Brownian distance covariance

Distance correlation is a new class of multivariate dependence coefficients applicable to random vectors of arbitrary and not necessarily equal dimension. Distance covariance and distance correlation are analogous to product-moment covariance and correlation, but generalize and extend these classical bivariate measures of dependence. Distance correlation characterizes independence: it is zero if and only if the random vectors are independent. The notion of covariance with respect to a stochastic process is introduced, and it is shown that population distance covariance coincides with the covariance with respect to Brownian motion; thus, both can be called Brownian distance covariance. In the bivariate case, Brownian covariance is the natural extension of product-moment covariance, as we obtain Pearson product-moment covariance by replacing the Brownian motion in the definition with identity. The corresponding statistic has an elegantly simple computing formula. Advantages of applying Brownian covariance and correlation vs the classical Pearson covariance and correlation are discussed and illustrated.Comment: This paper discussed in: [arXiv:0912.3295], [arXiv:1010.0822], [arXiv:1010.0825], [arXiv:1010.0828], [arXiv:1010.0836], [arXiv:1010.0838], [arXiv:1010.0839]. Rejoinder at [arXiv:1010.0844]. Published in at http://dx.doi.org/10.1214/09-AOAS312 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

DISCO analysis: A nonparametric extension of analysis of variance

In classical analysis of variance, dispersion is measured by considering squared distances of sample elements from the sample mean. We consider a measure of dispersion for univariate or multivariate response based on all pairwise distances between-sample elements, and derive an analogous distance components (DISCO) decomposition for powers of distance in $(0,2]$. The ANOVA F statistic is obtained when the index (exponent) is 2. For each index in $(0,2)$, this decomposition determines a nonparametric test for the multi-sample hypothesis of equal distributions that is statistically consistent against general alternatives.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS245 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Measuring and testing dependence by correlation of distances

Distance correlation is a new measure of dependence between random vectors. Distance covariance and distance correlation are analogous to product-moment covariance and correlation, but unlike the classical definition of correlation, distance correlation is zero only if the random vectors are independent. The empirical distance dependence measures are based on certain Euclidean distances between sample elements rather than sample moments, yet have a compact representation analogous to the classical covariance and correlation. Asymptotic properties and applications in testing independence are discussed. Implementation of the test and Monte Carlo results are also presented.Comment: Published in at http://dx.doi.org/10.1214/009053607000000505 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Enhanced epidemiological surveillance of influenza A(H1N1)v in Italy.

As of 7 July 2009, a total of 158 laboratory-confirmed cases of influenza A(H1N1)v were reported in Italy, from half of the 21 Italian regions. To date all cases have had symptoms consistent with seasonal influenza and no severe or fatal cases have been reported. An active surveillance of cases has been set up in Italy in order to undertake appropriate measures to slow down the spread of the new virus. This report describes the routine and enhanced surveillance currently ongoing in Italy

Epidemiological surveillance of West Nile neuroinvasive diseases in Italy, 2008 to 2011.

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Vaccine effectiveness against laboratory-confirmed influenza in Europe – Results from the DRIVE network during season 2018/19

The DRIVE project aims to establish a sustainable network to estimate brand-specific influenza vaccine effectiveness (IVE) annually. DRIVE is a public–private partnership launched in response to EMA guidance that requires effectiveness evaluation from manufacturers for all individual influenza vaccine brands every season. IVE studies are conducted by public partners in DRIVE. Private partners (vaccine manufacturers from the European Federation of Pharmaceutical Industries and Association (EFPIA)) provide written feedback moderated by an independent scientific committee. Test-negative design (TND) case-control studies (4 in primary care and five in hospital) were conducted in six countries in Europe during the 2018/19 season. Site-specific confounder-adjusted vaccine effectiveness (VE) estimates for any vaccine exposure were calculated by age group (<18 years (y), 18-64y and 65 + y) and pooled by setting (primary care, hospital) through random effects meta-analysis. In addition, one population-based cohort study was conducted in Finland. TND studies included 3339 cases and 6012 controls; seven vaccine brands were reported. For ages 65 + y, pooled VE against any influenza strain was estimated at 27% (95%CI 6–44) in hospital setting. Sample size was insufficient for meaningful IVE estimates in other age groups, in the primary care setting, or by vaccine brand. The population-based cohort study included 274,077 vaccinated and 494,337 unvaccinated person-years, two vaccine brands were reported. Brand-specific IVE was estimated for Fluenz Tetra (36% [95%CI 24–45]) for ages 2-6y, Vaxigrip Tetra (54% [43–62]) for ages 6 months to 6y, and Vaxigrip Tetra (30% [25–35]) for ages 65 + y. The results presented are from the second influenza season covered by the DRIVE network. While sample size from the pooled TND studies was still too low for precise (brand-specific) IVE estimates, the network has approximately doubled in size compared to the pilot season. Taking measures to increase sample size is an important focus of DRIVE for the coming years

Brand-specific influenza vaccine effectiveness estimates during 2019/20 season in Europe – Results from the DRIVE EU study platform

DRIVE (Development of Robust and Innovative Vaccine Effectiveness) is an IMI funded public–private platform that aims to annually estimate brand-specific influenza vaccine effectiveness (IVE), for public health and regulatory purposes. IVE analyses and reporting are conducted by public partners in the con- sortium. In 2019/20, four primary care-based test-negative design (TND) studies (Austria, England, Italy (n = 2)), eight hospital-based TND studies (Finland, France, Italy, Romania, Spain (n = 4)), and one population- based cohort study (Finland) were conducted. The COVID-19 pandemic affected influenza surveillance in all participating study sites, therefore the study period was truncated on February 29, 2020. Age- stratified (6 m-17y, 18-64y, !65y), confounder-adjusted, site-specific adjusted IVE estimates were calcu- lated and pooled through meta-analysis. Parsimonious confounder-adjustment was performed, adjusting the estimates for age, sex and calendar time. TND studies included 3531 cases (351 vaccinated) and 5546 controls (1415 vaccinated) of all ages. IVE estimates were available for 8/11 brands marketed in Europe in 2019. Most children and adults < 64y were captured in primary care setting and the most frequently observed vaccine brand was Vaxigrip Tetra. The estimate against any influenza for Vaxigrip Tetra in primary care setting was 61% (95%CI 38–77) in children and 32% (95%CI 13–59) in adults up to 64y. Most adults ! 65y were captured in hospital setting and the most frequently observed brand was Fluad, with an estimate of 52% (95%CI 27–68)