Quantifying Aggregated Uncertainty in Plasmodium falciparum Malaria Prevalence and Populations at Risk via Efficient Space-Time Geostatistical Joint Simulation

Risk maps estimating the spatial distribution of infectious diseases are required to guide public health policy from local to global scales. The advent of model-based geostatistics (MBG) has allowed these maps to be generated in a formal statistical framework, providing robust metrics of map uncertainty that enhances their utility for decision-makers. In many settings, decision-makers require spatially aggregated measures over large regions such as the mean prevalence within a country or administrative region, or national populations living under different levels of risk. Existing MBG mapping approaches provide suitable metrics of local uncertainty—the fidelity of predictions at each mapped pixel—but have not been adapted for measuring uncertainty over large areas, due largely to a series of fundamental computational constraints. Here the authors present a new efficient approximating algorithm that can generate for the first time the necessary joint simulation of prevalence values across the very large prediction spaces needed for global scale mapping. This new approach is implemented in conjunction with an established model for P. falciparum allowing robust estimates of mean prevalence at any specified level of spatial aggregation. The model is used to provide estimates of national populations at risk under three policy-relevant prevalence thresholds, along with accompanying model-based measures of uncertainty. By overcoming previously unchallenged computational barriers, this study illustrates how MBG approaches, already at the forefront of infectious disease mapping, can be extended to provide large-scale aggregate measures appropriate for decision-makers

Refining the Global Spatial Limits of Dengue Virus Transmission by Evidence-Based Consensus

Background: Dengue is a growing problem both in its geographical spread and in its intensity, and yet current global distribution remains highly uncertain. Challenges in diagnosis and diagnostic methods as well as highly variable national health systems mean no single data source can reliably estimate the distribution of this disease. As such, there is a lack of agreement on national dengue status among international health organisations. Here we bring together all available information on dengue occurrence using a novel approach to produce an evidence consensus map of the disease range that highlights nations with an uncertain dengue status. Methods/Principle Findings: A baseline methodology was used to assess a range of evidence for each country. In regions where dengue status was uncertain, additional evidence types were included to either clarify dengue status or confirm that it is unknown at this time. An algorithm was developed that assesses evidence quality and consistency, giving each country an evidence consensus score. Using this approach, we were able to generate a contemporary global map of national-level dengue status that assigns a relative measure of certainty and identifies gaps in the available evidence

Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax

<p>Abstract</p> <p>Background</p> <p>Temperature is a key determinant of environmental suitability for transmission of human malaria, modulating endemicity in some regions and preventing transmission in others. The spatial modelling of malaria endemicity has become increasingly sophisticated and is now central to the global scale planning, implementation, and monitoring of disease control and regional efforts towards elimination, but existing efforts to model the constraints of temperature on the malaria landscape at these scales have been simplistic. Here, we define an analytical framework to model these constraints appropriately at fine spatial and temporal resolutions, providing a detailed dynamic description that can enhance large scale malaria cartography as a decision-support tool in public health.</p> <p>Results</p> <p>We defined a dynamic biological model that incorporated the principal mechanisms of temperature dependency in the malaria transmission cycle and used it with fine spatial and temporal resolution temperature data to evaluate time-series of temperature suitability for transmission of <it>Plasmodium falciparum </it>and <it>P. vivax </it>throughout an average year, quantified using an index proportional to the basic reproductive number. Time-series were calculated for all 1 km resolution land pixels globally and were summarised to create high-resolution maps for each species delineating those regions where temperature precludes transmission throughout the year. Within suitable zones we mapped for each pixel the number of days in which transmission is possible and an integrated measure of the intensity of suitability across the year. The detailed evaluation of temporal suitability dynamics provided by the model is visualised in a series of accompanying animations.</p> <p>Conclusions</p> <p>These modelled products, made available freely in the public domain, can support the refined delineation of populations at risk; enhance endemicity mapping by offering a detailed, dynamic, and biologically driven alternative to the ubiquitous empirical incorporation of raw temperature data in geospatial models; and provide a rich spatial and temporal platform for future biological modelling studies.</p

