Tracking the Source of Human Q Fever from a Southern French Village: Sentinel Animals and Environmental Reservoir.
Younes LaidoudiElodie RoussetAnne-Sophie DessimoulieMyriam PrigentAlizée RaptopouloQuentin HuteauElisabeth ChabbertCatherine NavarroPierre-Edouard FournierBernard DavoustPublished in: Microorganisms (2023)
Coxiella burnetii , also known as the causal agent of Q fever, is a zoonotic pathogen infecting humans and several animal species. Here, we investigated the epidemiological context of C. burnetii from an area in the Hérault department in southern France, using the One Health paradigm. In total, 13 human cases of Q fever were diagnosed over the last three years in an area comprising four villages. Serological and molecular investigations conducted on the representative animal population, as well as wind data, indicated that some of the recent cases are likely to have originated from a sheepfold, which revealed bacterial contamination and a seroprevalence of 47.6%. However, the clear-cut origin of human cases cannot be ruled out in the absence of molecular data from the patients. Multi-spacer typing based on dual barcoding nanopore sequencing highlighted the occurrence of a new genotype of C. burnetii . In addition, the environmental contamination appeared to be widespread across a perimeter of 6 km due to local wind activity, according to the seroprevalence detected in dogs (12.6%) and horses (8.49%) in the surrounding populations. These findings were helpful in describing the extent of the exposed area and thus supporting the use of dogs and horses as valuable sentinel indicators for monitoring Q fever. The present data clearly highlighted that the epidemiological surveillance of Q fever should be reinforced and improved.
Keyphrases
- endothelial cells
- risk assessment
- human health
- electronic health record
- end stage renal disease
- induced pluripotent stem cells
- pluripotent stem cells
- chronic kidney disease
- single molecule
- drinking water
- newly diagnosed
- big data
- prognostic factors
- peritoneal dialysis
- tertiary care
- machine learning
- health risk
- high resolution