in respiratory ILI and SARI samples and the estimated incidence rates of SARI associated with each virus.MethodsWe defined ILI as an illness with onset of fever and cough or dyspnoea. SARI was defined as an illness requiring hospitalisation with onset of fever and cough or dyspnoea within the previous 10 days. Samples were collected in four winter seasons and tested by multiplex qPCR for influenza virus and NIRV. Using catchment population estimates, we calculated incidence rates of SARI associated with each virus.ResultsOne third of the SARI cases were positive for NIRV, reaching 49.4% among children younger than 15 years. In children younger than 5 years, incidence rates of NIRV-associated SARI were twice that of influenza (103.5 vs 57.6/100,000 person-months); co-infections with several NIRV, respiratory syncytial viruses, human metapneumoviruses and picornaviruses contributed most (33.1, 13.6, 15.8 and 18.2/100,000 person-months, respectively).ConclusionEarly testing for NIRV could be beneficial to clinical management of SARI patients, especially in children younger than 5 years, for whom the burden of NIRV-associated disease exceeds that of influenza.

Evaluation of the added value of viral genomic information for predicting severity of influenza infection.

BACKGROUND: The severity of an influenza infection is influenced by both host and viral characteristics. This study aims to assess the relevance of viral genomic data for the prediction of severe influenza A(H3N2) infections among patients hospitalized for severe acute respiratory infection (SARI), in view of risk assessment and patient management. METHODS: 160 A(H3N2) influenza positive samples from the 2016-2017 season originating from the Belgian SARI surveillance were selected for whole genome sequencing. Predictor variables for severity were selected using a penalized elastic net logistic regression model from a combined host and genomic dataset, including patient information and nucleotide mutations identified in the viral genome. The goodness-of-fit of the model combining host and genomic data was compared using a likelihood-ratio test with the model including host data only. Internal validation of model discrimination was conducted by calculating the optimism-adjusted area under the Receiver Operating Characteristic curve (AUC) for both models. RESULTS: The model including viral mutations in addition to the host characteristics had an improved fit ([Formula: see text]=12.03, df = 3, p = 0.007). The optimism-adjusted AUC increased from 0.671 to 0.732. CONCLUSIONS: Adding genomic data (selected season-specific mutations in the viral genome) to the model containing host characteristics improved the prediction of severe influenza infection among hospitalized SARI patients, thereby offering the potential for translation into a prospective strategy to perform early season risk assessment or to guide individual patient management.

Monitoring of human coronaviruses in Belgian primary care and hospitals, 2015-20: a surveillance study.

BACKGROUND: Seasonal human coronaviruses (hCoVs) broadly circulate in humans. Their epidemiology and effect on the spread of emerging coronaviruses has been neglected thus far. We aimed to elucidate the epidemiology and burden of disease of seasonal hCoVs OC43, NL63, and 229E in patients in primary care and hospitals in Belgium between 2015 and 2020. METHODS: We retrospectively analysed data from the national influenza surveillance networks in Belgium during the winter seasons of 2015-20. Respiratory specimens were collected through the severe acute respiratory infection (SARI) and the influenza-like illness networks from patients with acute respiratory illness with onset within the previous 10 days, with measured or reported fever of 38°C or greater, cough, or dyspnoea; and for patients admitted to hospital for at least one night. Potential risk factors were recorded and patients who were admitted to hospital were followed up for the occurrence of complications or death for the length of their hospital stay. All samples were analysed by multiplex quantitative RT-PCRs for respiratory viruses, including seasonal hCoVs OC43, NL63, and 229E. We estimated the prevalence and incidence of seasonal hCoV infection, with or without co-infection with other respiratory viruses. We evaluated the association between co-infections and potential risk factors with complications or death in patients admitted to hospital with seasonal hCoV infections by age group. Samples received from week 8, 2020, were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). FINDINGS: 2573 primary care and 6494 hospital samples were included in the study. 161 (6·3%) of 2573 patients in primary care and 371 (5·7%) of 6494 patients admitted to hospital were infected with a seasonal hCoV. OC43 was the seasonal hCoV with the highest prevalence across age groups and highest incidence in children admitted to hospital who were younger than 5 years (incidence 9·0 [95% CI 7·2-11·2] per 100 000 person-months) and adults older than 65 years (2·6 [2·1-3·2] per 100 000 person-months). Among 262 patients admitted to hospital with seasonal hCoV infection and with complete information on potential risk factors, 66 (73·3%) of 90 patients who had complications or died also had at least one potential risk factor (p=0·0064). Complications in children younger than 5 years were associated with co-infection (24 [36·4%] of 66; p=0·017), and in teenagers and adults (≥15 years), more complications arose in patients with a single hCoV infection (49 [45·0%] of 109; p=0·0097). In early 2020, the Belgian SARI surveillance detected the first SARS-CoV-2-positive sample concomitantly with the first confirmed COVID-19 case with no travel history to China. INTERPRETATION: The main burden of severe seasonal hCoV infection lies with children younger than 5 years with co-infections and adults aged 65 years and older with pre-existing comorbidities. These age and patient groups should be targeted for enhanced observation when in medical care and in possible future vaccination strategies, and co-infections in children younger than 5 years should be considered during diagnosis and treatment. Our findings support the use of national influenza surveillance systems for seasonal hCoV monitoring and early detection, and monitoring of emerging coronaviruses such as SARS-CoV-2. FUNDING: Belgian Federal Public Service Health, Food Chain Safety, and Environment; Belgian National Insurance Health Care (Institut national d'assurance maladie-invalidité/Rijksinstituut voor ziekte-en invaliditeitsverzekering); and Regional Health Authorities (Flanders Agentschap zorg en gezondheid, Brussels Commission communautaire commune, Wallonia Agence pour une vie de qualité).

