BackgroundBuruli ulcer disease (BU), due to the bacteria Mycobacterium ulcerans, represents an important and emerging public health problem, especially in many African countries. Few elements are known nowadays about the routes of transmission of this environmental bacterium to the human population.Methodology/Principal FindingsIn this study, we have investigated the relationships between the incidence of BU in Côte d'Ivoire, western Africa, and a group of environmental variables. These environmental variables concern vegetation, crops (rice and banana), dams, and lakes. Using a geographical information system and multivariate analyses, we show a link between cases of BU and different environmental factors for the first time on a country-wide scale. As a result, irrigated rice field cultures areas, and, to a lesser extent, banana fields as well as areas in the vicinity of dams used for irrigation and aquaculture purposes, represent high-risk zones for the human population to contract BU in Côte d'Ivoire. This is much more relevant in the central part of the country.Conclusions/SignificanceAs already suspected by several case-control studies in different African countries, we strengthen in this work the identification of high-risk areas of BU on a national spatial scale. This first study should now be followed by many others in other countries and at a multi-year temporal scale. This goal implies a strong improvement in data collection and sharing in order to achieve to a global picture of the environmental conditions that drive BU emergence and persistence in human populations.
BackgroundThe use of a malaria early warning system (MEWS) to trigger prompt public health interventions is a key step in adding value to the epidemiological data routinely collected by sentinel surveillance systems.MethodsThis study describes a system using various epidemic thresholds and a forecasting component with the support of new technologies to improve the performance of a sentinel MEWS. Malaria-related data from 21 sentinel sites collected by Short Message Service are automatically analysed to detect malaria trends and malaria outbreak alerts with automated feedback reports.ResultsRoll Back Malaria partners can, through a user-friendly web-based tool, visualize potential outbreaks and generate a forecasting model. The system already demonstrated its ability to detect malaria outbreaks in Madagascar in 2014.ConclusionThis approach aims to maximize the usefulness of a sentinel surveillance system to predict and detect epidemics in limited-resource environments.
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