#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Developing Global Maps of the Dominant Vectors of Human Malaria


article has not abstract


Published in the journal: Developing Global Maps of the Dominant Vectors of Human Malaria. PLoS Med 7(2): e32767. doi:10.1371/journal.pmed.1000209
Category: Health in Action
doi: https://doi.org/10.1371/journal.pmed.1000209

Summary

article has not abstract

Introduction

Despite advances in mapping the geographical distribution and intensity of malaria transmission [1],[2], 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 [3]. Substantial investments by major donors in the distribution of long-lasting insecticide-treated nets and indoor residual spraying campaigns [4] are, therefore, not always fully informed by the basic biology of local anophelines.

Recent attempts to delineate Anopheles distributions have been conducted in Africa [5][11], the Americas [12][16], Europe [17], Central and South East Asia [18][22], and at the global scale [23][26]. 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 [27] and others consider their prospects of malaria elimination [28],[29], contemporary maps of anophelines that are competent vectors for malaria are important in assessing local receptivity to reintroduction [30].

To help address these needs, the Malaria Atlas Project (MAP, http://www.map.ox.ac.uk) [31] 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 [32][34]. Of these, approximately 70 have been shown to be competent vectors of human malaria [35] 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 [36]. The DVS were the inclusive set of those species identified as “main” [37],[38], “dominant” [24], or “principal” [23],[25] 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 [23][25],[37],[38], 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 [39]. 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 [32],[33]. 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 [32],[33],[40][43]. 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 [44] using climate-envelope approaches [45], 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 [39], so that any uncertainty associated with the positioning could be estimated [46][49]. 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 [31].

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 [50][52]). 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., [53] 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.

Tab. 1. Summary of the online resources for <i>Anopheles</i>.
Summary of the online resources for &lt;i&gt;Anopheles&lt;/i&gt;.

New Species Mapping Techniques

Recent years have seen the development of a number of new techniques to predict species ranges [54][59], of which the most promising include methods based on boosted regression trees [60],[61], generalised additive models [62], and maximum entropy approaches [63]. In addition, Bayesian statistical approaches [64][66], which have been widely used in mapping malaria prevalence [67][72], have recently begun to be applied to mapping the relative frequency of Anopheles species [73]. 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 [74].

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 [75]. During model formulation and validation we shall use coarse spatial resolution (∼8×8 km) multitemporal remotely sensed imagery [76] 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 [77], to improve the spatial resolution of the predictions. Adapting temporal Fourier analyses techniques, which ordinate seasonal environmental data [78],[79], 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 [77].

New Bionomics Review

The usefulness of the species range maps when available online [80], 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 [81][83]. 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 [84]. 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 [85],[86]. 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 [87], 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 [88].

A systematic review of life-history characteristics pertinent to control is also timely as previous summaries become out of date [3],[89][97]. 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 [98] and An. (Cellia) minimus complexes [99]. In addition, it would be desirable to incorporate the latest information on the phylogeny of the Anopheles [33], so that modern comparative methods [100] 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 [101][103], but not all [104], attention. This will become increasingly important as operational and research communities alike continue to model the impact of vector control on malaria transmission [30].

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.

Conclusions

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 [1],[2]. 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.

Supporting Information

Attachment 1

Attachment 2

Attachment 3

Attachment 4

Attachment 5


Zdroje

1. GuerraCA

GikandiPW

TatemAJ

NoorAM

SmithDL

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. HaySI

GuerraCA

GethingPW

PatilAP

TatemAJ

2009 A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med 6 e48 doi:10.1371/journal.pmed.1000048

3. ZaharAR

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

4. Kelly-HopeL

RansonH

HemingwayJ

2008 Lessons from the past: managing insecticide resistance in malaria control and eradication programmes. Lancet Infect Dis 8 387 389

5. CoetzeeM

2004 Distribution of the African malaria vectors of the Anopheles gambiae complex. Am J Trop Med Hyg 70 103 104

6. CoetzeeM

CraigM

le SueurD

2000 Distribution of African malaria mosquitoes belonging to the Anopheles gambiae complex. Parasitol Today 16 74 77

7. LevineRS

Townsend PetersonA

BenedictMQ

2004 Geographic and ecologic distributions of the Anopheles gambiae complex predicted using a genetic algorithm. Am J Trop Med Hyg 70 105 109

