Vibrio cholerae is autochthonous to natural waters and can pose a health risk when it is consumed via untreated water or contaminated shellfish. The correlation between the occurrence of V. cholerae in Chesapeake Bay and environmental factors was investigated over a 3-year period. Water and plankton samples were collected monthly from five shore sampling sites in northern Chesapeake Bay (January 1998 to February 2000) and from research cruise stations on a north-south transect (summers of 1999 and 2000). Enrichment was used to detect culturable V. cholerae, and 21.1% (n ‫؍‬ 427) of the samples were positive. As determined by serology tests, the isolates, did not belong to serogroup O1 or O139 associated with cholera epidemics. A direct fluorescent-antibody assay was used to detect V. cholerae O1, and 23.8% (n ‫؍‬ 412) of the samples were positive. V. cholerae was more frequently detected during the warmer months and in northern Chesapeake Bay, where the salinity is lower. Statistical models successfully predicted the presence of V. cholerae as a function of water temperature and salinity. Temperatures above 19°C and salinities between 2 and 14 ppt yielded at least a fourfold increase in the number of detectable V. cholerae. The results suggest that salinity variation in Chesapeake Bay or other parameters associated with Susquehanna River inflow contribute to the variability in the occurrence of V. cholerae and that salinity is a useful indicator. Under scenarios of global climate change, increased climate variability, accompanied by higher stream flow rates and warmer temperatures, could favor conditions that increase the occurrence of V. cholerae in Chesapeake Bay.Vibrio cholerae is both the causative agent of cholera and a natural inhabitant of the aquatic environment. Nearly 200 V. cholerae serogroups have been identified to date (70), but only two serogroups, serogroups O1 and O139, are associated with epidemic cholera. V. cholerae was first isolated from the Chesapeake Bay in the 1970s and was suggested to be an autochthonous member of the aquatic environment (17). Further studies demonstrated clearly that V. cholerae is, in fact, autochthonous to the Chesapeake Bay and to the aquatic environment in general (15,18,40). V. cholerae has since been detected in natural waters worldwide, including areas where clinical cases of cholera did not exist (32,38,43,69). These studies showed that the majority of environmental isolates of V. cholerae are members of non-O1, non-O139 serogroups. However, various non-O1, non-O139 V. cholerae strains have repeatedly been isolated from patients with diarrhea (20, 59) and have shown a capacity to provoke localized diarrheal outbreaks (2,19,53, 56).Colwell (15) proposed that the natural aquatic environment serves as the reservoir for V. cholerae and that it may play a critical role in pandemics of cholera. Horizontal gene transfer, which has been demonstrated in V. cholerae by Waldor and Mekalanos (66), has been proposed as a mechanism for the emergence of new pathogenic strain...

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IntroductionThe global spread and the increased frequency and magnitude of epidemic dengue in the last 50 years underscore the urgent need for effective tools for surveillance, prevention, and control. This review aims at providing a systematic overview of what predictors are critical and which spatial and spatio-temporal modeling approaches are useful in generating risk maps for dengue.MethodsA systematic search was undertaken, using the PubMed, Web of Science, WHOLIS, Centers for Disease Control and Prevention (CDC) and OvidSP databases for published citations, without language or time restrictions. A manual search of the titles and abstracts was carried out using predefined criteria, notably the inclusion of dengue cases. Data were extracted for pre-identified variables, including the type of predictors and the type of modeling approach used for risk mapping.ResultsA wide variety of both predictors and modeling approaches was used to create dengue risk maps. No specific patterns could be identified in the combination of predictors or models across studies. The most important and commonly used predictors for the category of demographic and socio-economic variables were age, gender, education, housing conditions and level of income. Among environmental variables, precipitation and air temperature were often significant predictors. Remote sensing provided a source of varied land cover data that could act as a proxy for other predictor categories. Descriptive maps showing dengue case hotspots were useful for identifying high-risk areas. Predictive maps based on more complex methodology facilitated advanced data analysis and visualization, but their applicability in public health contexts remains to be established.ConclusionsThe majority of available dengue risk maps was descriptive and based on retrospective data. Availability of resources, feasibility of acquisition, quality of data, alongside available technical expertise, determines the accuracy of dengue risk maps and their applicability to the field of public health. A large number of unknowns, including effective entomological predictors, genetic diversity of circulating viruses, population serological profile, and human mobility, continue to pose challenges and to limit the ability to produce accurate and effective risk maps, and fail to support the development of early warning systems.Electronic supplementary materialThe online version of this article (doi:10.1186/1476-072X-13-50) contains supplementary material, which is available to authorized users.