Assessment of the MERS-CoV epidemic situation in the Middle East region

The appearance of a novel coronavirus named Middle East (ME) Respiratory Syndrome Coronavirus (MERS-CoV) has raised global public health concerns regarding the current situation and its future evolution. Here we propose an integrative maximum likelihood analysis of both cluster data in the ME region and importations in Europe to assess transmission scenario and incidence of sporadic infections. Our approach is based on a spatial-transmission model integrating mobility data worldwide and allows for variations in the zoonotic/environmental transmission and underascertainment. Maximum likelihood estimates for the ME region indicate the occurrence of a subcritical epidemic (R=0.50, 95% confidence interval (CI) 0.30-0.77) associated with a 0.28 (95% CI 0.12-0.85) daily rate of sporadic introductions. Infections in the region appear to be mainly dominated by zoonotic/environmental transmissions, with possible underascertainment (95% CI of estimated to observed sporadic cases in the range 1.03-7.32). No time evolution of the situation emerges. Analyses of flight passenger data from the region indicate areas at high risk of importation. While dismissing an immediate threat for global health security, this analysis provides a baseline scenario for future reference and updates, suggests reinforced surveillance to limit underascertainment, and calls for increased alertness in high-risk areas worldwide.Comment: in press on Eurosurveillance, 16 pages, 3 figure

Dengue serosurvey after a 2-month long outbreak in Nîmes, France, 2015: was there more than met the eye?

BackgroundClusters of dengue cases have recently become more frequent in areas of southern France colonised by the vector mosquito Aedes albopictus. In July 2015, a 2-month outbreak of dengue virus serotype 1 (DENV-1) was reported in Nîmes. Aim: We conducted a serosurvey in the affected area at the end of the vector activity period to determine the true extent of dengue transmission. Methods: We collected capillary blood from consenting household members, and information on their medical and travel histories, and exposure to mosquito bites. Recent infections were identified using IgM and IgG anti-DENV ELISA, followed, when positive, by plaque reduction neutralisation tests on serum against DENV 1-4 and West Nile virus. The prevalence estimator was calibrated on reference demographic data. We quantified the spatial clustering of dengue cases within the affected community and inferred the transmission tree. Results: The study participation rate was 39% (564/1,431). Three of 564 participants tested positive for DENV-1 infection (after marginal calibration, 0.41%; 95% confidence interval: 0.00-0.84). The spatial analysis showed that cases were clustered at the household level. Most participants perceived the presence of mosquitos as abundant (83%) and reported frequent mosquito bites (57%). We incidentally identified six past West Nile virus infections (0.9%; 95% CI: 0.2-1.6). Conclusion: This serosurvey confirms the potential for arboviral diseases to cause outbreaks - albeit limited for now - in France and Europe

Planning for the next influenza H1N1 season: a modelling study

<p>Abstract</p> <p>Background</p> <p>The level of herd immunity before and after the first 2009 pandemic season is not precisely known, and predicting the shape of the next pandemic H1N1 season is a difficult challenge.</p> <p>Methods</p> <p>This was a modelling study based on data on medical visits for influenza-like illness collected by the French General Practitioner Sentinel network, as well as pandemic H1N1 vaccination coverage rates, and an individual-centred model devoted to influenza. We estimated infection attack rates during the first 2009 pandemic H1N1 season in France, and the rates of pre- and post-exposure immunity. We then simulated various scenarios in which a pandemic influenza H1N1 virus would be reintroduced into a population with varying levels of protective cross-immunity, and considered the impact of extending influenza vaccination.</p> <p>Results</p> <p>During the first pandemic season in France, the proportion of infected persons was 18.1% overall, 38.3% among children, 14.8% among younger adults and 1.6% among the elderly. The rates of pre-exposure immunity required to fit data collected during the first pandemic season were 36% in younger adults and 85% in the elderly. We estimated that the rate of post-exposure immunity was 57.3% (95% Confidence Interval (95%CI) 49.6%-65.0%) overall, 44.6% (95%CI 35.5%-53.6%) in children, 53.8% (95%CI 44.5%-63.1%) in younger adults, and 87.4% (95%CI 82.0%-92.8%) in the elderly.</p> <p>The shape of a second season would depend on the degree of persistent protective cross-immunity to descendants of the 2009 H1N1 viruses. A cross-protection rate of 70% would imply that only a small proportion of the population would be affected. With a cross-protection rate of 50%, the second season would have a disease burden similar to the first, while vaccination of 50% of the entire population, in addition to the population vaccinated during the first pandemic season, would halve this burden. With a cross-protection rate of 30%, the second season could be more substantial, and vaccination would not provide a significant benefit.</p> <p>Conclusions</p> <p>These model-based findings should help to prepare for a second pandemic season, and highlight the need for studies of the different components of immune protection.</p

