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AI technology is capturing the African imaginations as a gateway to progress and prosperity. There is a growing interest in AI by different actors across the continent including scientists, researchers, humanitarian and aid organizations, academic institutions, tech start-ups, and media organizations. Several African states are looking to adopt AI technology to capture economic growth and development opportunities. On the other hand, African researchers highlight the gap in regulatory frameworks and policies that govern the development of AI in the continent. They argue that this could lead to AI technology exacerbating problems of inequalities and injustice in the continent. However, most of the literature on AI ethics is biased toward Euro-American perspectives and lack the understanding of how AI development is apprehended in the Global South, and particularly Africa. Drawing on the case study of the first African Master's in Machine Intelligence program, this paper argues for looking beyond the question of ethics in AI and examining AI governance issues through the analytical lens of the raciality of computing and the political economy of technoscience to understand AI development in Africa. By doing so, this paper seeks a different theorization for AI ethics from the South that is based on lived experiences of those in the margins and avoids the framings of technological futures that simplistically pathologize or celebrate Africa. [ABSTRACT FROM AUTHOR]

The Tracking Algorithm for Mesoscale Convective Systems (TAMS) is a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems (MCSs). TAMS was initially developed to analyze MCSs over Africa and their relation to African easterly waves using satellite-derived datasets. This paper describes TAMS v2.0, an open-source MCS tracking and classifying Python-based package that can be used to study both observed and simulated MCSs. Each step of the algorithm is described with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this MCS tracker is its support for unstructured grids in the MCS identification stage and grid-independent tracking of MCSs, enabling application across various native modeling grids and satellite-derived products. A description of the available settings and helper functions is also provided. Finally, we share some of the current development goals for TAMS. [ABSTRACT FROM AUTHOR]

With the increase of Internet of Things (IoT) and threats on Information Systems (IS) resources, it is urgent for Africa countries to stop being spectator on cyber security and begin to play a more active role. This paper aimed at helping Africa country governments and organizations to understand security challenges that they may face, and propose solutions. The paper described security investment factors, proposed a survey of IoT Worldwide and in Africa, and details as well a survey of cryptography algorithms (symmetric and asymmetric). Indeed, information security, in particular cryptography, seems alien to Africa IT professionals. This lack stems from a deep ignorance about this wonderful field of cryptography and its importance in the system. The authors believe that this trend must be reversed as soon as possible, as Africa strives opening up to the world. Henceforth, this paper presented a comprehensive introductory survey on cryptographic algorithms especially tailored for Africans. [ABSTRACT FROM AUTHOR]

• Discretionary or ex gratia payouts are widespread in parametric insurance programs. • Payouts attempt to secure social license to operate despite contractual basis risk. • Discretionary payouts obscure and entrench insurers' market power. • Attempts at conciliation often stoke further demands from aggrieved collectivities. • Geospatial monitoring alone cannot resolve questions of post-disaster distribution. The past decade has seen the dramatic proliferation of geospatial tools to remotely monitor, measure, and transfer weather and climate risk, particularly in the Global South. These "parametric" tools are calculative market devices used to commodify risk and facilitate its distantiated exchange via insurance markets. Yet the environmental estimates they generate are frequently wrong, sometimes grossly so. This paper investigates what happens after calculative market devices fail, analyzing so-called ex gratia ("from grace") practices in which insurers make payouts to aggrieved clients even when none are due under contractual terms. Though these payments are commonly deployed to dispel discontent, these "public secrets" of the industry have gone unnoticed in insurance scholarship. This paper documents numerous ex gratia payouts in parametric programs across Africa, disputing the myth of autonomous insurance markets that function according to formal, transparent rules administered by dispassionate calculative devices. Instead, these "discretionary" monetary transfers reintroduce human decision making in informal, black-boxed ways, cloaking the inadequacy of the parametric contracts they shore up. Far from incidental, I argue that these practices have been constitutive of the parametric insurance market's persistence and expansion. Though ex gratia payments sometimes give rise to negotiations over indemnification with aggrieved collectivities, these unfold on insurers' terms, informalizing power and delegitimizing complaint. The growing emphasis on parametric insurance as a tool for climate adaptation makes it all the more important to excavate the mechanics and politics of ex gratia, and to relinquish expectations that geospatial algorithms alone can resolve questions of post-disaster redistribution. [ABSTRACT FROM AUTHOR]

Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists "salt-and-pepper phenomenon" in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification. [ABSTRACT FROM AUTHOR]

This paper extends the work of Dinku and Anagnostou overland rain retrieval algorithm for use with Special Sensor Microwave Imager (SSM/I) observations. In Dinku and Anagnostou, Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) rainfall estimates were used to calibrate TRMM Microwave Imager (TMI) retrieval. Regional differences in PR-based TMI calibration were investigated by testing the algorithm over four geographic regions, consisting of Africa, northern South America (containing the Amazon basin), the continental United States, and south Asia. In this paper the performance of Dinku and Anagnostou's technique applied on SSM/I data over three of these regions (Africa, Amazon, and South Asia) is demonstrated. Two approaches are investigated for using PR rainfall products to calibrate the algorithm parameters. In the first approach, TMI channels are remapped to the spatial resolutions of the corresponding SSM/I channels; then, PR is used to calibrate the rain retrieval on the remapped TMI data. In the second approach, the PR-based TMI algorithm calibration is performed at a coarser (0.25°) resolution. To assess the quality of algorithm estimates with respect to PR, rainfall fields derived from Dinku and Anagnostou, applied to SSM/I observations (using parameters determined from both approaches), are compared with matched (within ±15 min of the satellites' overpass time difference) PR surface rain rates. Calibration data come from the wet seasons (January–March) of 2000 and 2001. To assess the quality of the estimates with respect to PR, data from a 5-month period (December–April) of 2002, 2003, and 2004 are used. In comparison with the latest version of the Goddard profiling (GPROF) algorithm rain estimates, the current algorithm shows significant improvements in terms of both bias and random error reduction. The paper also shows that rain estimation based on TMI observations is associated with lower error statistics in comparison with the corresponding SSM/I retrievals. [ABSTRACT FROM AUTHOR]

Distribution networks in Southern Africa and elsewhere are witnessing an unprecedented growth of consumerside distributed generation (DG) courtesy of governmental interventions to maximise the utilisation of renewable energy resources through low-carbon grid-edge technologies. To deal with the increasing adoption of consumer-side DG, distribution network operators need to conduct technical studies to foster an understanding of the benefits and impacts of DG and the hosting capacity (HC) of existing distribution networks. This will aid the implementation of measures to manage grid exports. Using a distribution network in Namibia as a case study, this paper presents an algorithm for assessing the HC of consumer-side DG in existing distribution networks that are situated in areas anticipating high and uniform uptake of DG. The algorithm is a hybrid of deterministic and probabilistic methods. The uniqueness of the algorithm is the concept of calculating monthly HC. The algorithm was tested on a real existing residential distribution network and the results confirmed that HC varies monthly. However, the practical implementation of monthly HC requires upgrades to existing inverter technology, which currently contains a single export limit functionality. This opens the possibility to drive innovation in the inverter technology to develop a date-based multiple export limit functionality. [ABSTRACT FROM AUTHOR]

The research focus of an institution indicates the academic strength of the institution. This academic strength goes a long way to determine the quality of academic product expected from such an institution. To this effect, this study aims at analyzing the research publication strength of 70 Nigerian universities that are visible in Scopus database for Computer Science from inception to 22 December 2017. They include 35 federal, 27 state and 8 private universities. This study is divided into two phases; the first phase consists of six major steps in a waterfall model and the second is carried out by adopting a knowledge discovery model using a clustering algorithm for pattern discovery in an unstructured data model. The data source for the analysis is the Scopus indexing database recommended by the Times Higher Education for international ranking of higher institutions. From the first phase, the predominant outlet for each institution was discovered alongside the percentage of publications in the dominant outlet and the number of publications by the institution within the database over the period of the analysis. In the second phase, the predominant areas of research of each institution within the field of Computer Science was determined. In particular, it was observed that such areas as deep learning and data science are under researched. The findings also revealed networking as the most published area in Nigerian universities, and a number of Nigerian computer science scholars engage in application research which link Computer Science with other disciplines with Nigeria and Africa as case studies. The findings from this research can support postgraduate students' decision in making the right choice of institution based on their research interest. It will also serve as an eye-opener for industries in promoting a result-oriented university-industry partnership. [ABSTRACT FROM AUTHOR]

