Citation: Hay SI, Sinka ME, Okara RM, Kabaria CW, Mbithi PM, Tago CC, et al. (2010) Developing Global Maps of the Dominant Anopheles Vectors of Human Malaria. PLoS Med 7(2): e1000209. doi:10.1371/journal.pmed.1000209
Published: February 9, 2010
Copyright: © 2010 Hay et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: SIH is funded by a Senior Research Fellowship from the Wellcome Trust (#079091) which also supports PWG, APP, and WHT. MES, CWK, PMM, CCT, and REH are funded by a Wellcome Trust project grant (#083534) to SIH. RMO is funded by a Wellcome Trust Masters Training Fellowship (#083124). This work forms part of the output of the Malaria Atlas Project (MAP, http://www.map.ox.ac.uk), principally funded by the Wellcome Trust, U.K. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: DVS, dominant vector species; MAP, Malaria Atlas Project; MODIS, Moderate Resolution Imaging Spectroradiometer
Provenance: Not commissioned; externally peer-reviewed.
Despite advances in mapping the geographical distribution and intensity of malaria transmission ,, the ability to provide strategic, evidence-based advice for malaria control programmes remains constrained by the lack of range maps of the dominant Anopheles vectors of human malaria. This is because appropriate vector control depends on knowing both the distribution and epidemiological significance of Anopheles vectors . Substantial investments by major donors in the distribution of long-lasting insecticide-treated nets and indoor residual spraying campaigns  are, therefore, not always fully informed by the basic biology of local anophelines.
Recent attempts to delineate Anopheles distributions have been conducted in Africa –, the Americas –, Europe , Central and South East Asia –, and at the global scale –. The mapping techniques used in these various studies range from those based on expert opinion and simple interpolations to those employing more sophisticated statistical methods. Consequently, these studies are difficult to compare and impossible to synthesize globally. In addition, whereas in some regions Anopheles species distributions and their contribution to human malaria transmission are well known, uncertainty arises when suites of vectors contribute to local transmission, when the margins of the species ranges are poorly defined, and/or when there is simply a lack of any, or reliably identified, distribution records. Furthermore, as many regions attempt to maintain their malaria-free status against imported malaria  and others consider their prospects of malaria elimination ,, contemporary maps of anophelines that are competent vectors for malaria are important in assessing local receptivity to reintroduction .
To help address these needs, the Malaria Atlas Project (MAP, http://www.map.ox.ac.uk)  has extended its activities to collate anopheline occurrence data to map the contemporary geographic distributions of the dominant mosquito vectors of human malaria. The plans for, and progress of, this initiative are described here.
Defining the Dominant Anopheles Vectors of Human Malaria
There are 462 formally named Anopheles species, with a further 50 provisionally designated and awaiting description –. Of these, approximately 70 have been shown to be competent vectors of human malaria  and from this set, 52 candidate dominant vector species (DVS) were initially chosen for inclusion in the MAP vector distribution mapping project. These DVS are species (or species complexes) that transmit the majority of human malaria parasites in an area by virtue of their abundance, their propensity for feeding on humans, their mean adult longevity (only old individuals incubate the parasite long enough to transmit the disease), or any combination of these and other factors that increase overall vectorial capacity . The DVS were the inclusive set of those species identified as “main” ,, “dominant” , or “principal” , in major reviews of Anopheles distribution and biology. The list was then further refined by anopheline experts from the Americas, Europe, Africa, Asia, and the Pacific, who co-author this article, to exclude 11 species that were not considered important vectors either because few recent data had implicated them in transmission or because they acted as vectors in only restricted geographical areas (Text S1). Following the convention of the major reviews in this area –,,, the DVS of the Anopheles (Cellia) gambiae complex are listed separately. We hope also to map at species level three other complexes, where examination of the primary literature has indicated sufficient species-specific data (the An. (Nyssorhynchus) albitarsis, An. (Cellia) culicifacies, and An. (Cellia) dirus complexes). Further details are provided in the legend of the maps of each complex in Text S3 (for the An. (Nyssorhynchus) albitarsis complex) and Text S5 (for the An. (Cellia) culicifacies and An. (Cellia) dirus complexes).
