Toward an Open-Access Global Database for Mapping, Control, and Surveillance of Neglected Tropical Diseases

Abstract Background: After many years of general neglect, interest has grown and efforts came under way for the mapping, control, surveillance, and eventual elimination of neglected tropical diseases (NTDs). Disease risk estimates are a key feature to target control interventions, and serve as a benchmark for monitoring and evaluation. What is currently missing is a georeferenced global database for NTDs providing open-access to the available survey data that is constantly updated and can be utilized by researchers and disease control managers to support other relevant stakeholders. We describe the steps taken toward the development of such a database that can be employed for spatial disease risk modeling and control of NTDs

Toward an Open-Access Global Database for Mapping, Control, and Surveillance of Neglected Tropical Diseases

There is growing interest in the scientific community, health ministries, and other organizations to control and eventually eliminate neglected tropical diseases (NTDs). Control efforts require reliable maps of NTD distribution estimated from appropriate models and survey data on the number of infected people among those examined at a given location. This kind of data is often available in the literature as part of epidemiological studies. However, an open-access database compiling location-specific survey data does not yet exist. We address this problem through a systematic literature review, along with contacting ministries of health, and research institutions to obtain disease data, including details on diagnostic techniques, demographic characteristics of the surveyed individuals, and geographical coordinates. All data were entered into a database which is freely accessible via the Internet (http://www.gntd.org). In contrast to similar efforts of the Global Atlas of Helminth Infections (GAHI) project, the survey data are not only displayed in form of maps but all information can be browsed, based on different search criteria, and downloaded as Excel files for further analyses. At the beginning of 2011, the database included over 12,000 survey locations for schistosomiasis across Africa, and it is continuously updated to cover other NTDs globally

Collection, verification, sharing and dissemination of data : the CONTRAST experience

The scientific community is charged with growing demands regarding the management of project data and outputs and the dissemination of key results to various stakeholders. We discuss experiences and lessons from CONTRAST, a multidisciplinary alliance that had been funded by the European Commission over a 4-year period, in order to optimize schistosomiasis control and transmission surveillance in sub-Saharan Africa. From the start, project partners from Europe and Africa set out an ambitious goal: to sample data following standard protocols at all field sites and then sharing the data in a way that would enable all project partners to have access through a password-protected Internet-based data portal. This required anonymous agreement on several common standardized sample forms, ranging from the mundane but important issue of using the same units of measurement to more complex challenges, for instance agreeing on the same protocols for double-treatment of praziquantel in different settings. With the experiences gained by the CONTRAST project, this paper discusses issues of data management and sharing in research projects in the light of the current donor demand, and offers advice and specific suggestions for similar interdisciplinary research projects

Large-scale determinants of intestinal schistosomiasis and intermediate host snail distribution across Africa : does climate matter?

The geographical ranges of most species, including many infectious disease agents and their vectors and intermediate hosts, are assumed to be constrained by climatic tolerances, mainly temperature. It has been suggested that global warming will cause an expansion of the areas potentially suitable for infectious disease transmission. However, the transmission of infectious diseases is governed by a myriad of ecological, economic, evolutionary and social factors. Hence, a deeper understanding of the total disease system (pathogens, vectors and hosts) and its drivers is important for predicting responses to climate change. Here, we combine a growing degree day model for Schistosoma mansoni with species distribution models for the intermediate host snail (Biomphalaria spp.) to investigate large-scale environmental determinants of the distribution of the African S. mansoni-Biomphalaria system and potential impacts of climatic changes. Snail species distribution models included several combinations of climatic and habitat-related predictors; the latter divided into "natural" and "human-impacted" habitat variables to measure anthropogenic influence. The predictive performance of the combined snail-parasite model was evaluated against a comprehensive compilation of historical S. mansoni parasitological survey records, and then examined for two climate change scenarios of increasing severity for 2080. Future projections indicate that while the potential S. mansoni transmission area expands, the snail ranges are more likely to contract and/or move into cooler areas in the south and east. Importantly, we also note that even though climate per se matters, the impact of humans on habitat play a crucial role in determining the distribution of the intermediate host snails in Africa. Thus, a future contraction in the geographical range size of the intermediate host snails caused by climatic changes does not necessarily translate into a decrease or zero-sum change in human schistosomiasis prevalence

Flow-chart showing the steps used to assemble the GNTD database.

<p>1. PubMed <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001404#pntd.0001404-PubMed1" target="_blank">[24]</a>, ISI Web of Knowledge <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001404#pntd.0001404-ISI1" target="_blank">[25]</a>, African Journal Online (AJOL) <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001404#pntd.0001404-African1" target="_blank">[26]</a>, Institut de Recherche pour le Développement (IRD)-resources documentaries <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001404#pntd.0001404-IRD1" target="_blank">[28]</a>, WHO library archive <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001404#pntd.0001404-WHO2" target="_blank">[27]</a>, Doumenge et al. <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001404#pntd.0001404-Doumenge1" target="_blank">[17]</a>; 2. Dissertations and theses in local universities or public health departments, ministry of health reports, other reports and personal communication. 3. Proforma and MySQL database include: (i) data source (authors); (ii) document type; (iii) location of the survey; (iv) area information (rural or urban); (v) coordinates (lat long in decimal degrees); (vi) method of the sample recruitment and diagnostic technique; (vii) description of survey (community-, school- or hospital-based); (viii) date of survey (month/year); and (ix) prevalence information (number of subjects examined and positive by age group and parasite species).</p

Observed prevalence of <i>S. haematobium</i> based on current progress of the GNTD database in Africa.

<p>The data included 5807 georeferenced survey locations. Prevalence equal to 0%, low infection rates (0.1–9.9%), moderate infection rates (10.0–49.9%) and high infection rates (≥50%) indicated by a red scale from light red to dark red. Cut-offs follow WHO recommendations <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001404#pntd.0001404-WHO3" target="_blank">[35]</a>.</p

African map of schistosomiasis survey locations based on current progress of the GNTD database.

<p>Survey locations are represented by pink squares for <i>S. matthei</i>, blue diamonds for <i>S. margrebowiei</i>, yellow stars for <i>S. intercalatum</i>, green crosses for <i>S. bovis</i>, brown dots for <i>S. mansoni</i> and red triangles for <i>S. haematobium</i>. Surveys where subjects were screened for co-occurrence of multiple species are indicated with overlapping symbols.</p

Observed prevalence of <i>S. mansoni</i> based on current progress of the GNTD database in Africa.

<p>The data included 4604 georeferenced survey locations. Prevalence equal to 0% in yellow dots, low infection rates (0.1–9.9%) in orange dots, moderate infection rates (10.0–49.9%) in light brown dots and high infection rates (≥50%) in brown dots. Cut-offs follow WHO recommendations <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001404#pntd.0001404-WHO3" target="_blank">[35]</a>.</p

Number of <i>Schistosoma</i> spp. survey locations in the GNTD database in Africa stratified by country.

<p>Number of survey locations of <i>S. mansoni</i>, <i>S. haematobium</i>, <i>S. intercalatum</i>, <i>S. bovis</i>, <i>S. matthei</i> and <i>S. margrebowiei</i> as of 10 January 2011.</p