Evaluating the impact of the community-based health planning and services initiative on uptake of skilled birth care in Ghana

Background: the Community-based Health Planning and Services (CHPS) initiative is a major government policy to improve maternal and child health and accelerate progress in the reduction of maternal mortality in Ghana. However, strategic intelligence on the impact of the initiative is lacking, given the persistent ?problems of patchy geographical access to care for rural women. This study investigates the impact of proximity to CHPS on facilitating uptake of skilled ?birth care in rural areas.Methods and findings: data from the ?2003 and 2008 Demographic and Health Survey, ? on 4,349 births from 463 rural communities were linked to georeferenced data on health facilities, CHPS and topographic data on national road-networks. Distance to nearest health facility and CHPS was computed using the closest facility functionality in ArcGIS 10.1. Multilevel logistic regression was used to examine the effect of proximity to health facilities and CHPS on use of skilled care at birth, adjusting for relevant predictors and clustering within ?communities.? The results show that a substantial proportion of births continue to occur in communities more than 8 km from both ?health facilities and CHPS. Increases in uptake of skilled birth care are more pronounced where both health ?facilities and CHPS compounds are within 8 km, but not in communities within 8 km of CHPS but lack access to health facilities. Where both health facilities and CHPS are within 8 km, the odds of skilled ?birth care is 16% higher than ?where there is only a health facility within 8km. Conclusion: where CHPS compounds are set up near health facilities, there is improved access to care, demonstrating the facilitatory role of CHPS in stimulating access to better care at birth, in areas where health facilities are accessible. <br/

Protein disulfide isomerase activity is essential for viability and extracellular matrix formation in the nematode Caenorhabditis elegans.

Protein disulfide isomerase (PDI) is a multifunctional protein required for many aspects of protein folding and transit through the endoplasmic reticulum. A conserved family of three PDIs have been functionally analysed using genetic mutants of the model organism Caenorhabditis elegans. PDI-1 and PDI-3 are individually nonessential, whereas PDI-2 is required for normal post-embryonic development. In combination, all three genes are synergistically essential for embryonic development in this nematode. Mutations in pdi-2 result in severe body morphology defects, uncoordinated movement, adult sterility, abnormal molting and aberrant collagen deposition. Many of these phenotypes are consistent with a role in collagen biogenesis and extracellular matrix formation. PDI-2 is required for the normal function of prolyl 4-hydroxylase, a key collagen-modifying enzyme. Site-directed mutagenesis indicates that the independent catalytic activity of PDI-2 may also perform an essential developmental function. PDI-2 therefore performs two critical roles during morphogenesis. The role of PDI-2 in collagen biogenesis can be partially restored following complementation of the mutant with human PDI

Defining the relationship between Plasmodium falciparum parasite rate and clinical disease: statistical models for disease burden estimation

