Three chromosomal forms of Anopheles gambiae s.s., designated as Bamako, Mopti and Savanna, were studied for diagnostic PCR assays based on the analysis of the X-linked ribosomal DNA (rDNA). The study was performed on a 1.3 kb fragment containing part of the 28S coding region and part of the intergenic spacer region. The amplified material was cut with fourteen restriction enzymes to detect Restriction Fragment Length Polymorphisms (RFLPs). The enzymes Tru9I and HhaI produced patterns of DNA bands which differentiated Mopti from Savanna and Bamako; moreover, a distinct 'hybrid' pattern was recognized in the F1 female progeny from the cross of Mopti with either one of the other two chromosomal forms. The diagnostic significance of the PCR-RFLP assay was verified on 203 karyotyped females from field samples collected in two villages in Mali and one village in Burkina Faso. Agreement was observed between the chromosomal and the molecular identifications. No 'hybrid' molecular patterns were detected even among carriers of rare heterokaryotypes hypothetically produced by crosses between Mopti and Savanna. The results confirm previous observations indicating barriers to gene flow within An. gambiae s.s. and supporting the specific status of the taxonomic units proposed on cytogenetic ground.

Vector-borne diseases continue to contribute significantly to the global burden of disease, and cause epidemics that disrupt health security and cause wider socioeconomic impacts around the world. All are sensitive in different ways to weather and climate conditions, so that the ongoing trends of increasing temperature and more variable weather threaten to undermine recent global progress against these diseases. Here, we review the current state of the global public health effort to address this challenge, and outline related initiatives by the World Health Organization (WHO) and its partners. Much of the debate to date has centred on attribution of past changes in disease rates to climate change, and the use of scenario-based models to project future changes in risk for specific diseases. While these can give useful indications, the unavoidable uncertainty in such analyses, and contingency on other socioeconomic and public health determinants in the past or future, limit their utility as decision-support tools. For operational health agencies, the most pressing need is the strengthening of current disease control efforts to bring down current disease rates and manage short-term climate risks, which will, in turn, increase resilience to long-term climate change. The WHO and partner agencies are working through a range of programmes to (i) ensure political support and financial investment in preventive and curative interventions to bring down current disease burdens; (ii) promote a comprehensive approach to climate risk management; (iii) support applied research, through definition of global and regional research agendas, and targeted research initiatives on priority diseases and population groups.

Malaria prevalence data were collated from surveys of childhood populations in Mali since 1960. Altogether 101 such surveys were identified yielding suitable estimates of malaria Background Good maps of malaria risk have long been recognized as an important tool for malaria control. The production of such maps relies on modelling to predict the risk for most of the map, with actual observations of malaria prevalence usually only known at a limited number of specific locations. Estimation is complicated by the fact that there is often local variation of risk that cannot be accounted for by the known covariates and because data points of measured malaria prevalence are not evenly or randomly spread across the area to be mapped. MethodWe describe, by way of an example, a simple two-stage procedure for producing maps of predicted risk: we use logistic regression modelling to determine approximate risk on a larger scale and we employ geo-statistical ('kriging') approaches to improve prediction at a local level. Malaria prevalence in children under 10 was modelled using climatic, population and topographic variables as potential predictors. After the regression analysis, spatial dependence of the model residuals was investigated. Kriging on the residuals was used to model local variation in malaria risk over and above that which is predicted by the regression model. ResultsThe method is illustrated by a map showing the improvement of risk prediction brought about by the second stage. The advantages and shortcomings of this approach are discussed in the context of the need for further development of methodology and software.

Successful propagation of the malaria parasite Plasmodium falciparum within a susceptible mosquito vector is a prerequisite for the transmission of malaria. A field-based genetic analysis of the major human malaria vector, Anopheles gambiae, has revealed natural factors that reduce the transmission of P. falciparum. Differences in P. falciparum oocyst numbers between mosquito isofemale families fed on the same infected blood indicated a large genetic component affecting resistance to the parasite, and genome-wide scanning in pedigrees of wild mosquitoes detected segregating resistance alleles. The apparently high natural frequency of resistance alleles suggests that malaria parasites (or a similar pathogen) exert a significant selective pressure on vector populations.

SummaryWe have produced maps of Plasmodium falciparum malaria transmission in West and Central Africa using the Mapping Malaria Risk in Africa (MARA) database comprising all malaria prevalence surveys in these regions that could be geolocated. The 1846 malaria surveys analysed were carried out during different seasons, and were reported using different age groupings of the human population. To allow comparison between these, we used the Garki malaria transmission model to convert the malaria prevalence data at each of the 976 locations sampled to a single estimate of transmission intensity E, making use of a seasonality model based on Normalized Difference Vegetation Index (NDVI), temperature and rainfall data. We fitted a Bayesian geostatistical model to E using further environmental covariates and applied Bayesian kriging to obtain smooth maps of E and hence of age-specific prevalence. The product is the first detailed empirical map of variations in malaria transmission intensity that includes Central Africa. It has been validated by expert opinion and in general confirms known patterns of malaria transmission, providing a baseline against which interventions such as insecticidetreated nets programmes and trends in drug resistance can be evaluated. There is considerable geographical variation in the precision of the model estimates and, in some parts of West Africa, the predictions differ substantially from those of other risk maps. The consequent uncertainties indicate zones where further survey data are needed most urgently. Malaria risk maps based on compilations of heterogeneous survey data are highly sensitive to the analytical methodology.keywords entomological inoculation rate, kriging, malaria, markov chain monte carlo, parasite prevalence, vectorial capacity