11 research outputs found
Risk factors for SARS-CoV-2 transmission in student residences: a case-ascertained study
No full textBACKGROUND: 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.sponsorship: This work was supported by the National Institute for Health and Disability Insurance (RIZIV/INAMI); the regional Flemish government's Agentschap voor Zorg & Gezondheid; the KU Leuven and Research Foundation Flanders (grant number 1S88721N to JR). (National Institute for Health and Disability Insurance (RIZIV/INAMI), regional Flemish government's Agentschap voor Zorg Gezondheid, KU Leuven and Research Foundation Flanders|1S88721N)status: Publishe
Fecal virome analysis of three carnivores reveals a novel nodavirus and multiple gemycircularviruses
No full textMore knowledge about viral populations in wild animals is needed in order to better understand and assess the risk of zoonotic diseases. In this study we performed viral metagenomic analysis of fecal samples from three healthy carnivores: a badger (Meles meles), a mongoose (Herpestes ichneumon) and an otter (Lutra lutra) from Portugal.sponsorship: NCN and MZ were supported by the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT Vlaanderen). We would like to thank Prof. Maria Sao Jose Nascimento and Joren Raymenants who assisted in the proof-reading of the manuscript. (Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT Vlaanderen))status: Publishe
COVID-19 contact tracing at work in Belgium - how tracers tweak guidelines for the better
No full textBackground: When conducting COVID-19 contact tracing, pre-defined criteria allow differentiating high-risk contacts (HRC) from low-risk contacts (LRC). Our study aimed to evaluate whether contact tracers in Belgium followed these criteria in practice and whether their deviations improved the infection risk assessment. Method: We conducted a retrospective cohort study in Belgium, through an anonymous online survey, sent to 111,763 workers by e-mail. First, we evaluated the concordance between the guideline-based classification of HRC or LRC and the tracer’s classification. We computed positive and negative agreements between both. Second, we used a multivariate Poisson regression to calculate the risk ratio (RR) of testing positive depending on the risk classification by the contact tracer and by the guideline-based risk classification. Results: For our first research question, we included 1105 participants. The positive agreement between the guideline-based classification in HRC or LRC and the tracer’s classification was 0.53 (95% CI 0.49–0.57) and the negative agreement 0.70 (95% CI: 0.67–0.72). The type of contact tracer (occupational doctors, internal tracer, general practitioner, other) did not significantly influence the results. For the second research question, we included 589 participants. The RR of testing positive after an HRC compared to an LRC was 3.10 (95% CI: 2.71–3.56) when classified by the contact tracer and 2.24 (95% CI: 1.94–2.60) when classified by the guideline-based criteria. Conclusion: Our study indicates that contact tracers did not apply pre-defined criteria for classifying high and low risk contacts. Risk stratification by contact tracers predicts who is at risk of infection better than guidelines only. This result indicates that a knowledgeable tracer can target testing better than a general guideline, asking for a debate on how to adapt the guidelines
Unravelling the effect of New Year’s Eve celebrations on SARS-CoV-2 transmission
No full textPublic holidays have been associated with SARS-CoV-2 incidence surges, although a firm link remains to be established. This association is sometimes attributed to events where transmissions occur at a disproportionately high rate, known as superspreading events. Here, we describe a sudden surge in new cases with the Omicron BA.1 strain amongst higher education students in Belgium. Contact tracers classed most of these cases as likely or possibly infected on New Year's Eve, indicating a direct trigger by New Year celebrations. Using a combination of contact tracing and phylogenetic data, we show the limited role of superspreading events in this surge. Finally, the numerous simultaneous transmissions allowed a unique opportunity to determine the distribution of incubation periods of the Omicron strain. Overall, our results indicate that, even under social restrictions, a surge in transmissibility of SARS-CoV-2 can occur when holiday celebrations result in small social gatherings attended simultaneously and communitywide.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Exhaled breath SARS-CoV-2 shedding patterns across variants of concern
