Animal leptospirosis in small tropical areas

Leptospirosis is the most widespread zoonosis in the world. Humans become infected through contact with the urine of carrier animals, directly or via contaminated environments. This review reports available data on animal leptospirosis in ten tropical islands: Barbados, Martinique, Guadeloupe, Grenada, Trinidad, New Caledonia, Hawaii, French Polynesia, La Re´union and Mayotte. Leptospirosis is endemic in these insular wild and domestic fauna. Each island presents a specific panel of circulating serovars, closely linked with animal and environmental biodiversity, making it epidemiologically different from the mainland. Rats, mongooses and mice are proven major renal carriers of leptospires in these areas but dogs also constitute a significant potential reservoir. In some islands seroprevalence of leptospirosis in animals evolves with time, inducing changes in the epidemiology of the human disease. Consequently more investigations on animal leptospirosis in these ecosystems and use of molecular tools are essential for prevention and control of the human disease. (Résumé d'auteur

Seasonality of Human Leptospirosis in Reunion Island (Indian Ocean) and Its Association with Meteorological Data

Background: Leptospirosis is a disease which occurs worldwide but particularly affects tropical areas. Transmission of the disease is dependent on its excretion by reservoir animals and the presence of moist environment which allows the survival of the bacteria. Methods and Findings: A retrospective study was undertaken to describe seasonal patterns of human leptospirosis cases reported by the Centre National de Re´fe´rences des Leptospiroses (CNRL, Pasteur Institute, Paris) between 1998 and 2008, to determine if there was an association between the occurrence of diagnosed cases and rainfall, temperature and global solar radiation (GSR). Meteorological data were recorded in the town of Saint-Beno?¿t (Me´te´o France ''Beaufonds-Miria'' station), located on the windward (East) coast. Time-series analysis was used to identify the variables that best described and predicted the occurrence of cases of leptospirosis on the island. Six hundred and thirteen cases were reported during the 11-year study period, and 359 cases (58.56%) were diagnosed between February and May. A significant correlation was identified between the number of cases in a given month and the associated cumulated rainfall as well as the mean monthly temperature recorded 2 months prior to diagnosis (r = 0.28 and r = 0.23 respectively). The predictive model includes the number of cases of leptospirosis recorded 1 month prior to diagnosis (b = 0.193), the cumulated monthly rainfall recorded 2 months prior to diagnosis (b = 0.145), the average monthly temperature recorded 0 month prior to diagnosis (b = 3.836), and the average monthly GSR recorded 0 month prior to diagnosis (b =21.293). Conclusions: Leptospirosis has a seasonal distribution in Reunion Island. Meteorological data can be used to predict the occurrence of the disease and our statistical model can help to implement seasonal prevention measures. (Résumé d'auteur

Delineation of the population genetic structure of Culicoides imicola in East and South Africa

BACKGROUND: Culicoides imicola Kieffer, 1913 is the main vector of bluetongue virus (BTV) and African horse sickness virus (AHSV) in Sub-Saharan Africa. Understanding the population genetic structure of this midge and the nature of barriers to gene flow will lead to a deeper understanding of bluetongue epidemiology and more effective vector control in this region. METHODS: A panel of 12 DNA microsatellite markers isolated de novo and mitochondrial DNA were utilized in a study of C. imicola populations from Africa and an outlier population from the Balearic Islands. The DNA microsatellite markers and mitochondrial DNA were also used to examine a population of closely related C. bolitinos Meiswinkel midges. RESULTS: The microsatellite data suggest gene flow between Kenya and south-west Indian Ocean Islands exist while a restricted gene flow between Kenya and South Africa C. imicola populations occurs. Genetic distance correlated with geographic distance by Mantel test. The mitochondrial DNA analysis results imply that the C. imicola populations from Kenya and south-west Indian Ocean Islands (Madagascar and Mauritius) shared haplotypes while C. imicola population from South Africa possessed private haplotypes and the highest nucleotide diversity among the African populations. The Bayesian skyline plot suggested a population growth. CONCLUSIONS: The gene flow demonstrated by this study indicates a potential risk of introduction of new BTV serotypes by wind-borne infected Culicoides into the Islands. Genetic similarity between Mauritius and South Africa may be due to translocation as a result of human-induced activities; this could impact negatively on the livestock industry. The microsatellite markers isolated in this study may be utilised to study C. bolitinos, an important vector of BTV and AHSV in Africa and identify sources of future incursions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13071-015-1277-4) contains supplementary material, which is available to authorized users

Knowledge on leptospirosis in wild and domestic fauna in small tropical islands: a review.

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High-risk regions and outbreak modelling of tularemia in humans

Sweden reports large and variable numbers of human tularemia cases, but the high-risk regions are anecdotally defined and factors explaining annual variations are poorly understood. Here, high-risk regions were identified by spatial cluster analysis on disease surveillance data for 1984-2012. Negative binomial regression with five previously validated predictors (including predicted mosquito abundance and predictors based on local weather data) was used to model the annual number of tularemia cases within the high-risk regions. Seven high-risk regions were identified with annual incidences of 3.8-44 cases/100 000 inhabitants, accounting for 56.4% of the tularemia cases but only 9.3% of Sweden's population. For all high-risk regions, most cases occurred between July and September. The regression models explained the annual variation of tularemia cases within most high-risk regions and discriminated between years with and without outbreaks. In conclusion, tularemia in Sweden is concentrated in a few high-risk regions and shows high annual and seasonal variations. We present reproducible methods for identifying tularemia high-risk regions and modelling tularemia cases within these regions. The results may help health authorities to target populations at risk and lay the foundation for developing an early warning system for outbreaks.</p