8. LindsaySW

ParsonL

ThomasCJ

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. RogersDJ

RandolphSE

SnowRW

HaySI

2002 Satellite imagery in the study and forecast of malaria. Nature 415 710 715

10. MoffettA

ShackelfordN

SarkarS

2007 Malaria in Africa: vector species' niche models and relative risk maps. PLoS One 2 e824 doi:10.1371/journal.pone.0000824

11. MoffettA

StrutzS

GudaN

GonzalezC

FerroMC

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-PalisY

ZimmermanRH

1997 Ecoregional classification of malaria vectors in the neotropics. J Med Entomol 34 499 510

13. LevineRS

PetersonAT

BenedictMQ

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. FoleyDH

WeitzmanAL

MillerSE

FaranME

RuedaLM

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. OsbornFR

Rubio-PalisY

HerreraM

FigueraA

MorenoJE

2004 Caracterización ecoregional de los vectores de malaria en Venezuela. Boletín de Malariología Y Salud Ambiental 44 77 92

16. LoaizaJR

BerminghamE

ScottME

RoviraJR

ConnJE

2008 Species composition and distribution of adult Anopheles (Diptera: Culicidae) in Panama. J Med Entomol 45 841 851

17. KuhnKG

Campbell-LendrumDH

DaviesCR

2002 A continental risk map for malaria mosquito (Diptera: Culicidae) vectors in Europe. J Med Entomol 39 621 630

18. ManguinS

GarrosC

DusfourI

HarbachRE

CoosemansM

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. SweeneyAW

BeebeNW

CooperRD

BauerJT

PetersonAT

2006 Environmental factors associated with distribution and range limits of malaria vector Anopheles farauti in Australia. J Med Entomol 43 1068 1075

20. ObsomerV

DefournyP

CoosemansM

2007 The Anopheles dirus complex: spatial distribution and environmental drivers. Malar J 6 26

21. FoleyDH

RuedaLM

PetersonAT

WilkersonRC

2008 Potential distribution of two species in the medically important Anopheles minimus Complex (Diptera: Culicidae). J Med Entomol 45 852 860

22. GarrosC

Van NguyenC

TrungHD

Van BortelW

CoosemansM

2008 Distribution of Anopheles in Vietnam, with particular attention to malaria vectors of the Anopheles minimus complex. Malar J 7 11

23. WhiteGB

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 7 22

24. KiszewskiA

MellingerA

SpielmanA

MalaneyP

SachsSE

2004 A global index representing the stability of malaria transmission. Am J Trop Med Hyg 70 486 498

25. MouchetJ

CarnevaleP

CoosemansM

JulvezJ

ManguinS

2004 Biodiversité du paludisme dans le monde. Montrouge, France John Libbey Eurotext 428

26. ManguinS

CarnevaleP

MouchetJ

CoosemansM

JulvezJ

2008 Biodiversity of malaria in the world. Montrouge, France John Libbey Eurotext 464

27. TatemAJ

RogersDJ

HaySI

2006 Estimating the malaria risk of African mosquito movement by air travel. Malar J 5 57

28. FeachemR

SabotO

2008 A new global malaria eradication strategy. Lancet 10 1633 1635

29. WernsdorferW

HaySI

ShanksGD

2009 Learning from history. Shrinking the Malaria Map: a Prospectus on Malaria Elimination 95 107

30. HaySI

SmithDL

SnowRW

2008 Measuring malaria endemicity from intense to interrupted transmission. Lancet Infect Dis 8 369 378

31. HaySI

SnowRW

2006 The Malaria Atlas Project: developing global maps of malaria risk. PLoS Med 3 e473 doi:10.1371/journal.pmed.0030473

32. HarbachRE

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. HarbachRE

2004 The classification of genus Anopheles (Diptera: Culicidae): a working hypothesis of phylogenetic relationships. Bull Entomol Res 94 537 553

34. HarbachRE

(2009) Mosquito taxonomic inventory (http://mosquito-taxonomic-inventory.info). Accessed 29 September 2009

35. ServiceMW

TownsonH

2002 The Anopheles vector.

GillesHM

WarrellDA

Essential Malariology. Fourth edition ed London Arnold 59 84

36. TakkenW

LindsaySW

2003 Factors affecting the vectorial competence of Anopheles gambiae: a question of scale.

TakkenW

ScottTW

Ecological Aspects for Application of Genetically Modified Mosquitoes Dordrecht Kluwer Academic Publishers 75 90