Bronchiolitis in COVID-19 times: a nearly absent disease?

Stay-at-home orders, physical distancing, face masks and other non-pharmaceutical interventions (NPIs) do not only impact COVID-19, but also the dynamics of various other infectious diseases. Bronchiolitis is a clinically diagnosed viral infection of the lower respiratory tract, and causes a yearly seasonal wave of admissions in paediatric wards worldwide. We counted 92,5% less bronchiolitis hospitalisations in Antwerp before the expected end of the peak this year (of which only 1 RSV positive), as compared to the last 3 years. Furthermore, there was a >99% reduction in the number of registered RSV cases in Belgium.Conslusion: The 2020 winter bronchiolitis peak is hitherto nonexistent, but we fear a 'delayed' spring/summer bronchiolitis peak when most NPIs will be relaxed and pre-pandemic life restarts. What is known? • Bronchiolitis causes a yearly seasonal wave of admissions in paediatric departments worldwide. • Non-pharmaceutical interventions (NPIs) do not only impact COVID-19, but also the dynamics of various other infectious diseases. What is new? • The 2020 winter bronchiolitis peak is hitherto nonexistent. • A 'delayed' spring or summer bronchiolitis peak could happen when most NPIs will be relaxed and pre-pandemic life restarts.

All-cause mortality supports the COVID-19 mortality in Belgium and comparison with major fatal events of the last century.

BACKGROUND: The COVID-19 mortality rate in Belgium has been ranked among the highest in the world. To assess the appropriateness of the country's COVID-19 mortality surveillance, that includes long-term care facilities deaths and deaths in possible cases, the number of COVID-19 deaths was compared with the number of deaths from all-cause mortality. Mortality during the COVID-19 pandemic was also compared with historical mortality rates from the last century including those of the Spanish influenza pandemic. METHODS: Excess mortality predictions and COVID-19 mortality data were analysed for the period March 10th to June 21st 2020. The number of COVID-19 deaths and the COVID-19 mortality rate per million were calculated for hospitals, nursing homes and other places of death, according to diagnostic status (confirmed/possible infection). To evaluate historical mortality, monthly mortality rates were calculated from January 1900 to June 2020. RESULTS: Nine thousand five hundred ninety-one COVID-19 deaths and 39,076 deaths from all-causes were recorded, with a correlation of 94% (Spearman's rho, p < 0,01). During the period with statistically significant excess mortality (March 20th to April 28th; total excess mortality 64.7%), 7917 excess deaths were observed among the 20,159 deaths from all-causes. In the same period, 7576 COVID-19 deaths were notified, indicating that 96% of the excess mortality were likely attributable to COVID-19. The inclusion of deaths in nursing homes doubled the COVID-19 mortality rate, while adding deaths in possible cases increased it by 27%. Deaths in laboratory-confirmed cases accounted for 69% of total COVID-19-related deaths and 43% of in-hospital deaths. Although the number of deaths was historically high, the monthly mortality rate was lower in April 2020 compared to the major fatal events of the last century. CONCLUSIONS: Trends in all-cause mortality during the first wave of the epidemic was a key indicator to validate the Belgium's high COVID-19 mortality figures. A COVID-19 mortality surveillance limited to deaths from hospitalised and selected laboratory-confirmed cases would have underestimated the magnitude of the epidemic. Excess mortality, daily and monthly number of deaths in Belgium were historically high classifying undeniably the first wave of the COVID-19 epidemic as a fatal event.

