NTR3386.

Coxiella burnetii seroprevalence was assessed on Dutch dairy and non-dairy sheep farms using ELISA. Risk factors for seropositivity on non-dairy sheep farms were identified at farm and sheep level by univariate and multivariate multilevel analyses. Based on 953 dairy and 5671 non-dairy serum samples, sheep seroprevalences were 18.7 per cent and 2.0 per cent, respectively, and 78.6 per cent and 30.5 per cent at farm level. Significant risk factors for non-dairy sheep farms were farm location in the south of the country, sheep kept on marginal grounds, one or several supply addresses for ewes during 2007-2009 and wearing farm boots and/or outfit by professional visitors. On sheep level, risk factors included among others farm location in the south of the country, lamb breeding as main farm purpose, goat density within 10 km farm radius, use of windbreak curtain or windshields, and presence of ≥6 stillborn lambs in 2009. Farm location in the south of the country and goat density suggests that infected goats have played a role in the transmission to non-dairy sheep. Other risk factors suggest introduction of the bacterium through sheep supply and professional visitors. Biosecurity measures should be strengthened, including avoiding infection during handling of stillborn lambs and birth products in the lambing period.

SUMMARYIn this study, Coxiella burnetii seroprevalence was assessed for dairy and non-dairy sheep farm residents in The Netherlands for 2009–2010. Risk factors for seropositivity were identified for non-dairy sheep farm residents. Participants completed farm-based and individual questionnaires. In addition, participants were tested for IgG and IgM C. burnetii antibodies using immunofluorescent assay. Risk factors were identified by univariate, multivariate logistic regression, and multivariate multilevel analyses. In dairy and non-dairy sheep farm residents, seroprevalence was 66·7% and 51·3%, respectively. Significant risk factors were cattle contact, high goat density near the farm, sheep supplied from two provinces, high frequency of refreshing stable bedding, farm started before 1990 and presence of the Blessumer breed. Most risk factors indicate current or past goat and cattle exposure, with limited factors involving sheep. Subtyping human, cattle, goat, and sheep C. burnetii strains might elucidate their role in the infection risk of sheep farm residents.

Since the 2009 influenza pandemic, the Netherlands has used a weekly death monitoring system to estimate deaths in excess of expectations. We present estimates of excess deaths during the ongoing coronavirus disease (COVID-19) epidemic and 10 previous influenza epidemics. Excess deaths per influenza epidemic averaged 4,000. The estimated 9,554 excess deaths (41% in excess) during the COVID-19 epidemic weeks 12–19 of 2020 appeared comparable to the 9,373 excess deaths (18%) during the severe influenza epidemic of 2017–18. However, these deaths occurred in a shorter time, had a higher peak, and were mitigated by nonpharmaceutical control measures. Excess deaths were 1.8-fold higher than reported laboratory-confirmed COVID-19 deaths (5,449). Based on excess deaths and preliminary results from seroepidemiologic studies, we estimated the infection-fatality rate to be 1%. Monitoring of excess deaths is crucial for timely estimates of disease burden for influenza and COVID-19. Our data complement laboratory-confirmed COVID-19 death reports and enable comparisons between epidemics.

BackgroundDuring the 2009 influenza pandemic period, routine surveillance of influenza-like-illness (ILI) was conducted in The Netherlands by a network of sentinel general practitioners (GPs). In addition during the pandemic period, four other ILI/influenza surveillance systems existed. For pandemic preparedness, we evaluated the performance of the sentinel system and the others to assess which of the four could be useful additions in the future. We also assessed whether performance of the five systems was influenced by media reports during the pandemic period.MethodsThe trends in ILI consultation rates reported by sentinel GPs from 20 April 2009 through 3 January 2010 were compared with trends in data from the other systems: ILI cases self-reported through the web-based Great Influenza Survey (GIS); influenza-related web searches through Google Flu Trends (GFT); patients admitted to hospital with laboratory-confirmed pandemic influenza, and detections of influenza virus by laboratories. In addition, correlations were determined between ILI consultation rates of the sentinel GPs and data from the four other systems. We also compared the trends of the five surveillance systems with trends in pandemic-related newspaper and television coverage and determined correlation coefficients with and without time lags.ResultsThe four other systems showed similar trends and had strong correlations with the ILI consultation rates reported by sentinel GPs. The number of influenza virus detections was the only system to register a summer peak. Increases in the number of newspaper articles and television broadcasts did not precede increases in activity among the five surveillance systems.ConclusionsThe sentinel general practice network should remain the basis of influenza surveillance, as it integrates epidemiological and virological information and was able to maintain stability and continuity under pandemic pressure. Hospital and virological data are important during a pandemic, tracking the severity, molecular and phenotypic characterization of the viruses and confirming whether ILI incidence is truly related to influenza virus infections. GIS showed that web-based, self-reported ILI can be a useful addition, especially if virological self-sampling is added and an epidemic threshold could be determined. GFT showed negligible added value.