<p>Abstract</p> <p>Background</p> <p>Clinical malaria has proven an elusive burden to enumerate. Many cases go undetected by routine disease recording systems. Epidemiologists have, therefore, frequently defaulted to actively measuring malaria in population cohorts through time. Measuring the clinical incidence of malaria longitudinally is labour-intensive and impossible to undertake universally. There is a need, therefore, to define a relationship between clinical incidence and the easier and more commonly measured index of infection prevalence: the "parasite rate". This relationship can help provide an informed basis to define malaria burdens in areas where health statistics are inadequate.</p> <p>Methods</p> <p>Formal literature searches were conducted for <it>Plasmodium falciparum </it>malaria incidence surveys undertaken prospectively through active case detection at least every 14 days. The data were abstracted, standardized and geo-referenced. Incidence surveys were time-space matched with modelled estimates of infection prevalence derived from a larger database of parasite prevalence surveys and modelling procedures developed for a global malaria endemicity map. Several potential relationships between clinical incidence and infection prevalence were then specified in a non-parametric Gaussian process model with minimal, biologically informed, prior constraints. Bayesian inference was then used to choose between the candidate models.</p> <p>Results</p> <p>The suggested relationships with credible intervals are shown for the Africa and a combined America and Central and South East Asia regions. In both regions clinical incidence increased slowly and smoothly as a function of infection prevalence. In Africa, when infection prevalence exceeded 40%, clinical incidence reached a plateau of 500 cases per thousand of the population <it>per annum</it>. In the combined America and Central and South East Asia regions, this plateau was reached at 250 cases per thousand of the population <it>per annum</it>. A temporal volatility model was also incorporated to facilitate a closer description of the variance in the observed data.</p> <p>Conclusion</p> <p>It was possible to model a relationship between clinical incidence and <it>P. falciparum </it>infection prevalence but the best-fit models were very noisy reflecting the large variance within the observed opportunistic data sample. This continuous quantification allows for estimates of the clinical burden of <it>P. falciparum </it>of known confidence from wherever an estimate of <it>P. falciparum </it>prevalence is available.</p

The risks of malariainfection in Kenya in 2009

BACKGROUND: To design an effective strategy for the control of malaria requires a map of infection and disease risks to select appropriate suites of interventions. Advances in model based geo-statistics and malaria parasite prevalence data assemblies provide unique opportunities to redefine national Plasmodium falciparum risk distributions. Here we present a new map of malaria risk for Kenya in 2009. METHODS: Plasmodium falciparum parasite rate data were assembled from cross-sectional community based surveys undertaken from 1975 to 2009. Details recorded for each survey included the month and year of the survey, sample size, positivity and the age ranges of sampled population. Data were corrected to a standard age-range of two to less than 10 years (PfPR2-10) and each survey location was geo-positioned using national and on-line digital settlement maps. Ecological and climate covariates were matched to each PfPR2-10 survey location and examined separately and in combination for relationships to PfPR2-10. Significant covariates were then included in a Bayesian geostatistical spatial-temporal framework to predict continuous and categorical maps of mean PfPR2-10 at a 1 x 1 km resolution across Kenya for the year 2009. Model hold-out data were used to test the predictive accuracy of the mapped surfaces and distributions of the posterior uncertainty were mapped. RESULTS: A total of 2,682 estimates of PfPR2-10 from surveys undertaken at 2,095 sites between 1975 and 2009 were selected for inclusion in the geo-statistical modeling. The covariates selected for prediction were urbanization; maximum temperature; precipitation; enhanced vegetation index; and distance to main water bodies. The final Bayesian geo-statistical model had a high predictive accuracy with mean error of -0.15% PfPR2-10; mean absolute error of 0.38% PfPR2-10; and linear correlation between observed and predicted PfPR2-10 of 0.81. The majority of Kenya's 2009 population (35.2 million, 86.3%) reside in areas where predicted PfPR2-10 is less than 5%; conversely in 2009 only 4.3 million people (10.6%) lived in areas where PfPR2-10 was predicted to be &gt; or =40% and were largely located around the shores of Lake Victoria. CONCLUSION: Model based geo-statistical methods can be used to interpolate malaria risks in Kenya with precision and our model shows that the majority of Kenyans live in areas of very low P. falciparum risk. As malaria interventions go to scale effectively tracking epidemiological changes of risk demands a rigorous effort to document infection prevalence in time and space to remodel risks and redefine intervention priorities over the next 10-15 years

Prevalence and Predictors of Urinary Tract Infection and Severe Malaria Among Febrile Children Attending Makongoro Health Centre in Mwanza City, North-Western Tanzania.