Comprehensive Literature Searches
An exhaustive and systematic search of formal and informal literature was conducted, mirroring the approaches developed by the MAP in building a global database of malaria parasite prevalence . Only information collected after 31 December 1984 was searched. This criterion ensured that the data collected were representative of the contemporary distribution of the DVS and that the DVS occurrence records included only data collected using modern taxonomic species concepts ,. Following the introduction of cytological and then molecular methods to mosquito systematics, the taxonomy of the Anopheles changed radically, making many earlier species determinations potentially unreliable ,,–. This date restriction also served to focus finite literature retrieval and abstracting resources on newer references, that are easier to retrieve from libraries, have sites that are less problematic to geo-position, and have authors that can often still be contacted with queries.
Records of the presence or absence of a DVS at a particular site and on a particular date were entered into the database so that information collected at different times from a locality was documented. Because abundance data have not been reported using methods that can be readily standardized across entomological surveys, only presence and absence data were used to generate the maps. Although the geographic distribution of the DVS in malaria-endemic countries is the first concern, data from any location was recorded because, as previously noted, information on DVS distribution is of major importance in those areas seeking to maintain their malaria-free status. Moreover, when modelling the fundamental niche of a species  using climate-envelope approaches , the aim is to be inclusive geographically, in an attempt to fully represent the environmental limits encompassed by its range.
Once a relevant literature source was identified, information was extracted using a list of data fields specified by a detailed pro forma (Text S2). Precise geo-positioning was conducted using established methods , so that any uncertainty associated with the positioning could be estimated –. Our strategy has been to first target the formally published literature and to use this base to direct further searches for informal (“grey”) literature sources and unpublished information held by relevant individuals and organisations. The results of this exercise were a total of 41,518 records with 22,249 spatially unique observations for all 41 DVS. These records are shown in full in a series of maps in Text S3, Text S4, and Text S5 for the American, Europe Africa, and Middle East and Asia Pacific region species, respectively. Short legends are included with each map indicating areas for which occurrence records are not well documented in the formal literature by comparison with digitised expert opinion distributions for each species. Informal searches are to be focussed on these areas of poor coverage and, where not prohibited by taxonomic identification issues, the inclusion date will be relaxed to the 31 December 1974. Ultimately, all these data will be made available in the public domain in accordance with the open access data sharing principles of the MAP .
Collaborative Online Databases
Many initiatives are being developed to provide information on the geographical distribution of disease vectors, including the Anopheles (Table 1; for example surveys of the geographical distribution of different forms of insecticide resistance –). These initiatives will be a significant help in data acquisition. Duplication of search effort will be minimized by ensuring compatibility between different data abstraction ontologies (e.g.,  and Text S2), so that where possible, data exchange can be automated. Where this cannot be achieved, data will be incorporated manually into the MAP archives with its provenance clearly recorded.
New Species Mapping Techniques
Recent years have seen the development of a number of new techniques to predict species ranges –, of which the most promising include methods based on boosted regression trees ,, generalised additive models , and maximum entropy approaches . In addition, Bayesian statistical approaches –, which have been widely used in mapping malaria prevalence –, have recently begun to be applied to mapping the relative frequency of Anopheles species . Bayesian models are able to integrate information from disparate sources and allow the comprehensive quantification of prediction uncertainty, something that is often overlooked in species mapping exercises .
An important input into the iterative mapping process is expert advice from entomologists and public health workers with extensive experience of DVS in the field. To facilitate this input, the DVS have been split into three biogeographical regions: the Americas (nine species); Africa, Europe, and the Middle East (13 species); and the Asia-Pacific region (19 species) (Text S1). These experts have helped refine the expert opinion distributions digitised from the literature for the 41 DVS. These are presented alongside the species occurrence summaries in Text S3, Text S4, and Text S5.
New Earth Observing Satellite Data
The statistical techniques we shall employ in future mapping efforts will model species occurrence as a function of environmental variables. We can then predict species distributions as a function of environmental conditions that can be obtained from Earth-observing satellite imagery . During model formulation and validation we shall use coarse spatial resolution (∼8×8 km) multitemporal remotely sensed imagery  to reduce computational demand. Once the particular mapping technique is chosen, we will move to more contemporary Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery, available globally at ∼1×1 km spatial resolution , to improve the spatial resolution of the predictions. Adapting temporal Fourier analyses techniques, which ordinate seasonal environmental data ,, to cope with the irregular compositing periods of MODIS data, has been completed and the data has already been made available in the public domain .