The Chikungunya Epidemic on La Réunion Island in 2005–2006: A Cost-of-Illness Study

For a long time, studies of chikungunya virus infection have been neglected, but since its resurgence in the south-western Indian Ocean and on La Réunion Island, this disease has been paid greater amounts of attention. The economic and social impacts of chikungunya epidemics are poorly documented, including in developed countries. This study estimated the cost-of-illness associated with the 2005–2006 chikungunya epidemics on La Réunion Island, a French overseas department with an economy and health care system of a developed country. “Cost-of-illness” studies measure the amount that would have been saved in the absence of a disease. We found that the epidemic incurred substantial medical expenses estimated at €43.9 million, of which 60% were attributable to direct medical costs related, in particular, to expenditure on medical consultations (47%), hospitalization (32%) and drugs (19%). The costs related to care in ambulatory and hospitalized cases were €90 and €2000 per case, respectively. This study provides the basic inputs for conducting cost-effectiveness and cost-benefit evaluations of chikungunya prevention strategies

Online detection and quantification of epidemics

<p>Abstract</p> <p>Background</p> <p>Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses.</p> <p>Results</p> <p>We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at <url>http://www.u707.jussieu.fr/periodic_regression/</url>. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea).</p> <p>Conclusion</p> <p>The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.</p

Field Effectiveness of Pandemic and 2009-2010 Seasonal Vaccines against 2009-2010 A(H1N1) Influenza: Estimations from Surveillance Data in France

BACKGROUND: In this study, we assess how effective pandemic and trivalent 2009-2010 seasonal vaccines were in preventing influenza-like illness (ILI) during the 2009 A(H1N1) pandemic in France. We also compare vaccine effectiveness against ILI versus laboratory-confirmed pandemic A(H1N1) influenza, and assess the possible bias caused by using non-specific endpoints and observational data. METHODOLOGY AND PRINCIPAL FINDINGS: We estimated vaccine effectiveness by using the following formula: VE  =  (PPV-PCV)/(PPV(1-PCV)) × 100%, where PPV is the proportion vaccinated in the population and PCV the proportion of vaccinated influenza cases. People were considered vaccinated three weeks after receiving a dose of vaccine. ILI and pandemic A(H1N1) laboratory-confirmed cases were obtained from two surveillance networks of general practitioners. During the epidemic, 99.7% of influenza isolates were pandemic A(H1N1). Pandemic and seasonal vaccine uptakes in the population were obtained from the National Health Insurance database and by telephonic surveys, respectively. Effectiveness estimates were adjusted by age and week. The presence of residual biases was explored by calculating vaccine effectiveness after the influenza period. The effectiveness of pandemic vaccines in preventing ILI was 52% (95% confidence interval: 30-69) during the pandemic and 33% (4-55) after. It was 86% (56-98) against confirmed influenza. The effectiveness of seasonal vaccines against ILI was 61% (56-66) during the pandemic and 19% (-10-41) after. It was 60% (41-74) against confirmed influenza. CONCLUSIONS: The effectiveness of pandemic vaccines in preventing confirmed pandemic A(H1N1) influenza on the field was high, consistently with published findings. It was significantly lower against ILI. This is unsurprising since not all ILI cases are caused by influenza. Trivalent 2009-2010 seasonal vaccines had a statistically significant effectiveness in preventing ILI and confirmed pandemic influenza, but were not better in preventing confirmed pandemic influenza than in preventing ILI. This lack of difference might be indicative of selection bias

Nouveaux outils et nouvelles données pour la surveillance des maladies infectieuses