In malaria endemic areas, fever has been used as an entry point for presumptive treatment of malaria. At present, the decrease in malaria transmission in Africa implies an increase in febrile illnesses related to other causes among underfives. Moreover, it is estimated that more than half of the children presenting with fever to public clinics in Africa do not have a malaria infection. Thus, for a better management of all febrile illnesses among under-fives, it becomes relevant to understand the underlying aetiology of the illness. The present study was conducted to determine the relative prevalence and predictors of P. falciparum malaria, urinary tract infections and bacteremia among under-fives presenting with a febrile illness at the Makongoro Primary Health Centre, North-Western Tanzania. From February to June 2011, a cross-sectional analytical survey was conducted among febrile children less than five years of age. Demographic and clinical data were collected using a standardized pre-tested questionnaire. Blood and urine culture was done, followed by the identification of isolates using in-house biochemical methods. Susceptibility patterns to commonly used antibiotics were investigated using the disc diffusion method. Giemsa stained thin and thick blood smears were examined for any malaria parasites stages. A total of 231 febrile under-fives were enrolled in the study. Of all the children, 20.3% (47/231, 95%CI, 15.10-25.48), 9.5% (22/231, 95%CI, 5.72-13.28) and 7.4% (17/231, 95%CI, 4.00-10.8) had urinary tract infections, P. falciparum malaria and bacteremia respectively. In general, 11.5% (10/87, 95%CI, 8.10-14.90) of the children had two infections and only one child had all three infections. Predictors of urinary tract infections (UTI) were dysuria (OR = 12.51, 95% CI, 4.28-36.57, P < 0.001) and body temperature (40-41 C) (OR = 12.54, 95% CI, 4.28-36.73, P < 0.001). Predictors of P. falciparum severe malaria were pallor (OR = 4.66 95%CI, 1.21-17.8, P = 0.025) and convulsion (OR = 102, 95% CI, 10-996, P = 0.001). Escherichia coli were the common gram negative isolates from urine (72.3%, 95% CI, 66.50-78.10) and blood (40%, 95%CI, and 33.70-46.30). Escherichia coli from urine were 100% resistant to ampicillin, 97% resistant to co-trimoxazole, 85% resistant to augmentin and 32.4% resistant to gentamicin; and they were 100%, 91.2% and 73.5% sensitive to meropenem, ciprofloxacin and ceftriaxone respectively. Urinary tract infection caused by multi drug resistant Escherichia coli was the common cause of febrile illness in our setting. Improvement of malaria diagnosis and its differential diagnosis from other causes of febrile illnesses may provide effective management of febrile illnesses among children in Tanzania

Spatiotemporal mapping of malaria incidence in Sudan using routine surveillance data

Malaria is a serious threat to global health, with over [Formula: see text] of the cases reported in 2020 by the World Health Organization in African countries, including Sudan. Sudan is a low-income country with a limited healthcare system and a substantial burden of malaria. The epidemiology of malaria in Sudan is rapidly changing due to factors including the rapidly developing resistance to drugs and insecticides among the parasites and vectors, respectively; the growing population living in humanitarian settings due to political instability; and the recent emergence of Anopheles stephensi in the country. These factors contribute to changes in the distribution of the parasites species as well as malaria vectors in Sudan, and the shifting patterns of malaria epidemiology underscore the need for investment in improved situational awareness, early preparedness, and a national prevention and control strategy that is updated, evidence based, and proactive. A key component of this strategy is accurate, high-resolution endemicity maps of species-specific malaria. Here, we present a spatiotemporal Bayesian model, developed in collaboration with the Sudanese Ministry of Health, that predicts a fine-scale (1 km [Formula: see text] 1 km) clinical incidence and seasonality profiles for Plasmodium falciparum and Plasmodium vivax across the country. We use monthly malaria case counts for both species collected via routine surveillance between January 2017 and December 2019, as well as a suite of high-resolution environmental covariates to inform our predictions. These epidemiological maps provide a useful resource for strategic planning and cost-effective implementation of malaria interventions, thus informing policymakers in Sudan to achieve success in malaria control and elimination