Rapid establishment of a national surveillance of COVID-19 hospitalizations in Belgium.

BACKGROUND: In response to the COVID-19 epidemic, caused by a novel coronavirus, it was of great importance to rapidly collect as much accurate information as possible in order to characterize the public health threat and support the health authorities in its management. Hospital-based surveillance is paramount to monitor the severity of a disease in the population. METHODS: Two separate surveillance systems, a Surge Capacity survey and a Clinical survey, were set up to collect complementary data on COVID-19 from Belgium's hospitals. The Surge Capacity survey collects aggregated data to monitor the hospital capacity through occupancy rates of beds and medical devices, and to follow a set of key epidemiological indicators over time. Participation is mandatory and the daily data collection includes prevalence and incidence figures on the number of COVID-19 patients in the hospital. The Clinical survey is strongly recommended by health authorities, focusses on specific patient characteristics and relies on individual patient data provided by the hospitals at admission and discharge. CONCLUSIONS: This national double-level hospital surveillance was implemented very rapidly after the first COVID-19 patients were hospitalized and revealed to be crucial to monitor hospital capacity over time and to better understand the disease in terms of risk groups and outcomes. The two approaches are complementary and serve different needs.

Capturing respiratory syncytial virus season in Belgium using the influenza severe acute respiratory infection surveillance network, season 2018/19.

BackgroundRespiratory syncytial virus (RSV) is a common cause of severe respiratory illness in young children (< 5 years old) and older adults (≥ 65 years old) leading the World Health Organization (WHO) to recommend the implementation of a dedicated surveillance in countries.AimWe tested the capacity of the severe acute respiratory infection (SARI) hospital network to contribute to RSV surveillance in Belgium.MethodsDuring the 2018/19 influenza season, we started the SARI surveillance for influenza in Belgium in week 40, earlier than in the past, to follow RSV activity, which usually precedes influenza virus circulation. While the WHO SARI case definition for influenza normally used by the SARI hospital network was employed, flexibility over the fever criterion was allowed, so patients without fever but meeting the other case definition criteria could be included in the surveillance.ResultsBetween weeks 40 2018 and 2 2019, we received 508 samples from SARI patients. We found an overall RSV detection rate of 62.4% (317/508), with rates varying depending on the age group: 77.6% in children aged < 5 years (253/326) and 34.4% in adults aged ≥ 65 years (44/128). Over 90% of the RSV-positive samples also positive for another tested respiratory virus (80/85) were from children aged < 5 years. Differences were also noted between age groups for symptoms, comorbidities and complications.ConclusionWith only marginal modifications in the case definition and the period of surveillance, the Belgian SARI network would be able to substantially contribute to RSV surveillance and burden evaluation in children and older adults, the two groups of particular interest for WHO.

Impact of solid cancer on in-hospital mortality overall and among different subgroups of patients with COVID-19: a nationwide, population-based analysis.

BACKGROUND: Cancer seems to have an independent adverse prognostic effect on COVID-19-related mortality, but uncertainty exists regarding its effect across different patient subgroups. We report a population-based analysis of patients hospitalised with COVID-19 with prior or current solid cancer versus those without cancer. METHODS: We analysed data of adult patients registered until 24 May 2020 in the Belgian nationwide database of Sciensano. The primary objective was in-hospital mortality within 30 days of COVID-19 diagnosis among patients with solid cancer versus patients without cancer. Severe event occurrence, a composite of intensive care unit admission, invasive ventilation and/or death, was a secondary objective. These endpoints were analysed across different patient subgroups. Multivariable logistic regression models were used to analyse the association between cancer and clinical characteristics (baseline analysis) and the effect of cancer on in-hospital mortality and on severe event occurrence, adjusting for clinical characteristics (in-hospital analysis). RESULTS: A total of 13 594 patients (of whom 1187 with solid cancer (8.7%)) were evaluable for the baseline analysis and 10 486 (892 with solid cancer (8.5%)) for the in-hospital analysis. Patients with cancer were older and presented with less symptoms/signs and lung imaging alterations. The 30-day in-hospital mortality was higher in patients with solid cancer compared with patients without cancer (31.7% vs 20.0%, respectively; adjusted OR (aOR) 1.34; 95% CI 1.13 to 1.58). The aOR was 3.84 (95% CI 1.94 to 7.59) among younger patients (<60 years) and 2.27 (95% CI 1.41 to 3.64) among patients without other comorbidities. Severe event occurrence was similar in both groups (36.7% vs 28.8%; aOR 1.10; 95% CI 0.95 to 1.29). CONCLUSIONS: This population-based analysis demonstrates that solid cancer is an independent adverse prognostic factor for in-hospital mortality among patients with COVID-19. This adverse effect was more pronounced among younger patients and those without other comorbidities. Patients with solid cancer should be prioritised in vaccination campaigns and in tailored containment measurements.

