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Evaluation of lockdown effect on SARS-CoV-2 dynamics through viral genome quantification in waste water, Greater Paris, France, 5 March to 23 April 2020
S Wurtzer , V Marechal , JM Mouchel , Y Maday , R Teyssou , E Richard , JL Almayrac and L MoulinIntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent of coronavirus disease (COVID-19). People infected with SARS-CoV-2 may exhibit no or mild non-specific symptoms; thus, they may contribute to silent circulation of the virus among humans. Since SARS-CoV-2 RNA can be detected in stool samples, monitoring SARS-CoV-2 RNA in waste water (WW) has been proposed as a complementary tool to investigate virus circulation in human populations.
AimTo test if the quantification of SARS-CoV-2 genomes in WW correlates with the number of symptomatic or non-symptomatic carriers.
MethodWe performed a time-course quantitative analysis of SARS-CoV-2 by RT-qPCR in raw WW samples collected from several major WW treatment plants in Greater Paris. The study period was 5 March to 23 April 2020, including the lockdown period in France (from 17 March).
ResultsWe showed that the increase of genome units in raw WW accurately followed the increase of human COVID-19 cases observed at the regional level. Of note, the viral genome could be detected before the epidemic grew massively (around 8 March). Equally importantly, a marked decrease in the quantities of genome units was observed concomitantly with the reduction in the number of new COVID-19 cases, 29 days following the lockdown.
ConclusionThis work suggests that a quantitative monitoring of SARS-CoV-2 genomes in WW could generate important additional information for improved monitoring of SARS-CoV-2 circulation at local or regional levels and emphasises the role of WW-based epidemiology.
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Occupation- and age-associated risk of SARS-CoV-2 test positivity, the Netherlands, June to October 2020
High coronavirus incidence has prompted the Netherlands to implement a second lockdown. To elucidate the epidemic’s development preceding this second wave, we analysed weekly test positivity in public test locations by population subgroup between 1 June and 17 October 2020. Hospitality and public transport workers, driving instructors, hairdressers and aestheticians had higher test positivity compared with a reference group of individuals without a close-contact occupation. Workers in childcare, education and healthcare showed lower test positivity.
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The first wave of the COVID-19 pandemic in Spain: characterisation of cases and risk factors for severe outcomes, as at 27 April 2020
More LessBackgroundThe first wave of the coronavirus disease (COVID-19) pandemic spread rapidly in Spain, one of Europe’s most affected countries. A national lockdown was implemented on 15 March 2020.
AimTo describe reported cases and the impact of national lockdown, and to identify disease severity risk factors.
MethodsNational surveillance data were used to describe PCR-confirmed cases as at 27 April 2020. We compared case characteristics by severity categories (hospitalisation, admission to intensive care unit (ICU), death) and identified severity risk factors using multivariable regression.
ResultsThe epidemic peaked on 20 March. Of 218,652 COVID-19 cases, 45.4% were hospitalised, 4.6% were admitted to ICU and 11.9% died. Among those who died, 94.8% had at least one underlying disease. Healthcare workers (HCWs) represented 22.9% of cases. Males were more likely to have severe outcomes than females. Cardiovascular disease was a consistent risk factor. Patients with pneumonia had higher odds of hospitalisation (odds ratio (OR): 26.63; 95% confidence interval (CI): 25.03–28.33). The strongest predictor of death was age ≥ 80 years (OR: 28.4; 95% CI: 19.85–40.78). Among underlying diseases, chronic renal disease had highest odds of death (OR: 1.47; 95% CI: 1.29–1.68).
ConclusionsCOVID-19 case numbers began declining 6 days after the national lockdown. The first wave of the COVID-19 pandemic in Spain had a severe impact on elderly people. Patients with cardiovascular or renal conditions were at higher risk for severe outcomes. A high proportion of cases were HCWs. Enhanced surveillance and control measures in these subgroups are crucial during future COVID-19 waves.
