Developing Global Maps of the Dominant Anopheles Vectors of Human Malaria

Simon Hay and colleagues describe how the Malaria Atlas Project has collated anopheline occurrence data to map the geographic distributions of the dominant mosquito vectors of human malaria

The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis

<p>Abstract</p> <p>Background</p> <p>This is the second in a series of three articles documenting the geographical distribution of 41 dominant vector species (DVS) of human malaria. The first paper addressed the DVS of the Americas and the third will consider those of the Asian Pacific Region. Here, the DVS of Africa, Europe and the Middle East are discussed. The continent of Africa experiences the bulk of the global malaria burden due in part to the presence of the <it>An. gambiae </it>complex. <it>Anopheles gambiae </it>is one of four DVS within the <it>An. gambiae </it>complex, the others being <it>An. arabiensis </it>and the coastal <it>An. merus </it>and <it>An. melas</it>. There are a further three, highly anthropophilic DVS in Africa, <it>An. funestus</it>, <it>An. moucheti </it>and <it>An. nili</it>. Conversely, across Europe and the Middle East, malaria transmission is low and frequently absent, despite the presence of six DVS. To help control malaria in Africa and the Middle East, or to identify the risk of its re-emergence in Europe, the contemporary distribution and bionomics of the relevant DVS are needed.</p> <p>Results</p> <p>A contemporary database of occurrence data, compiled from the formal literature and other relevant resources, resulted in the collation of information for seven DVS from 44 countries in Africa containing 4234 geo-referenced, independent sites. In Europe and the Middle East, six DVS were identified from 2784 geo-referenced sites across 49 countries. These occurrence data were combined with expert opinion ranges and a suite of environmental and climatic variables of relevance to anopheline ecology to produce predictive distribution maps using the Boosted Regression Tree (BRT) method.</p> <p>Conclusions</p> <p>The predicted geographic extent for the following DVS (or species/suspected species complex*) is provided for Africa: <it>Anopheles </it>(<it>Cellia</it>) <it>arabiensis</it>, <it>An. </it>(<it>Cel.</it>) <it>funestus*</it>, <it>An. </it>(<it>Cel.</it>) <it>gambiae</it>, <it>An. </it>(<it>Cel.</it>) <it>melas</it>, <it>An. </it>(<it>Cel.</it>) <it>merus</it>, <it>An. </it>(<it>Cel.</it>) <it>moucheti </it>and <it>An. </it>(<it>Cel.</it>) <it>nili*</it>, and in the European and Middle Eastern Region: <it>An. </it>(<it>Anopheles</it>) <it>atroparvus</it>, <it>An. </it>(<it>Ano.</it>) <it>labranchiae</it>, <it>An. </it>(<it>Ano.</it>) <it>messeae</it>, <it>An. </it>(<it>Ano.</it>) <it>sacharovi</it>, <it>An. </it>(<it>Cel.</it>) <it>sergentii </it>and <it>An. </it>(<it>Cel.</it>) <it>superpictus*</it>. These maps are presented alongside a bionomics summary for each species relevant to its control.</p

Distribution of the main malaria vectors in Kenya

Background: A detailed knowledge of the distribution of the main Anopheles malaria vectors in Kenya should guide national vector control strategies. However, contemporary spatial distributions of the locally dominant Anopheles vectors including Anopheles gambiae, Anopheles arabiensis, Anopheles merus, Anopheles funestus, Anopheles pharoensis and Anopheles nili are lacking. The methods and approaches used to assemble contemporary available data on the present distribution of the dominant malaria vectors in Kenya are presented here. Method: Primary empirical data from published and unpublished sources were identified for the period 1990 to 2009. Details recorded for each source included the first author, year of publication, report type, survey location name, month and year of survey, the main Anopheles species reported as present and the sampling and identification methods used. Survey locations were geo-positioned using national digital place name archives and on-line geo-referencing resources. The geo-located species-presence data were displayed and described administratively, using first-level administrative units (province), and biologically, based on the predicted spatial margins of Plasmodium falciparum transmission intensity in Kenya for the year 2009. Each geo-located survey site was assigned an urban or rural classification and attributed an altitude value. Results: A total of 498 spatially unique descriptions of Anopheles vector species across Kenya sampled between 1990 and 2009 were identified, 53% were obtained from published sources and further communications with authors. More than half (54%) of the sites surveyed were investigated since 2005. A total of 174 sites reported the presence of An. gambiae complex without identification of sibling species. Anopheles arabiensis and An. funestus were the most widely reported at 244 and 265 spatially unique sites respectively with the former showing the most ubiquitous distribution nationally. Anopheles gambiae, An. arabiensis, An. funestus, An. pharoensis were reported at sites located in all the transmission intensity classes with more reports of An. gambiae in the highest transmission intensity areas than the very low transmission ares. Conclusion: A contemporary, spatially defined database of the main malaria vectors in Kenya provides a baseline for future compilations of data and helps identify areas where information is currently lacking. The data collated here are published alongside this paper where it may help future sampling location decisions, help with with planning of vector control suites nationally and encourage broader research inquiry into vector species niche modeling

Distribution of the main malaria vectors in Kenya

Background: A detailed knowledge of the distribution of the main Anopheles malaria vectors in Kenya should guide national vector control strategies. However, contemporary spatial distributions of the locally dominant Anopheles vectors including Anopheles gambiae, Anopheles arabiensis, Anopheles merus, Anopheles funestus, Anopheles pharoensis and Anopheles nili are lacking. The methods and approaches used to assemble contemporary available data on the present distribution of the dominant malaria vectors in Kenya are presented here. Method: Primary empirical data from published and unpublished sources were identified for the period 1990 to 2009. Details recorded for each source included the first author, year of publication, report type, survey location name, month and year of survey, the main Anopheles species reported as present and the sampling and identification methods used. Survey locations were geo-positioned using national digital place name archives and on-line geo-referencing resources. The geo-located species-presence data were displayed and described administratively, using first-level administrative units (province), and biologically, based on the predicted spatial margins of Plasmodium falciparum transmission intensity in Kenya for the year 2009. Each geo-located survey site was assigned an urban or rural classification and attributed an altitude value. Results: A total of 498 spatially unique descriptions of Anopheles vector species across Kenya sampled between 1990 and 2009 were identified, 53% were obtained from published sources and further communications with authors. More than half (54%) of the sites surveyed were investigated since 2005. A total of 174 sites reported the presence of An. gambiae complex without identification of sibling species. Anopheles arabiensis and An. funestus were the most widely reported at 244 and 265 spatially unique sites respectively with the former showing the most ubiquitous distribution nationally. Anopheles gambiae, An. arabiensis, An. funestus, An. pharoensis were reported at sites located in all the transmission intensity classes with more reports of An. gambiae in the highest transmission intensity areas than the very low transmission ares. Conclusion: A contemporary, spatially defined database of the main malaria vectors in Kenya provides a baseline for future compilations of data and helps identify areas where information is currently lacking. The data collated here are published alongside this paper where it may help future sampling location decisions, help with with planning of vector control suites nationally and encourage broader research inquiry into vector species niche modeling