Abstract
The Netherlands experienced the largest human and veterinary Q fever epidemic ever described. From 2007 through 2010, over 4,000 human cases were notified and approximately a twelve-fold higher number was probably infected by Coxiella burnetii, the causative agent of Q fever. Dairy goat farms, and to a lesser extent dairy
... read more
sheep farms, were identified as the major source of these human infections with high Coxiella burnetii shedding rates during parturition of the animals. The epidemic curves showed a very clear seasonal pattern with peaks of human Q fever cases following the lambing and kidding season of sheep and goats. In addition, a very clear spatial pattern was visible as well: the majority of the infected farms and human patients were located/living in the same areas in the south of the country, and most human cases were spatially clustered close to the infected farms. Several published papers describing other outbreaks suggested an association between human Q fever incidence and specific meteorological and environmental conditions, such as wind speed and wind direction. With respect to the Dutch epidemic, the potential effects of meteorological and environmental conditions were confirmed by two pilot studies. The current project was based on these two pilot studies and aimed at (1) modelling the airborne dispersion of Coxiella burnetii in the environment with a focus on farm-to-human transmission, and (2) identifying environmental risk factors for the transmission of Coxiella burnetii from infected farms to humans. The main conclusions of this work include: 1) Livestock-related sources of Coxiella burnetii could be identified, even in the early stages of local outbreaks assuming an exponential incidence-distance method. 2) The distance between positive farms and the residential addresses of cases was a major predictor for Q fever incidence rates. 3) Atmospheric dispersion models - mechanistic models describing the transport of particles in the atmosphere using meteorological information (wind speed, wind direction, temperature, solar radiation, etc.) – are suitable for dispersion modelling of airborne pathogens. Modelled airborne Coxiella burnetii concentrations were a better predictor for Q fever incidence than distance alone. 4) Several variables related to transmission through re-aerosolisation from a contaminated environment – such as the sensitivity of soils to wind erosion – increased the correlation to reported Q fever incidence rates and thus probably influenced Coxiella burnetii exposure. The output tools from this study thus include a source identification model and a model to determine the time-dependent areas at risk given the location of a known source and atmospheric and environmental conditions. The latter tool could be fed with meteorological forecast data to establish predictions up to a few days ahead, or even with long-term climate scenarios. Nevertheless, we highly recommend applying our methods to other outbreak data and pathogens to better validate our findings. Also, more effort should be invested in determining time-dependent emission rates of Coxiella burnetii and defining a protocol for systematic and active surveillance (including air sampling) during future outbreaks of Coxiella burnetii or other zoonoses. This could lead to a better estimation of the public health risk of a future outbreak, and to more detailed and accurate hazard maps that could be used for spatial planning of livestock operations.
show less
