les fermes à fourrure y sont largement évoquées et, avec quarante-six occurrences, les visons tiennent une place de premier choix dans les débats. Les abattages ayant lieu chaque année entre le 15 novembre et le 15 décembre, il est difficile de ne pas être frappé par la concordance de ces dates avec celles de l’émergence du coronavirus…

Pour compliquer encore le travail d’enquête, d’ailleurs, les bases de données de coronavirus de chauves-souris que détenait le Wuhan Institute of Virology ne sont plus en ligne depuis septembre 2019, sans aucune explication satisfaisante

Ainsi, les critères de performance cliniques retenus pour valider les tests sont-ils une sensibilité clinique supérieure ou égale à 80 % et une spécificité clinique supérieure ou égale à 99% chez les sujets symptomatiques.

En France, si on se fie aux tests antigéniques, qui sont techniquement très proches, on est plutôt autour de 50 à 60% dans ce type de population asymptomatique.

Genomic reconstruction of the SARS-CoV-2 epidemic across England from September 2020 to May 2021

Abstract:
Despite regional successes in controlling the SARS-CoV-2 pandemic, global cases havereached an all time high in April 2021 in part due to the evolution of more transmissiblevariants. Here we use the dense genomic surveillance generated by the COVID-19Genomics UK Consortium to reconstruct the dynamics of 62 different lineages in each of 315English local authorities between September 2020 and April 2021. This analysis reveals aseries of sub-epidemics that peaked in the early autumn of 2020, followed by a singular jumpin transmissibility of the B.1.1.7 lineage. B.1.1.7 grew when other lineages declined duringthe second national lockdown and regionally tiered restrictions between November andDecember 2020. A third more stringent national lockdown eventually suppressed B.1.1.7and eliminated nearly all other lineages in early 2021. However, a series of variants (mostlycontaining the spike E484K mutation) defied these trends and persisted at moderatelyincreasing proportions. Accounting for sustained introductions, however, indicates that theirtransmissibility is unlikely to exceed that of B.1.1.7. Finally, B.1.617.2 was repeatedlyintroduced to England and grew rapidly in April 2021, constituting approximately 40% ofsampled COVID-19 genomes on May 15.

In the United Kingdom, by early May 2021 the COVID-19 Genomics UK Consortium(COG-UK) has sequenced more than 450,000 viral samples. These data have enabled thereconstruction of the dynamics of the first 6 months of the epidemic in the UK in great detail(du Plessis et al., 2021). Here, we leverage a subset of those data: genomic surveillancegenerated by the Wellcome Sanger Institute Covid-19 Surveillance Team as part ofCOG-UK, to perform a dense reconstruction and characterisation of the spread and diversityof different SARS-CoV-2 lineages in space and time. We analyse the impact of two nationallockdowns and tiered restrictions in the winter of 2020-21, track the elimination of previouslydominant SARS-CoV-2 lineages in the early spring of 2021, and assess relative growth ratesof variants introduced in the first months of 2021.

That translates to about one in 50,000 vaccine recipients, a number that isn’t concerning given the background rate of myocarditis in the general population, where it is typically triggered by viral or bacterial infections, including COVID-19.

Statistical analysis176Categorical variables were presented as n (%). We used the Wilcoxon Mann Whitney test,  
177Student t-test, χ2 test, or Fisher’s exact test to compare differences between groupsof patients  
178where appropriate.We performed multiple correspondence analysis (MCA) to investigate the  
179associations between clinical data, biologicaldata, radiological data, and the treatment  
180received.In order to control for selection bias in comparing mortality between treatment  
181groups, we used a propensity score weighting approach. The propensity score was calculated  
182using a logistic regression with sex, age groups, NEWS-2score, comorbidities and in-hospital  
183treatment(s) (HCQ, AZ, Zinc and/or corticosteroids whenappropriate) as covariates. The  
184predicted probabilities from the propensity-score model were then used to calculate the  
185stabilised inverse-probability-weighting weights (28). The association between treatment  
186groups and mortality was then assessed using a weighted multivariable Cox models. Cox 1
87models were adjusted on the following variables: sex, age groups, NEWS-2score,  
188comorbidities and in-hospital treatment (HCQ, AZ, Zinc and/or corticosteroids where  
189appropriate). Adjusted hazard ratios with 95% confidence intervals were calculated from the  
190Cox regression coefficient estimates.Sensitivity analyses wereperformed by assessing  
191whether observed effects were reproducible and consistent across subgroups according to age  
192class, sex, comorbidities, disease severity, co-medications, and reasons for non-treatment. A
193two-sided α value of less than 0.05 was considered to be statistically significant. Analyses  
194were carried out using SAS 9.4 statistical software (SAS Institute, Cary, NC).

An improvement to the inverse probability of treatment weighting is the use of stabilized weights (SW). Stabilized weights have been proposed in modeling time-varying treatment status in reducing selection bias in observational studies [16, 17]. The purpose of using SW in these studies is to reduce the weights of either those treated subjects with low propensity scores or those untreated subjects with high propensity scores.

Discussion:
 
In this paper we demonstrate several advantages of SWs over IPTWs in analyzing data obtained from observational studies. First, using SWs can reduce the weights of either those treated subjects with low propensity scores or those untreated subjects with high propensity scores in the pseudo data sets. Our serum potassium monitoring example showed that IPTWs have larger standard deviation and wider range than SWs (Table 4). Thus results using SWs are robust even with few observations with extreme IPTWs. Second, unlike variance estimators, no additional steps are needed when SWs are used because the SW approach provides appropriate variance estimates and confidence intervals of treatment effect from conventional regression models for fitting the outcome variables. Third, computer programming is simple for one to use SWs to obtain the effect of treatment effects and confidence intervals as compared to the bootstrap approach. One only needs to calculate the weights differently. Fourth, in our simulation studies and example, outcome variables are dichotomous. Unlike those developed estimators, the SW approach is applicable to outcome variables (e.g., dichotomous, continuous and count data) that have a finite distribution. Our simulation results also show that SW is a reasonable alternative to the robust variance estimator and has the advantage of reducing influential weights.
 
The limitation of the SW approach is the uncertainty of the influence of continuous confounders when their association with the probability of being treated and outcome is very strong. As shown in simulation studies, the sample size in some of the pseudo data sets can be different from the original data set when the confounding effect is strong. However, it is uncommon as our simulation results showed that the mean sample size approximated the original sample size. It is recommended that one always examine the difference between sample sizes in the original cohort and the pseudo cohort. When there is evidence that the sample size of the pseudo data is different from that of the original dataset, one can use the robust variance estimator with IPTWs although this latter method can produce slightly larger standard errors.
 
We conclude that our theoretical, simulation results and the real data example demonstrate that the use of the stabilized weights in the pseudo data preserves the sample size close to the original data. In addition, we conclude that use of stabilized weights produces the appropriate estimation of the variance of the main effect and maintains an appropriate type I error rate. Stabilized weights may be a useful tool to balance confounders between groups in observational studies.