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Community use of face masks and similar barriers to prevent respiratory illness such as COVID-19: a rapid scoping review
More LessBackgroundEvidence for face-mask wearing in the community to protect against respiratory disease is unclear.
AimTo assess effectiveness of wearing face masks in the community to prevent respiratory disease, and recommend improvements to this evidence base.
MethodsWe systematically searched Scopus, Embase and MEDLINE for studies evaluating respiratory disease incidence after face-mask wearing (or not). Narrative synthesis and random-effects meta-analysis of attack rates for primary and secondary prevention were performed, subgrouped by design, setting, face barrier type, and who wore the mask. Preferred outcome was influenza-like illness. Grading of Recommendations, Assessment, Development and Evaluations (GRADE) quality assessment was undertaken and evidence base deficits described.
Results33 studies (12 randomised control trials (RCTs)) were included. Mask wearing reduced primary infection by 6% (odds ratio (OR): 0.94; 95% CI: 0.75–1.19 for RCTs) to 61% (OR: 0.85; 95% CI: 0.32–2.27; OR: 0.39; 95% CI: 0.18–0.84 and OR: 0.61; 95% CI: 0.45–0.85 for cohort, case–control and cross-sectional studies respectively). RCTs suggested lowest secondary attack rates when both well and ill household members wore masks (OR: 0.81; 95% CI: 0.48–1.37). While RCTs might underestimate effects due to poor compliance and controls wearing masks, observational studies likely overestimate effects, as mask wearing might be associated with other risk-averse behaviours. GRADE was low or very low quality.
ConclusionWearing face masks may reduce primary respiratory infection risk, probably by 6–15%. It is important to balance evidence from RCTs and observational studies when their conclusions widely differ and both are at risk of significant bias. COVID-19-specific studies are required.
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Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020
Flavia Riccardo , Marco Ajelli , Xanthi D Andrianou , Antonino Bella , Martina Del Manso , Massimo Fabiani , Stefania Bellino , Stefano Boros , Alberto Mateo Urdiales , Valentina Marziano , Maria Cristina Rota , Antonietta Filia , Fortunato D'Ancona , Andrea Siddu , Ornella Punzo , Filippo Trentini , Giorgio Guzzetta , Piero Poletti , Paola Stefanelli , Maria Rita Castrucci , Alessandra Ciervo , Corrado Di Benedetto , Marco Tallon , Andrea Piccioli , Silvio Brusaferro , Giovanni Rezza , Stefano Merler , Patrizio Pezzotti and the COVID-19 working groupBackgroundOn 20 February 2020, a locally acquired coronavirus disease (COVID-19) case was detected in Lombardy, Italy. This was the first signal of ongoing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the country. The number of cases in Italy increased rapidly and the country became the first in Europe to experience a SARS-CoV-2 outbreak.
AimOur aim was to describe the epidemiology and transmission dynamics of the first COVID-19 cases in Italy amid ongoing control measures.
MethodsWe analysed all RT-PCR-confirmed COVID-19 cases reported to the national integrated surveillance system until 31 March 2020. We provide a descriptive epidemiological summary and estimate the basic and net reproductive numbers by region.
ResultsOf the 98,716 cases of COVID-19 analysed, 9,512 were healthcare workers. Of the 10,943 reported COVID-19-associated deaths (crude case fatality ratio: 11.1%) 49.5% occurred in cases older than 80 years. Male sex and age were independent risk factors for COVID-19 death. Estimates of R0 varied between 2.50 (95% confidence interval (CI): 2.18–2.83) in Tuscany and 3.00 (95% CI: 2.68–3.33) in Lazio. The net reproduction number Rt in northern regions started decreasing immediately after the first detection.
ConclusionThe COVID-19 outbreak in Italy showed a clustering onset similar to the one in Wuhan, China. R0 at 2.96 in Lombardy combined with delayed detection explains the high case load and rapid geographical spread. Overall, Rt in Italian regions showed early signs of decrease, with large diversity in incidence, supporting the importance of combined non-pharmacological control measures.
