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

Malaria in Africa: Vector Species' Niche Models and Relative Risk Maps

A central theoretical goal of epidemiology is the construction of spatial models of disease prevalence and risk, including maps for the potential spread of infectious disease. We provide three continent-wide maps representing the relative risk of malaria in Africa based on ecological niche models of vector species and risk analysis at a spatial resolution of 1 arc-minute (9 185 275 cells of approximately 4 sq km). Using a maximum entropy method we construct niche models for 10 malaria vector species based on species occurrence records since 1980, 19 climatic variables, altitude, and land cover data (in 14 classes). For seven vectors (Anopheles coustani, A. funestus, A. melas, A. merus, A. moucheti, A. nili, and A. paludis) these are the first published niche models. We predict that Central Africa has poor habitat for both A. arabiensis and A. gambiae, and that A. quadriannulatus and A. arabiensis have restricted habitats in Southern Africa as claimed by field experts in criticism of previous models. The results of the niche models are incorporated into three relative risk models which assume different ecological interactions between vector species. The “additive” model assumes no interaction; the “minimax” model assumes maximum relative risk due to any vector in a cell; and the “competitive exclusion” model assumes the relative risk that arises from the most suitable vector for a cell. All models include variable anthrophilicity of vectors and spatial variation in human population density. Relative risk maps are produced from these models. All models predict that human population density is the critical factor determining malaria risk. Our method of constructing relative risk maps is equally general. We discuss the limits of the relative risk maps reported here, and the additional data that are required for their improvement. The protocol developed here can be used for any other vector-borne disease

Species composition and inversion polymorphism of the Anopheles gambiae complex in some sites of Ghana, West Africa.

Sampleso f Anophelesg ambiaes .l. werec ollectedf rom eight localitiesb elongingt o four of the five main ecologicasl tratao f Ghana.A nalysiso f ovarianp olytenec hromosomesre vealedt he presenceo f A. gambiae s.s. in all the sites studied, while l. arabiensisw as detectedo nly in the extremen orthern locality of Navrongoa nd l. rnelasin somes outherns ites.A nopheleas rabiensis howeda degreeo f inversionp olymorphism comparablet o the one observedin other WestA frican countries.T he analysiso f the chromosomal polymorphism of A. gambiaes .s.s howedt he presenceo f the FOREST form in the rain forest localities and the SAVANNA form in the coastal savanna sites. The MOPTI form occurred sympatrically with the SAVANNA form in the northernmostl ocality. The possiblei nfluenceo f the presenceo f various taxa of the A. gambiaec omplexa nd oftheir intra-specificv ariantso n malaria vectorials ystemis discussed

Analysis of partial and complete protection in malaria cohort studies.

BACKGROUND: Malaria transmission is highly heterogeneous and analysis of incidence data must account for this for correct statistical inference. Less widely appreciated is the occurrence of a large number of zero counts (children without a malaria episode) in malaria cohort studies. Zero-inflated regression methods provide one means of addressing this issue, and also allow risk factors providing complete and partial protection to be disentangled. METHODS: Poisson, negative binomial (NB), zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) regression models were fitted to data from two cohort studies of malaria in children in Ghana. Multivariate models were used to understand risk factors for elevated incidence of malaria and for remaining malaria-free, and to estimate the fraction of the population not at risk of malaria. RESULTS: ZINB models, which account for both heterogeneity in individual risk and an unexposed sub-group within the population, provided the best fit to data in both cohorts. These approaches gave additional insight into the mechanism of factors influencing the incidence of malaria compared to simpler approaches, such as NB regression. For example, compared to urban areas, rural residence was found to both increase the incidence rate of malaria among exposed children, and increase the probability of being exposed. In Navrongo, 34% of urban residents were estimated to be at no risk, compared to 3% of rural residents. In Kintampo, 47% of urban residents and 13% of rural residents were estimated to be at no risk. CONCLUSION: These results illustrate the utility of zero-inflated regression methods for analysis of malaria cohort data that include a large number of zero counts. Specifically, these results suggest that interventions that reach mainly urban residents will have limited overall impact, since some urban residents are essentially at no risk, even in areas of high endemicity, such as in Ghana

Epidemiology of malaria in the forest-savanna transitional zone of Ghana.

BACKGROUND: Information on the epidemiology of malaria is essential for designing and interpreting results of clinical trials of drugs, vaccines and other interventions. As a background to the establishment of a site for anti-malarial drugs and vaccine trials, the epidemiology of malaria in a rural site in central Ghana was investigated. METHODS: Active surveillance of clinical malaria was carried out in a cohort of children below five years of age (n = 335) and the prevalence of malaria was estimated in a cohort of subjects of all ages (n = 1484) over a 12-month period. Participants were sampled from clusters drawn around sixteen index houses randomly selected from a total of about 22,000 houses within the study area. The child cohort was visited thrice weekly to screen for any illness and a blood slide was taken if a child had a history of fever or a temperature greater than or equal to 37.5 degree Celsius. The all-age cohort was screened for malaria once every eight weeks over a 12-month period. Estimation of Entomological Inoculation Rate (EIR) and characterization of Anopheline malaria vectors in the study area were also carried out. RESULTS: The average parasite prevalence in the all age cohort was 58% (95% CI: 56.9, 59.4). In children below five years of age, the average prevalence was 64% (95% CI: 61.9, 66.0). Geometric mean parasite densities decreased significantly with increasing age. More than 50% of all children less than 10 years of age were anaemic. Children less than 5 years of age had as many as seven malaria attacks per child per year. The attack rates decreased significantly with increasing cut-offs of parasite density. The average Multiplicity of Infection (MOI) was of 6.1. All three pyrimethamine resistance mutant alleles of the Plasmodium falciparum dhfr gene were prevalent in this population and 25% of infections had a fourth mutant of pfdhps-A437G. The main vectors were Anopheles funestus and Anopheles gambiae and the EIR was 269 infective bites per person per year. CONCLUSION: The transmission of malaria in the forest-savanna region of central Ghana is high and perennial and this is an appropriate site for conducting clinical trials of anti-malarial drugs and vaccines

High exposure to Tunga penetrans (Linnaeus, 1758) correlates with intensity of infestation

Tungiasis is a parasitic skin disease widespread in resource-poor urban and rural communities in Brazil. Inhabitants of an urban slum in Northeast Brazil were examined for the presence of tungiasis lesions and followed-up twice a week for a period of three weeks. Each time the number, stages, and topographic localization of lesions were recorded on a documentation sheet. The infestation rate (number of newly embedded sand fleas per individual and day) remained stable during the observation period. The infestation rate was significantly related to the intensity of infestation (total number of lesions present) (rho = 0.70, p < 0.0001) and the proportion of viable lesions (rho = 0.28, p < 0.0001). The results indicate that in an endemic area the infestation intensity and the proportion of viable lesions can be used as a proxy to assess the exposure of individuals at risk for tungiasis. Persistently high infestation rates during the transmission season favour the use of prevention measures against invading sand fleas (such as a repellent) rather than a drug to kill already embedded parasites