First cases of coronavirus disease 2019 (COVID-19) in the WHO European Region, 24 January to 21 February 2020.

In the WHO European Region, COVID-19 surveillance was implemented 27 January 2020. We detail the first European cases. As at 21 February, nine European countries reported 47 cases. Among 38 cases studied, 21 were linked to two clusters in Germany and France, 14 were infected in China. Median case age was 42 years; 25 were male. Late detection of the clusters' index cases delayed isolation of further local cases. As at 5 March, there were 4,250 cases.

Bigger and Better? Representativeness of the Influenza A Surveillance Using One Consolidated Clinical Microbiology Laboratory Data Set as Compared to the Belgian Sentinel Network of Laboratories.

Infectious diseases remain a serious public health concern globally, while the need for reliable and representative surveillance systems remains as acute as ever. The public health surveillance of infectious diseases uses reported positive results from sentinel clinical laboratories or laboratory networks, to survey the presence of specific microbial agents known to constitute a threat to public health in a given population. This monitoring activity is commonly based on a representative fraction of the microbiology laboratories nationally reporting to a single central reference point. However, in recent years a number of clinical microbiology laboratories (CML) have undergone a process of consolidation involving a shift toward laboratory amalgamation and closer real-time informational linkage. This report aims to investigate whether such merging activities might have a potential impact on infectious diseases surveillance. Influenza data was used from Belgian public health surveillance 2014-2017, to evaluate whether national infection trends could be estimated equally as effectively from only just one centralized CML serving the wider Brussels area (LHUB-ULB). The overall comparison reveals that there is a close correlation and representativeness of the LHUB-ULB data to the national and international data for the same time periods, both on epidemiological and molecular grounds. Notably, the effectiveness of the LHUB-ULB surveillance remains partially subject to local regional variations. A subset of the Influenza samples had their whole genome sequenced so that the observed epidemiological trends could be correlated to molecular observations from the same period, as an added-value proposition. These results illustrate that the real-time integration of high-throughput whole genome sequencing platforms available in consolidated CMLs into the public health surveillance system is not only credible but also advantageous to use for future surveillance and prediction purposes. This can be most effective when implemented for automatic detection systems that might include multiple layers of information and timely implementation of control strategies.

Real-time prediction of influenza outbreaks in Belgium.

Seasonal influenza is a worldwide public health concern. Forecasting its dynamics can improve the management of public health regulations, resources and infrastructure, and eventually reduce mortality and the costs induced by influenza-related absenteism. In Belgium, a network of Sentinel General Practitioners (SGPs) is in place for the early detection of the seasonal influenza epidemic. This surveillance network reports the weekly incidence of influenza-like illness (ILI) cases, which makes it possible to detect the epidemic onset, as well as other characteristics of the epidemic season. In this paper, we present an approach for predicting the weekly ILI incidence in real-time by resorting to a dynamically calibrated compartmental model, which furthermore takes into account the dynamics of other influenza seasons. In order to validate the proposed approach, we used data collected by the Belgian SGPs for the influenza seasons 2010-2016. In spite of the great variability among different epidemic seasons, providing weekly predictions makes it possible to capture variations in the ILI incidence. The confidence region becomes more representative of the epidemic behavior as ILI data from more seasons become available. Since the SIR model is then calibrated dynamically every week, the predicted ILI curve gets rapidly tuned to the dynamics of the ongoing season. The results show that the proposed method can be used to characterize the overall behavior of an epidemic.