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Clinical characteristics and risk factors associated with severe COVID-19: prospective analysis of 1,045 hospitalised cases in North-Eastern France, March 2020
Charlotte Kaeuffer , Coralie Le Hyaric , Thibaut Fabacher , Joy Mootien , Benjamin Dervieux , Yvon Ruch , Antonin Hugerot , Yves-Jean Zhu , Valentin Pointurier , Raphael Clere-Jehl , Valentin Greigert , Loic Kassegne , Nicolas Lefebvre , Floriane Gallais , Covid Alsace Study Group , Nicolas Meyer , Yves Hansmann , Olivier Hinschberger and François DanionBackgroundIn March 2020, the COVID-19 outbreak was declared a pandemic by the World Health Organization.
AimOur objective was to identify risk factors predictive of severe disease and death in France.
MethodsIn this prospective cohort study, we included patients ≥ 18 years old with confirmed COVID-19, hospitalised in Strasbourg and Mulhouse hospitals (France), in March 2020. We respectively compared patients who developed severe disease (admission to an intensive care unit (ICU) or death) and patients who died, to those who did not, by day 7 after hospitalisation.
ResultsAmong 1,045 patients, 424 (41%) had severe disease, including 335 (32%) who were admitted to ICU, and 115 (11%) who died. Mean age was 66 years (range: 20–100), and 612 (59%) were men. Almost 75% of patients with body mass index (BMI) data (n = 897) had a BMI ≥ 25 kg/m2 (n = 661). Independent risk factors associated with severe disease were advanced age (odds ratio (OR): 1.1 per 10-year increase; 95% CrI (credible interval): 1.0–1.2), male sex (OR: 2.1; 95% CrI: 1.5–2.8), BMI of 25–29.9 kg/m2 (OR: 1.8; 95% CrI: 1.2–2.7) or ≥ 30 (OR: 2.2; 95% CrI: 1.5–3.3), dyspnoea (OR: 2.5; 95% CrI: 1.8–3.4) and inflammatory parameters (elevated C-reactive protein and neutrophil count, low lymphocyte count). Risk factors associated with death were advanced age (OR: 2.7 per 10-year increase; 95% CrI: 2.1–3.4), male sex (OR: 1.7; 95% CrI: 1.1–2.7), immunosuppression (OR: 3.8; 95% CrI: 1.6–7.7), diabetes (OR: 1.7; 95% CrI: 1.0–2.7), chronic kidney disease (OR: 2.3; 95% CrI: 1.3–3.9), dyspnoea (OR: 2.1; 95% CrI: 1.2–3.4) and inflammatory parameters.
ConclusionsOverweightedness, obesity, advanced age, male sex, comorbidities, dyspnoea and inflammation are risk factors for severe COVID-19 or death in hospitalised patients. Identifying these features among patients in routine clinical practice might improve COVID-19 management.
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Epidemiology and public health response in early phase of COVID-19 pandemic, Veneto Region, Italy, 21 February to 2 April 2020
BackgroundVeneto was one of the Italian regions hit hardest by the early phase of the coronavirus disease (COVID-19) pandemic.
AimThis paper describes the public health response and epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the Veneto Region from 21 February to 2 April 2020.
MethodsInformation on the public health response was collected from regional health authorities’ official sources. Epidemiological data were extracted from a web-based regional surveillance system. The epidemic curve was represented by date of testing. Characteristics of hospitalised COVID-19 cases were described and compared to those never admitted to hospital. Age- and sex-stratified case-fatality ratios (CFRs) were calculated.
ResultsKey elements of the regional public health response were thorough case-finding and contact tracing, home care for non-severe cases, creation of dedicated COVID-19 healthcare facilities and activation of sub-intensive care units for non-invasive ventilation. As at 2 April 2020, 91,345 individuals were tested for SARS-CoV-2 and 10,457 (11.4%) were positive. Testing and attack rates were 18.6 per 1,000 and 213.2 per 100,000 population, respectively. The epidemic peaked around 20 to 24 March, with case numbers declining thereafter. Hospitalised cases (n = 3,623; 34.6%) were older and more frequently male compared with never-hospitalised cases. The CFR was 5.6% overall, and was higher among males and people > 60 years of age.
