BackgroundThe Anopheles dirus complex includes efficient malaria vectors of the Asian forested zone. Studies suggest ecological and biological differences between the species of the complex but variations within species suggest possible environmental influences. Behavioural variation might determine vector capacity and adaptation to changing environment. It is thus necessary to clarify the species distributions and the influences of environment on behavioural heterogeneity.MethodsA literature review highlights variation between species, influences of environmental drivers, and consequences on vector status and control. The localisation of collection sites from the literature and from a recent project (MALVECASIA) produces detailed species distributions maps. These facilitate species identification and analysis of environmental influences.ResultsThe maps give a good overview of species distributions. If species status partly explains behavioural heterogeneity, occurrence and vectorial status, some environmental drivers have at least the same importance. Those include rainfall, temperature, humidity, shade, soil type, water chemistry and moon phase. Most factors are probably constantly favourable in forest. Biological specificities, behaviour and high human-vector contact in the forest can explain the association of this complex with high malaria prevalence, multi-drug resistant Plasmodium falciparum and partial control failure of forest malaria in Southeast Asia.ConclusionEnvironmental and human factors seem better than species specificities at explaining behavioural heterogeneity. Although forest seems essential for mosquito survival, adaptations to orchards and wells have been recorded. Understanding the relationship between landscape components and mosquito population is a priority in foreseeing the influence of land-cover changes on malaria occurrence and in shaping control strategies for the future.

Visceral leishmaniasis (VL) is a vector-borne disease highly influenced by environmental factors. A model was developed for mapping the distribution and incidence of VL in Gedaref State, eastern Sudan, in relation to different environmental factors. Geographical information systems (GIS) were used to extract and map regression results for environmental variables of 190 villages in Gedaref State, including rainfall, vegetation status, soil type, altitude, distance from river, topography, wetness indexes, and average rainfall estimates. VL incidence in each village was calculated from hospital records. By use of logistic and linear multivariate regression analyses, models were developed to determine which environmental factors explain variability in VL presence and incidence. We found that average rainfall and the altitude were the best predictors of VL incidence. The resulting models were mapped by GIS software predicting both VL presence or absence and incidence at any locality in Gedaref State. The results are discussed in relation to VL control.

In Vietnam, a large proportion of all malaria cases and deaths occurs in the central mountainous and forested part of the country. Indeed, forest malaria, despite intensive control activities, is still a major problem which raises several questions about its dynamics.A large-scale malaria morbidity survey to measure malaria endemicity and identify important risk factors was carried out in 43 villages situated in a forested area of Ninh Thuan province, south central Vietnam. Four thousand three hundred and six randomly selected individuals, aged 10–60 years, participated in the survey. Rag Lays (86%), traditionally living in the forest and practising "slash and burn" cultivation represented the most common ethnic group. The overall parasite rate was 13.3% (range [0–42.3] while Plasmodium falciparum seroprevalence was 25.5% (range [2.1–75.6]). Mapping of these two variables showed a patchy distribution, suggesting that risk factors other than remoteness and forest proximity modulated the human-vector interactions. This was confirmed by the results of the multivariate-adjusted analysis, showing that forest work was a significant risk factor for malaria infection, further increased by staying in the forest overnight (OR= 2.86; 95%CI [1.62; 5.07]). Rag Lays had a higher risk of malaria infection, which inversely related to education level and socio-economic status. Women were less at risk than men (OR = 0.71; 95%CI [0.59; 0.86]), a possible consequence of different behaviour. This study confirms that malaria endemicity is still relatively high in this area and that the dynamics of transmission is constantly modulated by the behaviour of both humans and vectors. A well-targeted intervention reducing the "vector/forest worker" interaction, based on long-lasting insecticidal material, could be appropriate in this environment.

Background: Knowledge on insecticide resistance in target species is a basic requirement to guide insecticide use in malaria control programmes. Malaria transmission in the Mekong region is mainly concentrated in forested areas along the country borders, so that decisions on insecticide use should ideally be made at regional level. Consequently, cross-country monitoring of insecticide resistance is indispensable to acquire comparable baseline data on insecticide resistance.

BackgroundAfter successfully reducing the malaria burden to pre-elimination levels over the past two decades, the national malaria programme in Vietnam has recently switched from control to elimination. However, in forested areas of Central Vietnam malaria elimination is likely to be jeopardized by the high occurrence of asymptomatic and submicroscopic infections as shown by previous reports. This paper presents the results of a malaria survey carried out in a remote forested area of Central Vietnam where we evaluated malaria prevalence and risk factors for infection.MethodsAfter a full census (four study villages = 1,810 inhabitants), the study population was screened for malaria infections by standard microscopy and, if needed, treated according to national guidelines. An additional blood sample on filter paper was also taken in a random sample of the population for later polymerase chain reaction (PCR) and more accurate estimation of the actual burden of malaria infections. The risk factor analysis for malaria infections was done using survey multivariate logistic regression as well as the classification and regression tree method (CART).ResultsA total of 1,450 individuals were screened. Malaria prevalence by microscopy was 7.8% (ranging from 3.9 to 10.9% across villages) mostly Plasmodium falciparum (81.4%) or Plasmodium vivax (17.7%) mono-infections; a large majority (69.9%) was asymptomatic. By PCR, the prevalence was estimated at 22.6% (ranging from 16.4 to 42.5%) with a higher proportion of P. vivax mono-infections (43.2%). The proportion of sub-patent infections increased with increasing age and with decreasing prevalence across villages. The main risk factors were young age, village, house structure, and absence of bed net.ConclusionThis study confirmed that in Central Vietnam a substantial part of the human malaria reservoir is hidden. Additional studies are urgently needed to assess the contribution of this hidden reservoir to the maintenance of malaria transmission. Such evidence will be crucial for guiding elimination strategies.

Health decision-makers working in Africa often need to act for millions of people over large geographical areas on little and uncertain information. Spatial statistical modelling and Bayesian inference have now been used to quantify the uncertainty in the predictions of a regional, environmental risk map for Loa loa (a map that is currently being used as an essential decision tool by the African Programme for Onchocerciasis Control). The methodology allows the expression of the probability that, given the data, a particular location does or does not exceed a predefined high-risk threshold for which a change in strategy for the delivery of the antihelmintic ivermectin is required.

Health decision-makers working in Africa often need to act for millions of people over large geographical areas on little and uncertain information. Spatial statistical modelling and Bayesian inference have now been used to quantify the uncertainty in the predictions of a regional, environmental risk map for Loa loa (a map that is currently being used as an essential decision tool by the African Programme for Onchocerciasis Control). The methodology allows the expression of the probability that, given the data, a particular location does or does not exceed a predefined high-risk threshold for which a change in strategy for the delivery of the antihelmintic ivermectin is required.

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