Coronavirus disease (COVID-19)

Standard testing for infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is based on RT-PCR tests, but detection of viral genetic material alone does not indicate ongoing infectious potential. The ability to isolate whole virus represents a better proxy for infectivity.

The objective of this study was to gain an understanding of the current literature and compare the reported periods of positive SARS-CoV-2 detection from studies that conducted RT-PCR testing in addition to experiments isolating whole virus.

Using a rapid review approach, studies reporting empirical data on the duration of positive RT-PCR results and/or successful viral isolation following SARS-CoV-2 infection in humans were identified through searches of peer-reviewed and pre-print health sciences literature. Articles were screened for relevance, then data were extracted, analysed, and synthesised.

Of the 160 studies included for qualitative analysis, 84% (n = 135) investigated duration of positive RT-PCR tests only, 5% (n = 8) investigated duration of successful viral isolations, while 11% (n = 17) included measurements on both. There was significant heterogeneity in reported data. There was a prolonged time to viral clearance when deduced from RT-PCR tests compared with viral isolations (median: 26 vs 9 days).

Findings from this review support a minimum 10-day period of isolation but certain cases where virus was isolated after 10 days were identified. Given the extended time to viral clearance from RT-PCR tests, future research should ensure standard reporting of RT-PCR protocols and results to help inform testing policies aimed at clearance from isolation.

Children have a low rate of COVID-19 and secondary severe multisystem inflammatory syndrome (MIS) but present a high prevalence of symptomatic seasonal coronavirus infections.

We tested if prior infections by seasonal coronaviruses (HCoV) NL63, HKU1, 229E or OC43 as assessed by serology, provide cross-protective immunity against SARS-CoV-2 infection.

We set a cross-sectional observational multicentric study in pauci- or asymptomatic children hospitalised in Paris during the first wave for reasons other than COVID (hospitalised children (HOS), n = 739) plus children presenting with MIS (n = 36). SARS-CoV-2 antibodies directed against the nucleoprotein (N) and S1 and S2 domains of the spike (S) proteins were monitored by an in-house luciferase immunoprecipitation system assay. We randomly selected 69 SARS-CoV-2-seropositive patients (including 15 with MIS) and 115 matched SARS-CoV-2-seronegative patients (controls (CTL)). We measured antibodies against SARS-CoV-2 and HCoV as evidence for prior corresponding infections and assessed if SARS-CoV-2 prevalence of infection and levels of antibody responses were shaped by prior seasonal coronavirus infections.

Prevalence of HCoV infections were similar in HOS, MIS and CTL groups. Antibody levels against HCoV were not significantly different in the three groups and were not related to the level of SARS-CoV-2 antibodies in the HOS and MIS groups. SARS-CoV-2 antibody profiles were different between HOS and MIS children.

Prior infection by seasonal coronaviruses, as assessed by serology, does not interfere with SARS-CoV-2 infection and related MIS in children.

Sensitive molecular diagnostics and correct test interpretation are crucial for accurate COVID-19 diagnosis and thereby essential for good clinical practice. Furthermore, they are a key factor in outbreak control where active case finding in combination with isolation and contact tracing are crucial.

With the objective to inform the public health and laboratory responses to the pandemic, we reviewed current published knowledge on the kinetics of SARS-CoV-2 infection as assessed by RNA molecular detection in a wide range of clinical samples.

We performed an extensive search on studies published between 1 December 2019 and 15 May 2020, reporting on molecular detection and/or isolation of SARS-CoV-2 in any human laboratory specimen.

We compiled a dataset of 264 studies including 32,515 COVID-19 cases, and additionally aggregated data points (n = 2,777) from sampling of 217 adults with known infection timeline. We summarised data on SARS-CoV-2 detection in the respiratory and gastrointestinal tract, blood, oral fluid, tears, cerebrospinal fluid, peritoneal fluid, semen, vaginal fluid; where provided, we also summarised specific observations on SARS-CoV-2 detection in pregnancy, infancy, children, adolescents and immunocompromised individuals.

Optimal SARS-CoV-2 molecular testing relies on choosing the most appropriate sample type, collected with adequate sampling technique, and with the infection timeline in mind. We outlined knowledge gaps and directions for future well-documented systematic studies.

Timely monitoring of COVID-19 impact on mortality is critical for rapid risk assessment and public health action.

Building upon well-established models to estimate influenza-related mortality, we propose a new statistical Attributable Mortality Model (AttMOMO), which estimates mortality attributable to one or more pathogens simultaneously (e.g. SARS-CoV-2 and seasonal influenza viruses), while adjusting for seasonality and excess temperatures.

Data from Nationwide Danish registers from 2014-week(W)W27 to 2020-W22 were used to exemplify utilities of the model, and to estimate COVID-19 and influenza attributable mortality from 2019-W40 to 2020-W20.

SARS-CoV-2 was registered in Denmark from 2020-W09. Mortality attributable to COVID-19 in Denmark increased steeply, and peaked in 2020-W14. As preventive measures and national lockdown were implemented from 2020-W12, the attributable mortality started declining within a few weeks. Mortality attributable to COVID-19 from 2020-W09 to 2020-W20 was estimated to 16.2 (95% confidence interval (CI): 12.0 to 20.4) per 100,000 person-years. The 2019/20 influenza season was mild with few deaths attributable to influenza, 3.2 (95% CI: 1.1 to 5.4) per 100,000 person-years.

AttMOMO estimates mortality attributable to several pathogens simultaneously, providing a fuller picture of mortality by COVID-19 during the pandemic in the context of other seasonal diseases and mortality patterns. Using Danish data, we show that the model accurately estimates mortality attributable to COVID-19 and influenza, respectively. We propose using standardised indicators for pathogen circulation in the population, to make estimates comparable between countries and applicable for timely monitoring.

Severe 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.

To test if the quantification of SARS-CoV-2 genomes in WW correlates with the number of symptomatic or non-symptomatic carriers.

We 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).

We 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.

This 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.

The 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.

To describe reported cases and the impact of national lockdown, and to identify disease severity risk factors.

National 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.

The 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).

COVID-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.

On 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.

Our aim was to describe the epidemiology and transmission dynamics of the first COVID-19 cases in Italy amid ongoing control measures.

We 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.

Of 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.

The 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.

In March 2020, the COVID-19 outbreak was declared a pandemic by the World Health Organization.

Our objective was to identify risk factors predictive of severe disease and death in France.

In 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.

Among 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.

Overweightedness, 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.

The natural history of disease in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remained obscure during the early pandemic.

Our objective was to estimate epidemiological parameters of coronavirus disease (COVID-19) and assess the relative infectivity of the incubation period.

We 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.

The 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.

The 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.