37. ServiceMW

1993 The Anopheles vector.

GillesHM

WarrellDA

Bruce-Chwatt's Essential Malariology. Third edition ed London Edward Arnold 96 123

38. ServiceMW

1993 Appendix II. Characteristics of some major Anopheles vectors of human malaria.

GillesHM

WarrellDA

Bruce-Chwatt's Essential Malariology. Third edition ed London Edward Arnold 305 310

39. GuerraCA

HaySI

LucioparedesLS

GikandiPW

TatemAJ

2007 Assembling a global database of malaria parasite prevalence for the Malaria Atlas Project. Malar J 6 17

40. KnightKL

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

41. KnightKL

StoneA

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. WardRA

1984 Second supplement to “A catalog of the mosquitoes of the world (Diptera: Culicidae)”. Mosq Syst 16 227 270

43. WardRA

1992 Third supplement to “A catalog of the mosquitoes of the world (Diptera: Culicidae)”. Mosq Syst 24 177 230

44. SouthwoodTRE

1977 Habitat, templet for ecological strategies? Presidential address to British Ecological Society, 5 January 1977. J Anim Ecol 46 337 365

45. RogersDJ

2006 Models for vectors and vector-borne diseases. Adv Parasitol 62 1 35

46. ChapmanAD

WieczorekJ

2006 Guide to best practices for georeferencing. Copenhagen Global Biodiversity Information Facility

47. WieczorekJ

GuoQ

HijmansRJ

2004 The point-radius method for georeferencing locality descriptions and calculating associated uncertainty. Int J Geogr Inf Sci 18 745 767

48. GuralnickRP

WieczorekJ

BeamanR

HijmansRJ

2006 BioGeomancer: automated georeferencing to map the world's biodiversity data. PLoS Biol 4 e381 doi:10.1371/journal.pbio.0040381

49. GuoQ

LiuY

WieczorekJ

2008 Georeferencing locality descriptions and computing associated uncertainty using a probabilistic approach. Int J Geogr Inf Sci 22 1067 1090

50. ColemanM

SharpB

SeocharanI

HemingwayJ

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. HemingwayJ

BeatyBJ

RowlandM

ScottTW

SharpBL

2006 The Innovative Vector Control Consortium: improved control of mosquito-borne diseases. Trends Parasitol 22 308 312

52. Van BortelW

TrungHD

Thuan leK

SochanthaT

SocheatD

2008 The insecticide resistance status of malaria vectors in the Mekong region. Malar J 7 102

53. KoumG

YekelA

NdifonB

SimardF

2004 Design and implementation of a mosquito database through an entomological ontology. Bioinformatics 20 2205 2211

54. ArgaezJA

ChristenJA

NakamuraM

SoberonJ

2005 Prediction of potential areas of species distributions based on presence-only data. Environ Ecol Stat 12 27 44

55. ElithJ

GrahamCH

AndersonRP

DudikM

FerrierS

2006 Novel methods improve prediction of species' distributions from occurrence data. Ecography 29 129 151

56. SeguradoP

AraujoMB

2004 An evaluation of methods for modelling species distributions. J Biogeogr 31 1555 1568

57. LeathwickJR

ElithJ

HastieT

2006 Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199 188 196

58. PottsJM

ElithJ

2006 Comparing species abundance models. Ecol Model 199 153 163

59. TanCO

OzesmiU

BekliogluM

PerE

KurtB

2006 Predictive models in ecology: comparison of performances and assessment of applicability. Ecol Informatics 1 195 211

60. FriedmanJ

HastieT

TibshiraniR

2000 Additive logistic regression: a statistical view of boosting. Ann Stat 28 337 374

61. SextonJ

LaakeP

2007 Boosted regression trees with errors in variables. Biometrics 63 586 592

62. GuisanA

EdwardsTC

HastieT

2002 Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157 89 100

63. PhillipsSJ

AndersonRP

SchapireRE

2006 Maximum entropy modeling of species geographic distributions. Ecol Model 190 231 259

64. GelfandAE

SchmidtAM

WuS

SilanderJA

LatimerA

2005 Modelling species diversity through species level hierarchical modelling. J Roy Stat Soc C-App 54 1 20

65. GelfandAE

SilanderJAJr

WuS

LatimerA

LewisPO

2006 Explaining species distribution patterns through hierarchical modeling. Bayesian Analysis 1 41 92

66. KeryM

RoyleJA

2008 Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys. J Appl Ecol 45 589 598