New Bionomics Review
The usefulness of the species range maps when available online , can be improved by combining them with summaries of the species-specific life history characteristics or “bionomics” of the DVS. Anopheline vector bionomics are critical in defining the appropriate (and inappropriate) modes of control at the national and local level –. For example, indoor residual spraying of houses for the control of a vector that is predominantly an outdoor resting species and prefers biting animals (e.g., An. (Cellia) arabiensis) is unlikely to be an optimal control strategy . Conversely, if the vector feeds predominantly indoors and at night (e.g., An. (Cellia) gambiae), insecticide-treated nets are likely to be a very appropriate intervention ,. Information on characteristics of specific larval habitats and range will also be informative. Public health and education measures aimed at larval reduction may be feasible across large parts of the Middle East and Asia , where An. (Cellia) stephensi is the major DVS. This species readily breeds in urban areas, often using human-made water containers as its preferred larval habitat. Conversely, environmental management techniques such as installing tidal gates or constructing drainage systems are likely to be more effective as a permanent means of reducing or eliminating suitable coastal habitats of members of the An. (Cellia) sundaicus complex across substantial areas of South East Asia .
A systematic review of life-history characteristics pertinent to control is also timely as previous summaries become out of date ,–. For example, as the taxonomy of the genus is better understood, it is evident that previous accounts which do not separate the different members of species complexes may omit or confuse critical biological information relevant for pest management. Examples of this occur in the An. sundaicus  and An. (Cellia) minimus complexes . In addition, it would be desirable to incorporate the latest information on the phylogeny of the Anopheles , so that modern comparative methods  can be used to infer species characteristics from evolutionary relationships when no observations are available. This assembled information will be particularly useful for extending models of malaria transmission beyond An. gambiae, the species that has been the subject of most –, but not all , attention. This will become increasingly important as operational and research communities alike continue to model the impact of vector control on malaria transmission .
Since abundance cannot be modelled with these opportunistic data assemblies, the bionomics review will also facilitate a ranking of the importance in malaria transmission of the different DVS in each region. This ranking will enable multiple species maps to be overlaid to obtain a more accurate picture of the overall epidemiological significance of the local DVS community and thus provide a better understanding of the complexity of transmission in an area. It is clear that subregional ecological diversity, coupled with the behavioural plasticity of many DVS, will require that any maps, and associated bionomics information provided, be interpreted and acted on cautiously with local expert knowledge.
The completed DVS databases and predictive maps will be made available online once generated, alongside the wider portfolio of MAP products, including spatial limits and endemicity maps for the human malaria parasites ,. This juxtaposition of information should represent an important cartographic resource for those engaged in malaria control and where feasible, its elimination. The success and long-term sustainability of this DVS mapping initiative depends critically on its continued support, development, and refinement in the malaria vector control and research communities. We hope that the information on the aims and objectives provided here, and the commitment to providing data in an open access venue, will help ensure that support.
Defining the dominant Anopheles vector species (and species complexes) of human malaria.
(0.14 MB DOC)
Pro forma for the abstraction of occurrence data for the dominant Anopheles vectors of human malaria.
(0.08 MB DOC)
Maps of expert opinion distribution and species occurrence records for the dominant Anopheles vector species (and species complexes) of human malaria in the Americas.
(1.82 MB PDF)
Maps of expert opinion distribution and species occurrence records for the dominant Anopheles vector species (and species complexes) of human malaria in Africa, Europe, and the Middle East.
(3.02 MB PDF)
Maps of expert opinion distribution and species occurrence records for the dominant Anopheles vector species (and species complexes) of human malaria in the Asia-Pacific region.
(4.11 MB PDF)
We thank Anja Bibby, Simon Brooker, and Bob Snow for comments on the manuscript. The following persons provided valuable unpublished information for the malaria vectors in the Americas: Marylin Aparicio (Bolivia), Mauricio Casas (Mexico), Roberto Fernández (Peru), Ranulfo González (Colombia), and Ricardo Lourenço-de-Oliveira (Brazil). The authors acknowledge the support of the Kenyan Medical Research Institute (KEMRI) and this paper is published with the permission of the director of KEMRI.