ConclusionIn the Veneto Region, the strict social distancing measures imposed by the Italian government were supported by thorough case finding and contact tracing, as well as well-defined roles for different levels of care.
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Impact of the COVID-19 pandemic on testing services for HIV, viral hepatitis and sexually transmitted infections in the WHO European Region, March to August 2020
We present preliminary results of a coronavirus disease (COVID-19) impact assessment on testing for HIV, viral hepatitis and sexually transmitted infections in the WHO European Region. We analyse 98 responses from secondary care (n = 36), community testing sites (n = 52) and national level (n = 10). Compared to pre-COVID-19, 95% of respondents report decreased testing volumes during March–May and 58% during June–August 2020. Reasons for decreases and mitigation measures were analysed.
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Serology- and PCR-based cumulative incidence of SARS-CoV-2 infection in adults in a successfully contained early hotspot (CoMoLo study), Germany, May to June 2020
Claudia Santos-Hövener , Hannelore K Neuhauser , Angelika Schaffrath Rosario , Markus Busch , Martin Schlaud , Robert Hoffmann , Antje Gößwald , Carmen Koschollek , Jens Hoebel , Jennifer Allen , Antje Haack-Erdmann , Stefan Brockmann , Thomas Ziese , Andreas Nitsche , Janine Michel , Sebastian Haller , Hendrik Wilking , Osamah Hamouda , Victor M Corman , Christian Drosten , Lars Schaade , Lothar H Wieler , CoMoLo Study Group and Thomas LampertThree months after a coronavirus disease (COVID-19) outbreak in Kupferzell, Germany, a population-based study (n = 2,203) found no RT-PCR-positives. IgG-ELISA seropositivity with positive virus neutralisation tests was 7.7% (95% confidence interval (CI): 6.5–9.1) and 4.3% with negative neutralisation tests. We estimate 12.0% (95% CI: 10.4–14.0%) infected adults (24.5% asymptomatic), six times more than notified. Full hotspot containment confirms the effectiveness of prompt protection measures. However, 88% naïve adults are still at high COVID-19 risk.
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Where has all the influenza gone? The impact of COVID-19 on the circulation of influenza and other respiratory viruses, Australia, March to September 2020
The coronavirus disease pandemic was declared in March 2020, as the southern hemisphere’s winter approached. Australia expected co-circulation of severe acute respiratory syndrome coronavirus 2, influenza and other seasonal respiratory viruses. However, influenza notifications were 7,029 (March–September) compared with an average 149,832 for the same period in 2015–2109, despite substantial testing. Restrictions on movement within and into Australia may have temporarily eliminated influenza. Other respiratory pathogens also showed remarkably changed activity in 2020.
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Virological surveillance of influenza viruses in the WHO European Region in 2019/20 – impact of the COVID-19 pandemic
The COVID-19 pandemic negatively impacted the 2019/20 WHO European Region influenza surveillance. Compared with previous 4-year averages, antigenic and genetic characterisations decreased by 17% (3,140 vs 2,601) and 24% (4,474 vs 3,403). Of subtyped influenza A viruses, 56% (26,477/47,357) were A(H1)pdm09, 44% (20,880/47,357) A(H3). Of characterised B viruses, 98% (4,585/4,679) were B/Victoria. Considerable numbers of viruses antigenically differed from northern hemisphere vaccine components. In 2020/21, maintaining influenza virological surveillance, while supporting SARS-CoV-2 surveillance is crucial.
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Convalescent plasma for COVID-19: male gender, older age and hospitalisation associated with high neutralising antibody levels, England, 22 April to 12 May 2020
More LessWe analysed factors associated with neutralising antibody levels in 330 convalescent plasma donors. Women and younger donors were more likely not to have measurable neutralising antibodies, while higher antibody levels were observed in men, in older donors and in those who had been hospitalised. These data will be of value in the timely recruitment of convalescent plasma donors most likely to have high levels of neutralising antibodies for ongoing studies investigating its effectiveness.