67. DiggleP

MoyeedR

RowlingsonB

ThomsonM

2002 Childhood malaria in The Gambia: a case-study in model-based geostatistics. J Roy Stat Soc C-App 51 493 506

68. RattanasiriS

BohningD

RojanavipartP

AthipanyakomS

2004 A mixture model application in disease mapping of malaria. Southeast Asian J Trop Med Public Health 35 38 47

69. GemperliA

SogobaN

FondjoE

MabasoM

BagayokoM

2006 Mapping malaria transmission in West and Central Africa. Trop Med Int Health 11 1032 1046

70. GemperliA

VounatsouP

SogobaN

SmithT

2006 Malaria mapping using transmission models: application to survey data from Mali. Am J Epidemiol 163 289 297

71. GosoniuL

VounatsouP

SogobaN

SmithT

2006 Bayesian modelling of geostatistical malaria risk data. Geospat Health 1 127 139

72. NoorAM

ClementsACA

GethingPW

MoloneyG

BorleM

2008 Spatial prediction of Plasmodium falciparum prevalence in Somalia. Malar J 7 159

73. SogobaN

VounatsouP

BagayokoMM

DoumbiaS

DoloG

2007 The spatial distribution of Anopheles gambiae sensu stricto and An. arabiensis (Diptera: Culicidae) in Mali. Geospat Health 1 213 222

74. ElithJ

BurgmanMA

ReganHM

2002 Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecol Model 157 313 329

75. TatemAJ

GoetzSJ

HaySI

2008 Fifty years of Earth-observation satellites. Am Sci 96 390 398

76. HaySI

TatemAJ

GrahamAJ

GoetzSJ

RogersDJ

2006 Global environmental data for mapping infectious disease distribution. Adv Parasitol 62 37 77

77. ScharlemannJPW

BenzD

HaySI

PurseBV

TatemAJ

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. RogersDJ

2000 Satellites, space, time and the African trypanosomiases. Adv Parasitol 47 129 171

79. RogersDJ

RobinsonTP

2004 Tsetse distribution.

MaudlinI

HolmesPH

MilesMA

The Trypanosomiases CAB International 139 179

80. Lozano-FuentesS

Elizondo-QuirogaD

Farfan-AleJA

Loroño-PinoMA

Garcia-RejonJ

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. WalkerK

LynchM

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

83. W.H.O 2004 Global strategic framework for integrated vector management. Document WHO/CDS/CPE/PVC/2004.10 Geneva World Health Organization

84. ShililuJ

GhebremeskelT

SeuluF

MengistuS

FekaduH

2004 Seasonal abundance, vector behavior, and malaria parasite transmission in Eritrea. J Am Mosq Control Assoc 20 155 164

85. LengelerC

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. SnowRW

LindsaySW

HayesRJ

GreenwoodBM

1988 Permethrin-treated bed nets (mosquito nets) prevent malaria in Gambian children. Trans R Soc Trop Med Hyg 82 838 842

87. SharmaVP

1996 Re-emergence of malaria in India. Indian J Med Res 103 26 45

88. KonradsenF

van der HoekW

AmerasingheFP

MuteroC

BoeleeE

2004 Engineering and malaria control: learning from the past 100 years. Acta Trop 89 99 108

89. ZaharAR

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

90. ZaharAR

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

91. ZaharAR

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

92. ZaharAR

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

93. ZaharAR

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. ZaharAR

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

95. ZaharAR

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

96. ZaharAR

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. ZaharAR

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. DusfourI

HarbachRE

ManguinS

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. GarrosC

Van BortelW

TrungHD

CoosemansM

ManguinS

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. HarveyPH

PagelMD

1991 The comparative method in evolutionary biology;

HarveyPH

MayRM

Oxford Oxford University Press

101. SmithDL

McKenzieFE

2004 Statics and dynamics of malaria infection in Anopheles mosquitoes. Malar J 3 13

102. KilleenGF

McKenzieFE

FoyBD

SchieffelinC

BillingsleyPF

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. SmithDL

McKenzieFE

SnowRW

HaySI

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 MenachA

TakalaS

McKenzieFE

PerisseA

HarrisA

2007 An elaborated feeding cycle model for reductions in vectorial capacity of night-biting mosquitoes by insecticide-treated nets. Malar J 6 10

Štítky
Interné lekárstvo

Článok vyšiel v časopise

PLOS Medicine


2010 Číslo 2
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#