ICMJE criteria for authorship read and met: SIH MES RMO CWK PMM CCT DB PWG REH APP WHT MJB TC IRFE REH JH SM CMM YRP HCJG. Wrote the first draft of the paper: SIH. Contributed to the writing of the paper: SIH MES RMO CWK PMM CCT DB PWG REH APP WHT MJB TC IRFE REH JH SM CMM YRP HCJG. Conceived the project: SIH. Abstracted occurrence data: MES RMO CWK PMM CCT DB REH IRFE. Technical advisory group for the project and collectively responsible for reviewing occurrence data and providing expert opinion on the geographical distribution maps: MJB TC REH JH SM CMM YR-P HCJH. Ensured the use of correct formal taxonomic nomenclature: REH. Provided modeling advice: PWG APP. Provided database support and created the maps: WHT.
- 1. Guerra CA, Gikandi PW, Tatem AJ, Noor AM, Smith DL, et al. (2008) The limits and intensity of Plasmodium falciparum transmission: implications for malaria control and elimination worldwide. PLoS Med 5: e38. doi:10.1371/journal.pmed.0050038.
- 2. Hay SI, Guerra CA, Gething PW, Patil AP, Tatem AJ, et al. (2009) A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med 6: e48. doi:10.1371/journal.pmed.1000048.
- 3. Zahar AR (1984) Vector bionomics in the epidemiology and control of malaria. Part I. The WHO African region and the southern WHO Eastern Mediterranean region. Section I: malaria vectors of the Afrotropical region - general information. Section II: an overview of malaria control problems and the recent malaria situation. (VBC/84.6-MAP/84.3). Geneva: World Health Organization . 109 p.
- 4. Kelly-Hope L, Ranson H, Hemingway J (2008) Lessons from the past: managing insecticide resistance in malaria control and eradication programmes. Lancet Infect Dis 8: 387–389.
- 5. Coetzee M (2004) Distribution of the African malaria vectors of the Anopheles gambiae complex. Am J Trop Med Hyg 70: 103–104.
- 6. Coetzee M, Craig M, le Sueur D (2000) Distribution of African malaria mosquitoes belonging to the Anopheles gambiae complex. Parasitol Today 16: 74–77.
- 7. Levine RS, Townsend Peterson A, Benedict MQ (2004) Geographic and ecologic distributions of the Anopheles gambiae complex predicted using a genetic algorithm. Am J Trop Med Hyg 70: 105–109.
- 8. Lindsay SW, Parson L, Thomas CJ (1998) Mapping the ranges and relative abundance of the two principal African malaria vectors, Anopheles gambiae sensu stricto and An. arabiensis, using climate data. Proc R Soc Lond B Biol Sci 265: 847–854.
- 9. Rogers DJ, Randolph SE, Snow RW, Hay SI (2002) Satellite imagery in the study and forecast of malaria. Nature 415: 710–715.
- 10. Moffett A, Shackelford N, Sarkar S (2007) Malaria in Africa: vector species' niche models and relative risk maps. PLoS One 2: e824. doi:10.1371/journal.pone.0000824.
- 11. Moffett A, Strutz S, Guda N, Gonzalez C, Ferro MC, et al. (2009) A global public database of disease vector and reservoir distributions. PLoS Negl Trop Dis 3: e378. doi:10.1371/journal.pntd.0000378.
- 12. Rubio-Palis Y, Zimmerman RH (1997) Ecoregional classification of malaria vectors in the neotropics. J Med Entomol 34: 499–510.
- 13. Levine RS, Peterson AT, Benedict MQ (2004) Distribution of members of Anopheles quadrimaculatus Say s.l. (Diptera: Culicidae) and implications for their roles in malaria transmission in the United States. J Med Entomol 41: 607–613.
- 14. Foley DH, Weitzman AL, Miller SE, Faran ME, Rueda LM, et al. (2008) The value of georeferenced collection records for predicting patterns of mosquito species richness and endemism in the Neotropics. Ecol Entomol 33: 12–23.
- 15. Osborn FR, Rubio-Palis Y, Herrera M, Figuera A, Moreno JE (2004) Caracterización ecoregional de los vectores de malaria en Venezuela. Boletín de Malariología Y Salud Ambiental 44: 77–92.
- 16. Loaiza JR, Bermingham E, Scott ME, Rovira JR, Conn JE (2008) Species composition and distribution of adult Anopheles (Diptera: Culicidae) in Panama. J Med Entomol 45: 841–851.
- 17. Kuhn KG, Campbell-Lendrum DH, Davies CR (2002) A continental risk map for malaria mosquito (Diptera: Culicidae) vectors in Europe. J Med Entomol 39: 621–630.
