Seroprevalence of IgG antibodies to SARS-coronavirus in asymptomatic or subclinical population groups

Abstract

SUMMARYWe systematically reviewed the current understanding of human population immunity against SARS-CoV in different groups, settings and geography. Our meta-analysis, which included all identified studies except those on wild animal handlers, yielded an overall seroprevalence of 0 . 10% [95% confidence interval (CI) 0 . 02-0 . 18]. Health-care workers and others who had close contact with SARS patients had a slightly higher degree of seroconversion (0 . 23%, 95 % CI 0 . 02-0 . 45) compared to healthy blood donors, others from the general community or non-SARS patients recruited from the health-care setting (0 . 16%, 95% CI 0-0 . 37). When analysed by the two broad classes of testing procedures, it is clear that serial confirmatory test protocols resulted in a much lower estimate (0 . 050%, 95 % CI 0-0 . 15) than single test protocols (0 . 20 %, 95% CI 0 . 06-0 . 34). Potential epidemiological and laboratory pitfalls are also discussed as they may give rise to false or inconsistent results in measuring the seroprevalence of IgG antibodies to SARS-CoV.

Prise en charge de 90 patients suspects de syndrome respiratoire aigu sévère. Expérience d'une collaboration épidémioclinique en situation d'alerte sanitaire liée à une infection émergente

“…L'absence de résultat positif chez les 19 patients testés en raison d'une forte suspicion clinique laissait persister à l'époque un doute sur l'existence de formes frustes de SRAS ou d'infections asymptomatiques, fréquentes dans d'autres infections virales. Malgré une incidence modérée, leur réalité au cours de l'infection à SARS-CoV était mise en évidence secondairement [20][21][22][23][24][25][26], principalement en cas de contact direct avec un animal porteur de SARS-CoV [23] ou par transmission interhumaine en cas de contact rapproché (personnel soignant, notamment). En Asie du Sud-Est, l'incidence des formes asymptomatiques fut estimée à 0,23 % au sein du personnel soignant et à 0,16 % dans la population générale [24], celle des formes frustes atypiques à 12 % [25].…”

Epidemic and Emerging Coronaviruses (Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome)

“…50 Asymptomatic infection seems uncommon because a metaanalysis has shown overall seroprevalence rates of 0.1% for the general population and 0.23% for HCWs, although the true incidence of asymptomatic infection remains unknown. 51 There was a typical pattern for the clinical course of SARS 5,24,52,53 : phase 1 (viral replication) was associated with increasing SARS-CoV load and characterized by fever, myalgia, and other systemic symptoms that generally improved after a few days; phase 2 (immunopathologic injury) was characterized by recurrence of fever, hypoxemia, and radiologic progression of pneumonia with falls in viral load whereas approximately 20% of patients progressed into acute respiratory distress syndrome (ARDS), necessitating invasive mechanical ventilatory support. 5,53 Because there was peaking of viral load on day 10 of illness followed by progressive decrease in rates of viral shedding from nasopharynx, stool, and urine from day 10 to day 21 after symptom onset, clinical worsening during phase 2 was likely the result of immune-mediated lung injury due to an overexuberant host response.…”

An Architecture of Deep Learning Method to Predict Traffic Flow in Big Data

“…Leung et al [1] have discussed the introduction of machine learning (ML) tasks, to address the issues in genomic medicine (GM) (helps in determination of individual DNA variations). The study provides the platform for future computational method for effective GM.…”

“…Collectively all the layers together with the predictor form the deep network. Exclusively using the back propagation along with the gradient-descent based optimization is proved to be slow and sometimes halts in deep networks, as noted by James Martens et al [1]. Hence greedy layer wise unsupervised algorithm is considered for training, as suggested by Hinton et al [2], to train the network starting from hidden layer 1.…”

“…The main objective associated with the prediction of traffic information is to provide the traffic information to each and every vehicle associated with one network in order to optimize the traffic congestion issues in vehicular adhoc networks (VANET) [1] [2]. The recent conventional studies highlights that now a days the deployment of Intelligent Transport Systems (ITSs) has improved the traffic operation efficiency with respect to better travel decision making, reducing carbon emissions optimization of level of congestion.…”