After an outbreak of Chikungunya infection in Emilia-Romagna Region (North-eastern Italy), a survey was performed to estimate the seroprevalence of antibody to Chikungunya virus and the proportion of asymptomatic infections, to identify factors associated with infection, and evaluate the performance of the surveillance system. The method used was a survey on a random sample of residents of the village with the largest number of reported cases. The prevalence was 10.2% (33 of 325), being higher in older people and males, and lower when window screens and insect repellents were used. Only 18% of infected persons were fully asymptomatic, 85% of the 27 symptomatic confirmed cases satisfied the surveillance case definition, and 63% of the persons meeting the criteria for suspect case were identified by the active surveillance system. This study provides basic parameters for modeling the transmission potential of outbreaks and planning control measures for Chikungunya infection in temperate settings.

BackgroundIn the third season of I-MOVE (Influenza Monitoring Vaccine Effectiveness in Europe), we undertook a multicentre case-control study based on sentinel practitioner surveillance networks in eight European Union (EU) member states to estimate 2010/11 influenza vaccine effectiveness (VE) against medically-attended influenza-like illness (ILI) laboratory-confirmed as influenza.MethodsUsing systematic sampling, practitioners swabbed ILI/ARI patients within seven days of symptom onset. We compared influenza-positive to influenza laboratory-negative patients among those meeting the EU ILI case definition. A valid vaccination corresponded to > 14 days between receiving a dose of vaccine and symptom onset. We used multiple imputation with chained equations to estimate missing values. Using logistic regression with study as fixed effect we calculated influenza VE adjusting for potential confounders. We estimated influenza VE overall, by influenza type, age group and among the target group for vaccination.ResultsWe included 2019 cases and 2391 controls in the analysis. Adjusted VE was 52% (95% CI 30-67) overall (N = 4410), 55% (95% CI 29-72) against A(H1N1) and 50% (95% CI 14-71) against influenza B. Adjusted VE against all influenza subtypes was 66% (95% CI 15-86), 41% (95% CI -3-66) and 60% (95% CI 17-81) among those aged 0-14, 15-59 and ≥60 respectively. Among target groups for vaccination (N = 1004), VE was 56% (95% CI 34-71) overall, 59% (95% CI 32-75) against A(H1N1) and 63% (95% CI 31-81) against influenza B.ConclusionsResults suggest moderate protection from 2010-11 trivalent influenza vaccines against medically-attended ILI laboratory-confirmed as influenza across Europe. Adjusted and stratified influenza VE estimates are possible with the large sample size of this multi-centre case-control. I-MOVE shows how a network can provide precise summary VE measures across Europe.

BackgroundThe ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.Methods and FindingsOver 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola (“cases”) were asked if they had exposure to other potential Ebola cases (“potential source contacts”) in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO’s response during the epidemic, and have been updated for publication.We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = −0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible mis...

To estimate the burden and cost of chikungunya in India, we searched for cases of fever and joint pain in the village of Mallela, Andhra Pradesh, and collected information on the demography, signs, symptoms, healthcare utilization and expenditure associated with the disease. We estimated the burden of the disease using disability-adjusted life years (DALYs). We estimated direct and indirect costs and made projections for the district and state using surveillance data corrected for under-reporting. On average, from December 2005 to April 2006, each of the 242 cases in the village led to a burden of 0.0272 DALYs (95% CI 0.0224-0.0319) and a cost of US$37.50 (95% CI 30.6-44.3). Overall, chikungunya in Mallela led to 6.57 DALYs and a loss of US$9100. Out-of-pocket direct medical costs accounted for 68% of the total. From January to December 2006 the burden for Kadapa district was 160 DALYs (cost: US$290 000). Over the same period the burden for Andhra Pradesh was 6600 DALYs (cost: US$12 400 000). While the burden was moderate, costs were high and mostly out of pocket.