- 18. Manguin S, Garros C, Dusfour I, Harbach RE, Coosemans M (2008) Bionomics, taxonomy, and distribution of the major malaria vector taxa of Anopheles subgenus Cellia in Southeast Asia: an updated review. Infect Genet Evol 8: 489–503.
- 19. Sweeney AW, Beebe NW, Cooper RD, Bauer JT, Peterson AT (2006) Environmental factors associated with distribution and range limits of malaria vector Anopheles farauti in Australia. J Med Entomol 43: 1068–1075.
- 20. Obsomer V, Defourny P, Coosemans M (2007) The Anopheles dirus complex: spatial distribution and environmental drivers. Malar J 6: 26.
- 21. Foley DH, Rueda LM, Peterson AT, Wilkerson RC (2008) Potential distribution of two species in the medically important Anopheles minimus Complex (Diptera: Culicidae). J Med Entomol 45: 852–860.
- 22. Garros C, Van Nguyen C, Trung HD, Van Bortel W, Coosemans M, et al. (2008) Distribution of Anopheles in Vietnam, with particular attention to malaria vectors of the Anopheles minimus complex. Malar J 7: 11.
- 23. White GB (1989) Malaria. Geographical distribution of arthropod-borne diseases and their principal vectors WHO/VBC/89967. Geneva: World Health Organization, Division of Vector Biology and Control. pp. 7–22.
- 24. Kiszewski A, Mellinger A, Spielman A, Malaney P, Sachs SE, et al. (2004) A global index representing the stability of malaria transmission. Am J Trop Med Hyg 70: 486–498.
- 25. Mouchet J, Carnevale P, Coosemans M, Julvez J, Manguin S, et al. (2004) Biodiversité du paludisme dans le monde. Montrouge, France: John Libbey Eurotext . 428 p.
- 26. Manguin S, Carnevale P, Mouchet J, Coosemans M, Julvez J, et al. (2008) Biodiversity of malaria in the world. Montrouge, France: John Libbey Eurotext . 464 p.
- 27. Tatem AJ, Rogers DJ, Hay SI (2006) Estimating the malaria risk of African mosquito movement by air travel. Malar J 5: 57.
- 28. Feachem R, Sabot O (2008) A new global malaria eradication strategy. Lancet 10: 1633–1635.
- 29. Wernsdorfer W, Hay SI, Shanks GD (2009) Learning from history. Shrinking the Malaria Map: a Prospectus on Malaria Elimination 95–107.
- 30. Hay SI, Smith DL, Snow RW (2008) Measuring malaria endemicity from intense to interrupted transmission. Lancet Infect Dis 8: 369–378.
- 31. Hay SI, Snow RW (2006) The Malaria Atlas Project: developing global maps of malaria risk. PLoS Med 3: e473. doi:10.1371/journal.pmed.0030473.
- 32. Harbach RE (1994) Review of the internal classification of the genus Anopheles (Diptera: Culicidae): the foundation for comparative systematics and phylogenetic research. Bull Entomol Res 84: 331–342.
- 33. Harbach RE (2004) The classification of genus Anopheles (Diptera: Culicidae): a working hypothesis of phylogenetic relationships. Bull Entomol Res 94: 537–553.
- 34. Harbach RE (2009) Mosquito taxonomic inventory (http://mosquito-taxonomic-inventory.info). Accessed 29 September 2009.
- 35. Service MW, Townson H (2002) The Anopheles vector. In: Gilles HM, Warrell DA, editors. Essential Malariology. Fourth edition ed. London: Arnold. pp. 59–84.
- 36. Takken W, Lindsay SW (2003) Factors affecting the vectorial competence of Anopheles gambiae: a question of scale. In: Takken W, Scott TW , editors. Ecological Aspects for Application of Genetically Modified Mosquitoes. Dordrecht: Kluwer Academic Publishers. pp. 75–90.
- 37. Service MW (1993) The Anopheles vector. In: Gilles HM, Warrell DA , editors. Bruce-Chwatt's Essential Malariology. Third edition ed. London: Edward Arnold. pp. 96–123.
- 38. Service MW (1993) Appendix II. Characteristics of some major Anopheles vectors of human malaria. In: Gilles HM, Warrell DA , editors. Bruce-Chwatt's Essential Malariology. Third edition ed. London: Edward Arnold. pp. 305–310.