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Epidemiological parameters of COVID-19 and its implication for infectivity among patients in China, 1 January to 11 February 2020
Qing-Bin Lu , Yong Zhang , Ming-Jin Liu , Hai-Yang Zhang , Neda Jalali , An-Ran Zhang , Jia-Chen Li , Han Zhao , Qian-Qian Song , Tian-Shuo Zhao , Jing Zhao , Han-Yu Liu , Juan Du , Ai-Ying Teng , Zi-Wei Zhou , Shi-Xia Zhou , Tian-Le Che , Tao Wang , Tong Yang , Xiu-Gang Guan , Xue-Fang Peng , Yu-Na Wang , Yuan-Yuan Zhang , Shou-Ming Lv , Bao-Cheng Liu , Wen-Qiang Shi , Xiao-Ai Zhang , Xiao-Gang Duan , Wei Liu , Yang Yang and Li-Qun FangBackgroundThe natural history of disease in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remained obscure during the early pandemic.
AimOur objective was to estimate epidemiological parameters of coronavirus disease (COVID-19) and assess the relative infectivity of the incubation period.
MethodsWe estimated the distributions of four epidemiological parameters of SARS-CoV-2 transmission using a large database of COVID-19 cases and potential transmission pairs of cases, and assessed their heterogeneity by demographics, epidemic phase and geographical region. We further calculated the time of peak infectivity and quantified the proportion of secondary infections during the incubation period.
ResultsThe median incubation period was 7.2 (95% confidence interval (CI): 6.9‒7.5) days. The median serial and generation intervals were similar, 4.7 (95% CI: 4.2‒5.3) and 4.6 (95% CI: 4.2‒5.1) days, respectively. Paediatric cases < 18 years had a longer incubation period than adult age groups (p = 0.007). The median incubation period increased from 4.4 days before 25 January to 11.5 days after 31 January (p < 0.001), whereas the median serial (generation) interval contracted from 5.9 (4.8) days before 25 January to 3.4 (3.7) days after. The median time from symptom onset to discharge was also shortened from 18.3 before 22 January to 14.1 days after. Peak infectivity occurred 1 day before symptom onset on average, and the incubation period accounted for 70% of transmission.
ConclusionThe high infectivity during the incubation period led to short generation and serial intervals, necessitating aggressive control measures such as early case finding and quarantine of close contacts.
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SARS-CoV-2 samples may escape detection because of a single point mutation in the N gene
We found that a single nucleotide polymorphism (SNP) in the nucleoprotein gene of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from a patient interfered with detection in a widely used commercial assay. Some 0.2% of the isolates in the EpiCoV database contain this SNP. Although SARS-CoV-2 was still detected by the other probe in the assay, this underlines the necessity of targeting two independent essential regions of a pathogen for reliable detection.
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Surveillance of COVID-19 school outbreaks, Germany, March to August 2020
More LessMitigation of the coronavirus disease (COVID-19) pandemic in Germany included school closures in early March 2020. After reopening in April, preventive measures were taken in schools. We analysed national surveillance system data on COVID-19 school outbreaks during different time periods. After reopening, smaller outbreaks (average: 2.2/week) occurred despite low incidence in the general population. School closures might have a detrimental effect on children and should be applied only cautiously and in combination with other measures.
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Poor self-reported adherence to COVID-19-related quarantine/isolation requests, Norway, April to July 2020
To limit SARS-CoV-2 spread, quarantine and isolation are obligatory in several situations in Norway. We found low self-reported adherence to requested measures among 1,704 individuals (42%; 95% confidence interval: 37–48). Adherence was lower in May–June–July (33–38%) compared with April (66%), and higher among those experiencing COVID-19-compatible symptoms (71%) compared with those without (28%). These findings suggest that consideration is required of strategies to improve people’s adherence to quarantine and isolation.
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