Modelling the geographical distribution of co-infection risk from single-disease surveys.

BACKGROUND: The need to deliver interventions targeting multiple diseases in a cost-effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co-infection is particularly high. Co-infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data. METHODS: Bayesian geostatistical shared component models (allowing for covariates, disease-specific and shared spatial and non-spatial random effects) are proposed to model the geographical distribution and burden of co-infection risk from single-disease surveys. The ability of the models to capture co-infection risk is assessed on simulated data sets based on multinomial distributions assuming light- and heavy-dependent diseases, and a real data set of Schistosoma mansoni-hookworm co-infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single-disease surveys. The estimated results of co-infection risk, together with independent and multinomial model results, were compared via different validation techniques. RESULTS: The results showed that shared component models result in more accurate estimates of co-infection risk than models assuming independence in settings of heavy-dependent diseases. The shared spatial random effects are similar to the spatial co-infection random effects of the multinomial model for heavy-dependent data. CONCLUSIONS: In the absence of true co-infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co-infection risk from single-disease survey data, especially in settings of heavy-dependent diseases.

Spatial dispersion and characterisation of mosquito breeding habitats in urban vegetable-production areas of Abidjan, Côte d'Ivoire.

Although urban agriculture (UA) in the developing world may enhance nutrition and local economies, it may also lead to higher densities of mosquito breeding sites and, consequently, to increased transmission of malarial parasites. If targeted interventions against malaria vectors are to be successful in urban areas, the habitats that support Anopheles breeding need to be identified and detected. Mosquito breeding sites have recently been characterised, and the factors associated with productive Anopheles habitats identified, in market gardens of Abidjan, Côte d'Ivoire. Two surveys were conducted in seven vegetable-production areas, one towards the end of the rainy season and one during the dry season. A standardized methodology was used for habitat characterisation and the detection of Anopheles larvae and mosquito pupae. Overall, 454 and 559 potential mosquito-breeding sites were recorded in the rainy-season and dry-season surveys, respectively. In the rainy season, Anopheles larvae and mosquito pupae were found in 29.7% and 5.5% of the potential breeding sites, respectively, whereas the corresponding percentages in the dry season were 24.3% and 8.6%. The potential breeding sites in an agricultural zone on the periphery of Abidjan were those least likely to be positive for Anopheles larvae and mosquito pupae whereas 'agricultural trenches' between seedbeds were the sites most likely to be positive. In a spatially-explicit Bayesian multivariate logistic-regression model, although one out of every five such wells was also found to harbour Anopheles larvae, irrigation wells were found to be the least productive habitats, of those sampled, for pupae. In the study area, simple and cost-effective strategies of larval control should be targeted at agricultural trenches, ideally with the active involvement of local stakeholders (i.e. urban farmers and urban agricultural extension services).

The spatial distribution of Anopheles gambiae sensu stricto and An. arabiensis (Diptera: Culicidae) in Mali.

Variations in the biology and ecology and the high level of genetic polymorphism of malaria vectors in Africa highlight the value of mapping their spatial distribution to enhance successful implementation of integrated vector management. The objective of this study was to collate data on the relative frequencies of Anopheles gambiae s.s. and An. arabiensis mosquitoes in Mali, to assess their association with climate and environmental covariates, and to produce maps of their spatial distribution. Bayesian geostatistical logistic regression models were fitted to identify environmental determinants of the relative frequencies of An. gambiae s.s. and An. arabiensis species and to produce smooth maps of their geographical distribution. The frequency of An. arabiensis was positively associated with the normalized difference vegetation index, the soil water storage index, the maximum temperature and the distance to water bodies. It was negatively associated with the minimum temperature and rainfall. The predicted map suggests that, in West Africa, An. arabiensis is concentrated in the drier savannah areas, while An. gambiae s.s. prefers the southern savannah and land along the rivers, particularly the inner delta of Niger. Because the insecticide knockdown resistance (kdr) gene is reported only in An. gambiae s.s. in Mali, the maps provide valuable information for vector control. They may also be useful for planning future implementation of malaria control by genetically manipulated mosquitoes.

Bayesian modelling of geostatistical malaria risk data.

Bayesian geostatistical models applied to malaria risk data quantify the environment-disease relations, identify significant environmental predictors of malaria transmission and provide model-based predictions of malaria risk together with their precision. These models are often based on the stationarity assumption which implies that spatial correlation is a function of distance between locations and independent of location. We relax this assumption and analyse malaria survey data in Mali using a Bayesian non-stationary model. Model fit and predictions are based on Markov chain Monte Carlo simulation methods. Model validation compares the predictive ability of the non-stationary model with the stationary analogue. Results indicate that the stationarity assumption is important because it influences the significance of environmental factors and the corresponding malaria risk maps.