- 39. Guerra CA, Hay SI, Lucioparedes LS, Gikandi PW, Tatem AJ, et al. (2007) Assembling a global database of malaria parasite prevalence for the Malaria Atlas Project. Malar J 6: 17.
- 40. Knight KL (1978) Supplement to “A catalog of the mosquitoes of the world (Diptera: Culicidae)”. College Park, Maryland, U.S.A.: Thomas Say Foundation, Entomological Society of America . 107 p.
- 41. Knight KL, Stone A (1977) A catalog of the mosquitoes of the world (Diptera: Culicidae). College Park, Maryland, U.S.A.: Thomas Say Foundation, Entomological Society of America.
- 42. Ward RA (1984) Second supplement to “A catalog of the mosquitoes of the world (Diptera: Culicidae)”. Mosq Syst 16: 227–270.
- 43. Ward RA (1992) Third supplement to “A catalog of the mosquitoes of the world (Diptera: Culicidae)”. Mosq Syst 24: 177–230.
- 44. Southwood TRE (1977) Habitat, templet for ecological strategies? Presidential address to British Ecological Society, 5 January 1977. J Anim Ecol 46: 337–365.
- 45. Rogers DJ (2006) Models for vectors and vector-borne diseases. Adv Parasitol 62: 1–35.
- 46. Chapman AD, Wieczorek J (2006) Guide to best practices for georeferencing. Copenhagen: Global Biodiversity Information Facility.
- 47. Wieczorek J, Guo Q, Hijmans RJ (2004) The point-radius method for georeferencing locality descriptions and calculating associated uncertainty. Int J Geogr Inf Sci 18: 745–767.
- 48. Guralnick RP, Wieczorek J, Beaman R, Hijmans RJ (2006) BioGeomancer: automated georeferencing to map the world's biodiversity data. PLoS Biol 4: e381. doi:10.1371/journal.pbio.0040381.
- 49. Guo Q, Liu Y, Wieczorek J (2008) Georeferencing locality descriptions and computing associated uncertainty using a probabilistic approach. Int J Geogr Inf Sci 22: 1067–1090.
- 50. Coleman M, Sharp B, Seocharan I, Hemingway J (2006) Developing an evidence-based decision support system for rational insecticide choice in the control of African malaria vectors. J Med Entomol 43: 663–668.
- 51. Hemingway J, Beaty BJ, Rowland M, Scott TW, Sharp BL (2006) The Innovative Vector Control Consortium: improved control of mosquito-borne diseases. Trends Parasitol 22: 308–312.
- 52. Van Bortel W, Trung HD, Thuan le K, Sochantha T, Socheat D, et al. (2008) The insecticide resistance status of malaria vectors in the Mekong region. Malar J 7: 102.
- 53. Koum G, Yekel A, Ndifon B, Simard F (2004) Design and implementation of a mosquito database through an entomological ontology. Bioinformatics 20: 2205–2211.
- 54. Argaez JA, Christen JA, Nakamura M, Soberon J (2005) Prediction of potential areas of species distributions based on presence-only data. Environ Ecol Stat 12: 27–44.
- 55. Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, et al. (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129–151.
- 56. Segurado P, Araujo MB (2004) An evaluation of methods for modelling species distributions. J Biogeogr 31: 1555–1568.
- 57. Leathwick JR, Elith J, Hastie T (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199: 188–196.
- 58. Potts JM, Elith J (2006) Comparing species abundance models. Ecol Model 199: 153–163.
- 59. Tan CO, Ozesmi U, Beklioglu M, Per E, Kurt B (2006) Predictive models in ecology: comparison of performances and assessment of applicability. Ecol Informatics 1: 195–211.
- 60. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28: 337–374.
- 61. Sexton J, Laake P (2007) Boosted regression trees with errors in variables. Biometrics 63: 586–592.
- 62. Guisan A, Edwards TC, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157: 89–100.
- 63. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190: 231–259.
- 64. Gelfand AE, Schmidt AM, Wu S, Silander JA, Latimer A, et al. (2005) Modelling species diversity through species level hierarchical modelling. J Roy Stat Soc C-App 54: 1–20.
- 65. Gelfand AE, Silander JA Jr, Wu S, Latimer A, Lewis PO, et al. (2006) Explaining species distribution patterns through hierarchical modeling. Bayesian Analysis 1: 41–92.
- 66. Kery M, Royle JA (2008) Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys. J Appl Ecol 45: 589–598.
