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  • A sticky situation: the unexpected stability of malaria elimination.

    16 October 2018

    Malaria eradication involves eliminating malaria from every country where transmission occurs. Current theory suggests that the post-elimination challenges of remaining malaria-free by stopping transmission from imported malaria will have onerous operational and financial requirements. Although resurgent malaria has occurred in a majority of countries that tried but failed to eliminate malaria, a review of resurgence in countries that successfully eliminated finds only four such failures out of 50 successful programmes. Data documenting malaria importation and onwards transmission in these countries suggests malaria transmission potential has declined by more than 50-fold (i.e. more than 98%) since before elimination. These outcomes suggest that elimination is a surprisingly stable state. Elimination's 'stickiness' must be explained either by eliminating countries starting off qualitatively different from non-eliminating countries or becoming different once elimination was achieved. Countries that successfully eliminated were wealthier and had lower baseline endemicity than those that were unsuccessful, but our analysis shows that those same variables were at best incomplete predictors of the patterns of resurgence. Stability is reinforced by the loss of immunity to disease and by the health system's increasing capacity to control malaria transmission after elimination through routine treatment of cases with antimalarial drugs supplemented by malaria outbreak control. Human travel patterns reinforce these patterns; as malaria recedes, fewer people carry malaria from remote endemic areas to remote areas where transmission potential remains high. Establishment of an international resource with backup capacity to control large outbreaks can make elimination stickier, increase the incentives for countries to eliminate, and ensure steady progress towards global eradication. Although available evidence supports malaria elimination's stickiness at moderate-to-low transmission in areas with well-developed health systems, it is not yet clear if such patterns will hold in all areas. The sticky endpoint changes the projected costs of maintaining elimination and makes it substantially more attractive for countries acting alone, and it makes spatially progressive elimination a sensible strategy for a malaria eradication endgame.

  • Optimizing investments in malaria treatment and diagnosis

    16 October 2018

    Better targeting of antimalarials to people who need them will maximize the impact of interventions in the private sector.

  • Bayesian geostatistical analysis and prediction of rhodesian human African trypanosomiasis

    16 October 2018

    Background: The persistent spread of Rhodesian human African trypanosomiasis (HAT) in Uganda in recent years has increased concerns of a potential overlap with the Gambian form of the disease. Recent research has aimed to increase the evidence base for targeting control measures by focusing on the environmental and climatic factors that control the spatial distribution of the disease. Objectives:One recent study used simple logistic regression methods to explore the relationship between prevalence of Rhodesian HAT and several social, environmental and climatic variables in two of the most recently affected districts of Uganda, and suggested the disease had spread into the study area due to the movement of infected, untreated livestock. Here we extend this study to account for spatial autocorrelation, incorporate uncertainty in input data and model parameters and undertake predictive mapping for risk of high HAT prevalence in future. Materials and Methods: Using a spatial analysis in which a generalised linear geostatistical model is used in a Bayesian framework to account explicitly for spatial autocorrelation and incorporate uncertainty in input data and model parameters we are able to demonstrate a more rigorous analytical approach, potentially resulting in more accurate parameter and significance estimates and increased predictive accuracy, thereby allowing an assessment of the validity of the livestock movement hypothesis given more robust parameter estimation and appropriate assessment of covariate effects. Results:Analysis strongly supports the theory that Rhodesian HAT was imported to the study area via the movement of untreated, infected livestock from endemic areas. The confounding effect of health care accessibility on the spatial distribution of Rhodesian HAT and the linkages between the disease's distribution and minimum land surface temperature have also been confirmed via the application of these methods. Conclusions: Predictive mapping indicates an increased risk of high HAT prevalence in the future in areas surrounding livestock markets, demonstrating the importance of livestock trading for continuing disease spread. Adherence to government policy to treat livestock at the point of sale is essential to prevent the spread of sleeping sickness in Uganda. © 2010 Wardrop et al.

  • A local space-time kriging approach applied to a national outpatient malaria dataset.

    16 October 2018

    Increases in the availability of reliable health data are widely recognised as essential for efforts to strengthen health-care systems in resource-poor settings worldwide. Effective health-system planning requires comprehensive and up-to-date information on a range of health metrics and this requirement is generally addressed by a Health Management Information System (HMIS) that coordinates the routine collection of data at individual health facilities and their compilation into national databases. In many resource-poor settings, these systems are inadequate and national databases often contain only a small proportion of the expected records. In this paper we take an important health metric in Kenya (the proportion of outpatient treatments for malaria, MP) from the national HMIS database and predict the values of MP at facilities where monthly records are missing. The available MP data were densely distributed across a spatiotemporal domain and displayed second-order heterogeneity. We used three different kriging methodologies to make cross-validation predictions of MP in order to test the effect on prediction accuracy of (a) the extension of a spatial-only to a space-time prediction approach, and (b) the replacement of a globally-stationary with a locally-varying random function model. Space-time kriging was found to produce predictions with 98.4% less mean bias and 14.8% smaller mean imprecision than conventional spatial-only kriging. A modification of space-time kriging that allowed space-time variograms to be recalculated for every prediction location within a spatially-local neighbourhood resulted in a larger decrease in mean imprecision over ordinary kriging (18.3%) although mean bias was reduced less (87.5%).

  • Tree line identification from pollen data: Beyond the limit?