No full textOBJECTIVES: We performed exhaled breath (EB) and nasopharyngeal (NP) quantitative polymerase chain reaction (qPCR) and NP rapid antigen testing (NP RAT) of SARS-CoV-2 infections with different variants. METHODS: We included immuno-naïve alpha-infected (n = 11) and partly boosted omicron-infected patients (n = 8) as high-risk contacts. We compared peak NP and EB qPCR cycle time (ct) values between cohorts (Wilcoxon-Mann-Whitney test). Test positivity was compared for three infection phases using Cochran Q test. RESULTS: Peak median NP ct was 11.5 (interquartile range [IQR] 10.1-12.1) for alpha and 12.2 (IQR 11.1-15.3) for omicron infections. Peak median EB ct was 25.2 (IQR 24.5-26.9) and 28.3 (IQR 26.4-30.8) for alpha and omicron infections, respectively. Distributions did not differ between cohorts for NP (P = 0.19) or EB (P = 0.09). SARS-CoV-2 shedding peaked on day 1 in EB (confidence interval [CI] 0.0 - 4.5) and day 3 in NP (CI 1.5 - 6.0). EB qPCR positivity equaled NP qPCR positivity on D0-D1 (P = 0.44) and D2-D6 (P = 1.0). It superseded NP RAT positivity on D0-D1 (P = 0.003) and D2-D6 (P = 0.008). It was inferior to both on D7-D10 (P < 0.001). CONCLUSION: Peak EB and nasopharynx shedding were comparable across variants. EB qPCR positivity matched NP qPCR and superseded NP RAT in the first week of infection
Additional file 1 of Risk factors for SARS-CoV-2 transmission in student residences: a case-ascertained study
No full textAdditional file 1: S1. Statistical analysis – secondary attack rate. Supplementary 2. Sensitivity analysis. Supplementary Figure 1. Timing of outbreaks in student residences. Supplementary Figure 2. Google mobility data between October 30th 2020 and May 25st 2021 for the region of Flemish Brabant, Belgium
Interpretation of indoor air surveillance for respiratory infections: a prospective longitudinal observational study in a childcare settingResearch in context
No full textSummary: Background: Sampling the air in indoor congregate settings, where respiratory pathogens are ubiquitous, may constitute a valuable yet underutilised data source for community-wide surveillance of respiratory infections. However, there is a lack of research comparing air sampling and individual sampling of attendees. Therefore, it remains unclear how air sampling results should be interpreted for the purpose of surveillance. Methods: In this prospective observational study, we compared the presence and concentration of several respiratory pathogens in the air with the number of attendees with infections and the pathogen load in their nasal mucus. Weekly for 22 consecutive weeks, we sampled the air in a single childcare setting in Belgium. Concurrently, we collected the paper tissues used to wipe the noses of 23 regular attendees: children aged zero to three and childcare workers. All samples were tested for 29 respiratory pathogens using PCR. Findings: Air sampling sensitively detected most respiratory pathogens found in nasal mucus. Some pathogens (SARS-CoV-2, Pneumocystis jirovecii) were found repeatedly in the air, but rarely in nasal mucus, whilst the opposite was true for others (Human coronavirus NL63). All three pathogens with a clear outbreak pattern (Human coronavirus HKU-1, human parainfluenza virus 3 and 4) were found in the air one week before or concurrent with the first detection in paper tissue samples. The presence and concentration of pathogens in the air was best predicted by the pathogen load of the most infectious case. However, air pathogen concentrations also correlated with the number of attendees with infections. Detection and concentration in the air were associated with CO2 concentration, a marker of ventilation and occupancy. Interpretation: Our results suggest that air sampling could provide sensitive, responsive epidemiological indicators for the surveillance of respiratory pathogens. Using air CO2 concentrations to normalise such signals emerges as a promising approach. Funding: KU Leuven; DURABLE project, under the EU4Health Programme of the European Commission; Thermo Fisher Scientific
Additional file 2 of Risk factors for SARS-CoV-2 transmission in student residences: a case-ascertained study
No full textAdditional file 2
Development of an integrated breath analysis technology for on-chip aerosol capture and molecular analysis
No full textAs proven early on in the pandemic, SARS-CoV-2 is mainly transmitted by aerosols. This urged us to develop a silicon impactor that collects the virus particles directly from breath. Performing PCR on these breath samples proved equally sensitive as nasopharyngeal swabs during the first week of an infection [Stakenborg et al., 2022], yet it remained a mostly manual process and PCR turn-around-time was still long. To overcome these drawbacks, we developed a fast and sensitive, fully integrated point-of-need breath test, comprising a novel breath sampler device and PCR instrument. The breath sampler combines virus collection and in-situ RNA amplification. The PCR instrument performs very fast amplification of the released viral RNA. Sample-to-result time was reduced to <20 min with an equal performance as the original manual procedure
Considerable escape of SARS-CoV-2 Omicron to antibody neutralization
