Bayesian geostatistics in health cartography: the perspective of malaria

Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision.     

Temperature and malaria trends in highland East Africa

There has been considerable debate on the existence of trends in climate in the highlands of East Africa and hypotheses about their potential effect on the trends in malaria in the region. We apply a new robust trend test to mean temperature time series data from three editions of the University of East Anglia's Climatic Research Unit database (CRU TS) for several relevant locations. We find significant trends in the data extracted from newer editions of the database but not in the older version for periods ending in 1996. The trends in the newer data are even more significant when post-1996 data are added to the samples. We also test for trends in the data from the Kericho meteorological station prepared by Omumbo et al. We find no significant trend in the 1979-1995 period but a highly significant trend in the full 1979-2009 sample. However, although the malaria cases observed at Kericho, Kenya rose during a period of resurgent epidemics (1994-2002) they have since returned to a low level. A large assembly of parasite rate surveys from the region, stratified by altitude, show that this decrease in malaria prevalence is not limited to Kericho.     

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

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.     

BiP-binding sequences in HIV gp160. Implications for the binding specificity of bip.

BiP, a resident endoplasmic reticulum member of the HSP70 family of molecular chaperones, associates transiently with a wide variety of newly synthesized exocytotic proteins. In addition to immunoglobulin heavy and light chains, the first natural substrates identified for BiP, a number of viral polypeptides including the human immunodeficiency virus type 1 envelope glycoprotein gp160 interact with BiP during their passage through the endoplasmic reticulum. We have used a computer algorithm developed to predict BiP-binding sites within protein primary sequences to identify sites within gp160 that might mediate its association with BiP. Analysis of the ability of 22 synthetic heptapeptides corresponding to predicted binding sites to stimulate the ATPase activity of BiP or to compete with an unfolded polypeptide for binding to BiP indicated that about half of them are indeed recognized by the chaperone. All of the confirmed binding sites are localized within conserved regions of gp160, suggesting a conserved role for BiP in the folding of gp160. Information on the characteristics of confirmed BiP-binding peptides gained in this and previous studies has been utilized to improve the predictive power of the BiP Score algorithm and to investigate the differences in peptide binding specificities of HSP70 family members.     

Defining the Geographical Range of the Plasmodium knowlesi Reservoir

Background

The simian malaria parasite, Plasmodium knowlesi, can cause severe and fatal disease in humans yet it is rarely included in routine public health reporting systems for malaria and its geographical range is largely unknown. Because malaria caused by P. knowlesi is a truly neglected tropical disease, there are substantial obstacles to defining the geographical extent and risk of this disease. Information is required on the occurrence of human cases in different locations, on which non-human primates host this parasite and on which vectors are able to transmit it to humans. We undertook a systematic review and ranked the existing evidence, at a subnational spatial scale, to investigate the potential geographical range of the parasite reservoir capable of infecting humans.

Methodology/Principal Findings

After reviewing the published literature we identified potential host and vector species and ranked these based on how informative they are for the presence of an infectious parasite reservoir, based on current evidence. We collated spatial data on parasite occurrence and the ranges of the identified host and vector species. The ranked spatial data allowed us to assign an evidence score to 475 subnational areas in 19 countries and we present the results on a map of the Southeast and South Asia region.

Conclusions/Significance

We have ranked subnational areas within the potential disease range according to evidence for presence of a disease risk to humans, providing geographical evidence to support decisions on prevention, management and prophylaxis. This work also highlights the unknown risk status of large parts of the region. Within this unknown category, our map identifies which areas have most evidence for the potential to support an infectious reservoir and are therefore a priority for further investigation. Furthermore we identify geographical areas where further investigation of putative host and vector species would be highly informative for the region-wide assessment.