BackgroundEbola and Marburg viruses cause highly lethal hemorrhagic fevers in humans. Recently, bats of multiple species have been identified as possible natural hosts of Zaire ebolavirus (ZEBOV) in Gabon and Republic of Congo, and also of marburgvirus (MARV) in Gabon and Democratic Republic of Congo.MethodsWe tested 2147 bats belonging to at least nine species sampled between 2003 and 2008 in three regions of Gabon and in the Ebola epidemic region of north Congo for IgG antibodies specific for ZEBOV and MARV.ResultsOverall, IgG antibodies to ZEBOV and MARV were found in 4% and 1% of bats, respectively. ZEBOV-specific antibodies were found in six bat species (Epomops franqueti, Hypsignathus monstrosus, Myonycteris torquata, Micropteropus pusillus, Mops condylurus and Rousettus aegyptiacus), while MARV-specific antibodies were only found in Rousettus aegyptiacus and Hypsignathus monstrosus. The prevalence of MARV-specific IgG was significantly higher in R. aegyptiacus members captured inside caves than elsewhere. No significant difference in prevalence was found according to age or gender. A higher prevalence of ZEBOV-specific IgG was found in pregnant females than in non pregnant females.ConclusionThese findings confirm that ZEBOV and MARV co-circulate in Gabon, the only country where bats infected by each virus have been found. IgG antibodies to both viruses were detected only in Rousettus aegyptiacus, suggesting that this bat species may be involved in the natural cycle of both Marburg and Ebola viruses. The presence of MARV in Gabon indicates a potential risk for a first human outbreak. Disease surveillance should be enhanced in areas near caves.

To better understand Zaire ebolavirus (ZEBOV) circulation and transmission to humans, we conducted a large serological survey of rural populations in Gabon, a country characterized by both epidemic and non epidemic regions. The survey lasted three years and covered 4,349 individuals from 220 randomly selected villages, representing 10.7% of all villages in Gabon. Using a sensitive and specific ELISA method, we found a ZEBOV-specific IgG seroprevalence of 15.3% overall, the highest ever reported. The seroprevalence rate was significantly higher in forested areas (19.4%) than in other ecosystems, namely grassland (12.4%), savannah (10.5%), and lakeland (2.7%). No other risk factors for seropositivity were found. The specificity of anti-ZEBOV IgG was confirmed by Western blot in 138 individuals, and CD8 T cells from seven IgG+ individuals were shown to produce IFN-γ after ZEBOV stimulation. Together, these findings show that a large fraction of the human population living in forested areas of Gabon has both humoral and cellular immunity to ZEBOV. In the absence of identified risk factors, the high prevalence of “immune” persons suggests a common source of human exposure such as fruits contaminated by bat saliva. These findings provide significant new insights into ZEBOV circulation and human exposure, and raise important questions as to the human pathogenicity of ZEBOV and the existence of natural protective immunization.

Abstract:In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999-2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999-2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well.