No full textThe SARS-CoV-2 Omicron variant was first identified in November 2021 in Botswana and South Africa1-3. It has since spread to many countries and is expected to rapidly become dominant worldwide. The lineage is characterized by the presence of around 32 mutations in spike-located mostly in the N-terminal domain and the receptor-binding domain-that may enhance viral fitness and enable antibody evasion. Here we isolated an infectious Omicron virus in Belgium from a traveller returning from Egypt. We examined its sensitivity to nine monoclonal antibodies that have been clinically approved or are in development4, and to antibodies present in 115 serum samples from COVID-19 vaccine recipients or individuals who have recovered from COVID-19. Omicron was completely or partially resistant to neutralization by all monoclonal antibodies tested. Sera from recipients of the Pfizer or AstraZeneca vaccine, sampled five months after complete vaccination, barely inhibited Omicron. Sera from COVID-19-convalescent patients collected 6 or 12 months after symptoms displayed low or no neutralizing activity against Omicron. Administration of a booster Pfizer dose as well as vaccination of previously infected individuals generated an anti-Omicron neutralizing response, with titres 6-fold to 23-fold lower against Omicron compared with those against Delta. Thus, Omicron escapes most therapeutic monoclonal antibodies and, to a large extent, vaccine-elicited antibodies. However, Omicron is neutralized by antibodies generated by a booster vaccine dose.sponsorship: We thank the staff at the European Health Emergency Preparedness and Response Authority (HERA) for their support; S. Cole for his help in initiating the collaboration between Institut Pasteur and KU Leuven through the HERA network; A. Baidaliuk and F. Gambaro for their help with the sequencing data analysis; the patients who participated to this study; the members of the Virus and Immunity Unit and other teams for discussions and help; N. Aulner and the staff at the UtechS Photonic BioImaging (UPBI) core facility (Institut Pasteur)-a member of the France BioImaging network-for image acquisition and analysis; the members of the KU Leuven University authorities, and J. Arnout, B. Lambrecht, C. Van Geet and L. Sels for their support; L. Belec, N. Robillard and M. Saliba for their help with sequencing; and F. Peira, V. Legros and L. Courtellemont for their help with the cohorts. The Opera system was co-funded by Institut Pasteur and the Region ile de France (DIM1Health). Work in the O.S. laboratory is funded by Institut Pasteur, Urgence COVID-19 Fundraising Campaign of Institut Pasteur, Fondation pour la Recherche Medicale (FRM), ANRS, the Vaccine Research Institute (ANR-10-LABX-77), Labex IBEID (ANR-10-LABX-62-IBEID), ANR/FRM Flash Covid PROTEO-SARS-CoV-2 and IDISCOVR. Work in the UPBI is funded by grant ANR-10-INSB-04-01 and Region Ile-de-France program DIM1-Health. D.P. is supported by the Vaccine Research Institute. The H.M. laboratory is funded by the Institut Pasteur, the Milieu Interieur Program (ANR-10-LABX-69-01), the INSERM, REACTing, EU (RECOVER) and Fondation de France (00106077) grants. The E.S.-L. laboratory is funded by Institut Pasteur, the INCEPTION program (Investissements d'Avenir grant ANR-16-CONV-0005) and the French Government's Investissement d'Avenir programme, Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases' (grant no. ANR-10-LABX-62-IBEID). G.B. acknowledges support from the Internal Funds KU Leuven under grant agreement C14/18/094, and the Research Foundation-Flanders (Fonds voor Wetenschappelijk Onderzoek-Vlaanderen, G0E1420N, G098321N). P.M. acknowledges support from a COVID-19 research grant of 'Fonds Wetenschappelijk Onderzoek'/Research Foundation-Flanders (grant no. G0H4420N). S.D. is supported by the Fonds National de la Recherche Scientifique (FNRS, Belgium) and also acknowledges support from the Research Foundation-Flanders (Fonds voor Wetenschappelijk Onderzoek-Vlaanderen, G098321N) and from the European Union Horizon 2020 project MOOD (grant no. 874850). The funders of this study had no role in study design, data collection, analysis and interpretation, or writing of the article. (Institut Pasteur, Region ile de France (DIM1Health), Urgence COVID-19 Fundraising Campaign of Institut Pasteur, Fondation pour la Recherche Medicale (FRM), ANRS, Vaccine Research Institute|ANR-10-LABX-77, Labex IBEID|ANR-10-LABX-62-IBEID, ANR/FRM Flash Covid PROTEO-SARS-CoV-2, IDISCOVR, Region Ile-de-France program DIM1-Health, Vaccine Research Institute, Milieu Interieur Program|ANR-10-LABX-69-01, INSERM, REACTing, EU (RECOVER), Fondation de France|00106077, INCEPTION program (Investissements d'Avenir grant)|ANR-16-CONV-0005, French Government's Investissement d'Avenir programme, Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'|ANR-10-LABX-62-IBEID, Internal Funds KU Leuven|C14/18/094, Research Foundation-Flanders (Fonds voor Wetenschappelijk Onderzoek-Vlaanderen)|G0E1420N, Research Foundation-Flanders (Fonds voor Wetenschappelijk Onderzoek-Vlaanderen)|G098321N, COVID-19 research grant of 'Fonds Wetenschappelijk Onderzoek'/Research Foundation-Flanders|G0H4420N, Fonds National de la Recherche Scientifique (FNRS, Belgium), European Union|874850, ANR-10-INSB-04-01)status: Publishe