- 67. Diggle P, Moyeed R, Rowlingson B, Thomson M (2002) Childhood malaria in The Gambia: a case-study in model-based geostatistics. J Roy Stat Soc C-App 51: 493–506.
- 68. Rattanasiri S, Bohning D, Rojanavipart P, Athipanyakom S (2004) A mixture model application in disease mapping of malaria. Southeast Asian J Trop Med Public Health 35: 38–47.
- 69. Gemperli A, Sogoba N, Fondjo E, Mabaso M, Bagayoko M, et al. (2006) Mapping malaria transmission in West and Central Africa. Trop Med Int Health 11: 1032–1046.
- 70. Gemperli A, Vounatsou P, Sogoba N, Smith T (2006) Malaria mapping using transmission models: application to survey data from Mali. Am J Epidemiol 163: 289–297.
- 71. Gosoniu L, Vounatsou P, Sogoba N, Smith T (2006) Bayesian modelling of geostatistical malaria risk data. Geospat Health 1: 127–139.
- 72. Noor AM, Clements ACA, Gething PW, Moloney G, Borle M, et al. (2008) Spatial prediction of Plasmodium falciparum prevalence in Somalia. Malar J 7: 159.
- 73. Sogoba N, Vounatsou P, Bagayoko MM, Doumbia S, Dolo G, et al. (2007) The spatial distribution of Anopheles gambiae sensu stricto and An. arabiensis (Diptera: Culicidae) in Mali. Geospat Health 1: 213–222.
- 74. Elith J, Burgman MA, Regan HM (2002) Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecol Model 157: 313–329.
- 75. Tatem AJ, Goetz SJ, Hay SI (2008) Fifty years of Earth-observation satellites. Am Sci 96: 390–398.
- 76. Hay SI, Tatem AJ, Graham AJ, Goetz SJ, Rogers DJ (2006) Global environmental data for mapping infectious disease distribution. Adv Parasitol 62: 37–77.
- 77. Scharlemann JPW, Benz D, Hay SI, Purse BV, Tatem AJ, et al. (2008) Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS One 3: e1408. doi:10.1371/journal.pone.0001408.
- 78. Rogers DJ (2000) Satellites, space, time and the African trypanosomiases. Adv Parasitol 47: 129–171.
- 79. Rogers DJ, Robinson TP (2004) Tsetse distribution. In: Maudlin I, Holmes PH, Miles MA, editors. The Trypanosomiases: CAB International. pp. 139–179.
- 80. Lozano-Fuentes S, Elizondo-Quiroga D, Farfan-Ale JA, Loroño-Pino MA, Garcia-Rejon J, et al. (2008) Use of Google Earth™ to strengthen public health capacity and facilitate management of vector-borne diseases in resource-poor environments. Bull World Health Organ 86: 718–725.
- 81. Walker K, Lynch M (2007) Contributions of Anopheles larval control to malaria suppression in tropical Africa: review of achievements and potential. Med Vet Entomol 21: 2–21.
- 82. W.H.O (2006) Malaria vector control and personal protection: report of a WHO study group. WHO Technical Report Series, no 936. Geneva: World Health Organization . 72 p.
- 83. W.H.O (2004) Global strategic framework for integrated vector management. Document WHO/CDS/CPE/PVC/2004.10. Geneva: World Health Organization.
- 84. Shililu J, Ghebremeskel T, Seulu F, Mengistu S, Fekadu H, et al. (2004) Seasonal abundance, vector behavior, and malaria parasite transmission in Eritrea. J Am Mosq Control Assoc 20: 155–164.
- 85. Lengeler C (2004) Insecticide-treated bed nets and curtains for preventing malaria. The Cochrane Database of Systematic Reviews 2004, Issue 2. Art. No.:CD000363.pub2. DOI: 10.1002/14651858.CD000363.pub2.
- 86. Snow RW, Lindsay SW, Hayes RJ, Greenwood BM (1988) Permethrin-treated bed nets (mosquito nets) prevent malaria in Gambian children. Trans R Soc Trop Med Hyg 82: 838–842.
- 87. Sharma VP (1996) Re-emergence of malaria in India. Indian J Med Res 103: 26–45.
- 88. Konradsen F, van der Hoek W, Amerasinghe FP, Mutero C, Boelee E (2004) Engineering and malaria control: learning from the past 100 years. Acta Trop 89: 99–108.