Concerns about bioterrorism, emerging pathogens and pandemic influenza resulted, this last ten years, in a surge of development of new public health surveillance systems, often based on new data sources, designed to provide more timely detection of outbreaks of infectious diseases. The present thesis first focused on particular statistical methods used for outbreak detection from temporal surveillance data: the periodic regression models. We then evaluated two non clinical data sources potentially useful for the surveillance of infectious diseases in France (medication sales and web searches). Periodic regression models allow detecting and quantifying epidemics from temporal surveillance data, for diseases such as influenza or gastroenteritis, taking into account their seasonal pattern. We determined the key parameters of such models by reviewing the literature. A web site was developed for the users to create periodic regression models that fit their data by tuning these key parameters. Tools for model hierarchy and comparison were proposed. Thus, this website allows rapid hypothesis testing and model comparisons for implementation of a prospective surveillance, as well as retrospective assessment of epidemic burden. We then constructed and evaluated an indicator based on medication sales for the detection of gastroenteritis outbreaks. To select the most relevant therapeutic classes for this surveillance, a large database of drug sales was analysed by data mining. The constructed indicator allowed detecting with good sensitivity, specificity and timeliness the gastroenteritis epidemics signalled by the Sentinelles network, a surveillance system relying on sentinel general practitioners that based its alerts on acute diarrhoea incidence analysis. Finally, the number of queries searched online in the Google search engine, concerning three infectious diseases, was compared to clinical surveillance data from the Sentinelles network. High correlations were obtained between some queries and the incidence of influenza-like illness, acute diarrhoea and chickenpox, between 2004 and 2008. Multiple regression models based on these queries allowed accurate prediction of the incidences of these three diseases during this period. However, they gave erroneous prediction of influenza-like illness incidences during the 2009 A/H1N1 influenza pandemic.La menace du bioterrorisme, l'émergence de nouveaux pathogènes et la crainte d'une pandémie grippale ont favorisé, ces dix dernières années, la recherche de nouveaux outils et de nouvelles données pour la surveillance des maladies infectieuses. Dans cette thèse, ce problème est abordé d'une part avec des modèles statistiques pour la détection des épidémies à partir de données temporelles de surveillance (modèles de régression périodique), puis par l'évaluation de deux sources de données non cliniques (ventes de médicaments et recherches sur Internet) potentiellement intéressantes pour la surveillance des maladies infectieuses. Les modèles de régression périodique permettent la détection et la quantification des épidémies à partir de séries temporelles de surveillance, pour des maladies telles que la grippe ou la gastroentérite, où l'enjeu est d'extraire un signal en présence d'un niveau de base périodique. Nous avons déterminé les paramètres clés de ces modèles en effectuant une revue de la littérature. Une interface Internet autorisant la modification de ces paramètres clés a été construite pour permettre l'analyse de données temporelles et la comparaison de modèles. Ainsi, ce site Internet permet de tester rapidement des hypothèses d'analyse, de comparer des modèles et d'en choisir un, pour mettre en place une surveillance ou évaluer l'impact des épidémies. Nous avons ensuite construit et évalué un indicateur basé sur les ventes de médicaments pour la détection des épidémies de gastroentérite. Pour déterminer les classes thérapeutiques les plus informatives pour cette surveillance, une large base de ventes pharmaceutiques a été analysée par classification hiérarchique. L'indicateur obtenu a permis de détecter avec de très bonnes sensibilité, spécificité et rapidité, les épidémies de gastroentérite déclarées par le Réseau Sentinelles sur la base de la surveillance des diarrhées aiguës en médecine générale. Enfin, le nombre de requêtes effectuées sur le moteur de recherche Google au sujet de trois maladies infectieuses a été comparé aux données cliniques de surveillance fournies par le Réseau Sentinelles. Une corrélation élevée a été mise en évidence entre certaines requêtes et l'incidence des syndromes grippaux, des diarrhées aiguës et de la varicelle entre 2004 et 2008. Des modèles de régression multiple construits sur ces requêtes ont permis d'estimer, avec une bonne précision, les incidences de ces trois maladies sur cette période. Toutefois, ces mêmes modèles ont donné des prédictions erronées pour les syndromes grippaux durant la pandémie de grippe A/H1N1 de 2009

Blogs as a new tool of communication and promotion of fashion brands : How do fashion companies make use of bloggers as a new tool of communication to promote their brands?

Title: Blogs as a new tool of communication and promotion of fashion brands. Research question: How do fashion companies make use of bloggers as a new tool of communication to promote their brands? Purpose: The purpose of this study is to observe and explore how the fashion brands use bloggers and their blogs as a new method to advertise and communicate about them and their products to consumers. The study is conducted from an external point of view. Design/methodology/approach: This study is exploratory and descriptive and uses a qualitative method, with non-randomly method where the sample is composed of six international fashion blogs. To collect data the authors used guideline to conduct the content analysis of these selected blogs. Findings: This study shows that brands use the blogger as human being with the phenomenon of the word-of-mouth and opinion formers/leaders and they also make us of the blog content to incorporate ads to directly communicate to the online communities