    16 October 2018

    Aim The boreal tree line is a prominent biogeographic feature, the position of which reflects climatic conditions. Pollen is the key sensor used to reconstruct past tree line patterns. Our aims in this study were to investigate pollen-vegetation relationships at the boreal tree line and to assess the success of a modified version of the biomization method that incorporates pollen productivity and dispersal in distinguishing the tree line. Location Northern Canada (307 sites) and Alaska (316 sites). Methods The REVEALS method for estimating regional vegetation composition from pollen data was simplified to provide correction factors to account for differential production and dispersal of pollen among taxa. The REVEALS-based correction factors were used to adapt the biomization method and applied as a set of experiments to pollen data from lake sediments and moss polsters from the boreal tree line. Proportions of forest and tundra predicted from modern pollen samples along two longitudinal transects were compared with those derived from a vegetation map by: (1) a tally of 'correct' versus 'incorrect' assignments using vegetation in the relevant map pixels, and (2) a comparison of the shape and position of north-south forest-cover curves generated from all transect pixels and from pollen data. Possible causes of bias in the misclassifications were assessed. Results Correcting for pollen productivity alone gave fewest misclassifications and the closest estimate of the modern mapped tree line position (Canada, +300km; Alaska, +10km). In Canada success rates were c.40-70% and all experiments over-predicted forest cover. Most corrections improved results over uncorrected biomization; using only lakes improved success rates to c.80%. In Alaska success rates were 70-80% and classification errors were more evenly distributed; there was little improvement over uncorrected biomization. Main conclusions Corrected biomization should improve broad-scale reconstructions of spatial patterns in forest/non-forest vegetation mosaics and across climate-sensitive ecotones. The Canadian example shows this is particularly the case in regions affected by taxa with extremely high pollen productivity (such as Pinus). Improved representation of actual vegetation distribution is most likely if pollen data from lake sediments are used because the REVEALS algorithm is based on the pollen dynamics of lake-based systems. © 2011 Blackwell Publishing Ltd.

  • Identification of specific tree species in ancient semi-natural woodland from digital aerial sensor imagery

    16 October 2018

    Remote sensing has great potential as a source of information on tree species. The classification approaches used commonly to extract species information from remotely sensed imagery typically aim to optimize the overall accuracy of species identification, a target which need not satisfy the requirements of a particular user. Often users are interested in a specific species or subset of species, and these may not be accurately identified in a conventional classification. Here, a two-phase classification approach was used to map specific species from aerial sensor imagery of an ancient British woodland. Particular attention was focused on the identification of sycamore since this is displacing the native ash and information on its distribution would enhance basic understanding and management activities. The results show that the classification approach can be adapted to focus on a specific species of interest and used to increase classification accuracy significantly. For example, sycamore was classified to a low accuracy when a conventional approach to classification with a neural network was used (46.6-63.6%, depending on perspective), but the adoption of the two-phase approach increased its accuracy significantly (82.3-93.3%). The results demonstrate the ability to map specific class(es) of interest accurately from remotely sensed imagery. The approach used also highlights the ability to tailor an analysis to the specific requirements of the ecological study in hand and is of broad applicability. © 2005 by the Ecological Society of America.

  • Coverage of malaria protection in pregnant women in sub-Saharan Africa: A synthesis and analysis of national survey data

    16 October 2018

    Background: Insecticide-treated nets and intermittent preventive treatment with sulfadoxine-pyrimethamine are recommended for the control of malaria during pregnancy in endemic areas in Africa, but there has been no analysis of coverage data at a subnational level. We aimed to synthesise data from national surveys about these interventions, accounting for disparities in malaria risk within national borders. Methods: We extracted data for specific strategies for malaria control in pregnant women from national malaria policies from endemic countries in Africa. We identified the most recent national household cluster-sample surveys recording intermittent preventive treatment with sulfadoxine-pyrimethamine and use of insecticide-treated nets. We reconciled data to subnational administrative units to construct a model to estimate the number of pregnant women covered by a recommended intervention in 2007. Findings: 45 (96%) of 47 countries surveyed had a policy for distribution of insecticide-treated nets for pregnant women; estimated coverage in 2007 was 4·7 million (17%) of 27·7 million pregnancies at risk of malaria in 32 countries with data. 39 (83%) of 47 countries surveyed had an intermittent preventive treatment policy; in 2007, an estimated 6·4 million (25%) of 25·6 million pregnant women received at least one dose of treatment and 19·8 million (77%) visited an antenatal clinic (31 countries). Estimated coverage was lowest in areas of high-intensity transmission of malaria. Interpretation: Despite success in a few countries, coverage of insecticide-treated nets and intermittent preventive treatment in pregnant African women is inadequate; increased efforts towards scale-up are needed. Funding: The Malaria in Pregnancy Consortium and Wellcome Trust. © 2011 Elsevier Ltd.

  • Investigating spatial structure in specific tree species in ancient semi-natural woodland using remote sensing and marked point pattern analysis

    16 October 2018

    Remote sensing classification has the potential to provide important information, such as tree species distribution maps, to ecologists, at a range of spatial and temporal scales. However, standard classification procedures often fail to provide the high accuracies required for many ecological applications. Previously, a modified remote sensing classification technique was used to provide very high classification accuracies for one or two classes (e.g. species) of interest. The aim of this paper was to demonstrate that the output from the method can be suitable for spatial ecological analyses, and to provide a generic simulation framework for assessing the adequacy of any given remote sensing classification for such analyses. Marked point pattern analysis (MPPA) was applied to tree species distribution data obtained for sycamore Acer pseudoplatanus and ash Fraxinus excelsior from a 400 ha ancient semi-natural woodland in southern England using the modified remote sensing classification method to test several hypotheses of ecological interest relating to the spatial distribution and interaction of these species. Monte Carlo simulation methods were then used to evaluate the data and data quality requirements of the MPPA to check that the classified tree species maps for sycamore and ash were adequate. Using the combined method the spatial distributions for sycamore and ash were found to be aggregated and inter-dependent at a range of spatial scales. Together, the remote sensing classification and simulation approaches provide the basis for exploiting more fully the potential of remote sensing to provide information of value to ecologists. © Ecography.