- 89. Zahar AR (1985) Vector bionomics in the epidemiology and control of malaria. Part I. The WHO African region and the southern WHO Eastern Mediterranean region. Section III: vector bionomics, malaria epidemiology and control by geographical areas (a) West Africa (VBC/85.1-MAP/85.1). Geneva: World Health Organization . 225 p.
- 90. Zahar AR (1985) Vector bionomics in the epidemiology and control of malaria. Part I. The WHO African region and the southern WHO Eastern Mediterranean region. Section III: Vector bionomics, malaria epidemiology and control by geographical areas (b) equatorial Africa, (c) southern Africa (VBC/85.2-MAP/85.2). Geneva: World Health Organization . 136 p.
- 91. Zahar AR (1985) Vector bionomics in the epidemiology and control of malaria. Part I. The WHO African region and the southern WHO Eastern Mediterranean region. Section III: Vector bionomics, malaria epidemiology and control by geographical areas (d) East Africa, (e) eastern outer islands, (f) southwestern Arabia (VBC/85.3-MAP/85.3). Geneva: World Health Organization . 244 p.
- 92. Zahar AR (1988) Vector bionomics in the epidemiology and control of malaria. Part II. The WHO European region and the WHO Eastern Mediterranean region. Volume I: vector laboratory studies. (VBC/88.5-MAP/88.2). Geneva: World Health Organization . 228 p.
- 93. Zahar AR (1990) Vector bionomics in the epidemiology and control of malaria. Part II. The WHO European region and the WHO Eastern Mediterranean region. Volume II: applied field studies. Section I: an overview of the malaria situation and current problems. Section II: vector distribution (VBC/90.1). Geneva: World Health Organization.
- 94. Zahar AR (1990) Vector bionomics in the epidemiology and control of malaria. Part II. The WHO European region and the WHO Eastern Mediterranean region. Volume II: applied field studies. Section III: vector bionomics, malaria epidemiology and control by geographical areas (a) the Mediterranean basin (VBC/90.2-MAL/90.2). Geneva: World Health Organization . 226 p.
- 95. Zahar AR (1990) Vector bionomics in the epidemiology and control of malaria. Part II. The WHO European region and the WHO Eastern Mediterranean region. Volume II: applied field studies. Section III: vector bionomics, malaria epidemiology and control by geographical areas (b) Asia west of India (VBC/90.3-MAL/90.3). Geneva: World Health Organization . 352 p.
- 96. Zahar AR (1994) Vector bionomics in the epidemiology and control of malaria. Part III. The WHO South East Asia Region and the WHO Western Pacific Region. (CDT/MAL/94.1). Geneva: World Health Organization.
- 97. Zahar AR (1996) Vector bionomics in the epidemiology and control of malaria. Part III. The WHO South East Asia Region and the WHO Western Pacific Region. (CDT/MAL/96.1). Geneva: World Health Organization.
- 98. Dusfour I, Harbach RE, Manguin S (2004) Bionomics and systematics of the Oriental Anopheles sundaicus complex in relation to malaria transmission and vector control. Am J Trop Med Hyg 71: 518–524.
- 99. Garros C, Van Bortel W, Trung HD, Coosemans M, Manguin S (2006) Review of the Minimus Complex of Anopheles, main malaria vector in Southeast Asia: from taxonomic issues to vector control strategies. Trop Med Int Health 11: 102–114.
- 100. Harvey PH, Pagel MD (1991) The comparative method in evolutionary biology;. In: Harvey PH, May RM, editors. Oxford: Oxford University Press.
- 101. Smith DL, McKenzie FE (2004) Statics and dynamics of malaria infection in Anopheles mosquitoes. Malar J 3: 13.
- 102. Killeen GF, McKenzie FE, Foy BD, Schieffelin C, Billingsley PF, et al. (2000) A simplified model for predicting malaria entomologic inoculation rates based on entomologic and parasitologic parameters relevant to control. Am J Trop Med Hyg 62: 535–544.
- 103. Smith DL, McKenzie FE, Snow RW, Hay SI (2007) Revisiting the basic reproductive number for malaria and its implications for malaria control. PLoS Biol 5: e42. doi:10.1371/journal.pbio.0050042.
- 104. Le Menach A, Takala S, McKenzie FE, Perisse A, Harris A, et al. (2007) An elaborated feeding cycle model for reductions in vectorial capacity of night-biting mosquitoes by insecticide-treated nets. Malar J 6: 10.