There are increasing efforts to use climate model output for adaptation planning, but meanwhile there is often limited understanding of how models represent regional climate. Here we analyze the simulation in global coupled climate models of a key rainfall-generating mechanism over southern Africa: tropical temperate troughs (TTTs). An image-processing algorithm is applied to outgoing longwave radiation data from satellites and models to create TTT event sets. All models investigated produce TTTs with similar circulation features to observed. However, there are large differences among models in the number, intensity, and preferred longitude of events. Five groups of models are identified. The first group generates too few TTTs, and relatively dry conditions over southern Africa compared to other models.Asecond group generates more TTTs and wet biases. The contrast between these two groups suggests that the number of TTTs could explain intermodel variations in climatological rainfall. However, there is a third group of models that simulate up to 92% more TTTs than observed, but do not have large rainfall biases, as each TTT event is relatively weak. Finally, there are a further two groups that concentrate TTTs over the subcontinent or the ocean, respectively. These distinctions between models are associated with the amount of convective activity in the Congo Basin, the magnitude of moisture fluxes into southern Africa, and the degree of zonal asymmetry in upper-level westerly flow. Model development focused on tropical convection and the representation of orography is needed for improved simulation of TTTs, and therefore southern African rainfall. [ABSTRACT FROM AUTHOR]

Background: Algorithms to diagnose gambiense human African trypanosomiasis (HAT, sleeping sickness) are often complex due to the unsatisfactory sensitivity and/or specificity of available tests, and typically include a screening (serological), confirmation (parasitological) and staging component. There is insufficient evidence on the relative accuracy of these algorithms. This paper presents estimates of the accuracy of five algorithms used by past Médecins Sans Frontières programmes in the Republic of Congo, Southern Sudan and Uganda. Methodology and Principal Findings: The sequence of tests in each algorithm was programmed into a probabilistic model, informed by distributions of the sensitivity, specificity and staging accuracy of each test, constructed based on a literature review. The accuracy of algorithms was estimated in a baseline scenario and in a worst-case scenario introducing various near worst-case assumptions. In the baseline scenario, sensitivity was estimated as 85-90% in all but one algorithm, with specificity above 99.9% except for the Republic of Congo, where CATT serology was used as independent confirmation test: here, positive predictive value (PPV) was estimated at <50% in realistic active screening prevalence scenarios. Furthermore, most algorithms misclassified about one third of true stage 1 cases as stage 2, and about 10% of true stage 2 cases as stage 1. In the worst-case scenario, sensitivity was 75-90% and PPV no more than 75% at 1% prevalence, with about half of stage 1 cases misclassified as stage 2. Conclusions: Published evidence on the accuracy of widely used tests is scanty. Algorithms should carefully weigh the use of serology alone for confirmation, and could enhance sensitivity through serological suspect follow-up and repeat parasitology. Better evidence on the frequency of low-parasitaemia infections is needed. Simulation studies should guide the tailoring of algorithms to specific scenarios of HAT prevalence and availability of control tools. [ABSTRACT FROM AUTHOR]

A concentration of surface mass has a distinct, localized signature in Gravity Recovery and Climate Experiment (GRACE) K-band range rate (KBRR) data. This fact is exploited in the regional solutions for mass concentration parameters (mascons) made at the Goddard Space Flight Center (GSFC). In this paper we explore an experimental set of regionally constrained mascon blocks over Southern Africa where a system of 1.25° × 1.5° and 1.5° × 1.5° blocks has been designed. The blocks are divided into hydrological regions based on drainage patterns of the largest river basins, and are constrained in different ways. We show that the use of regional constraints, when solving mascon parameters of different hydrological regions independently, yields more detail and variation than comparable spherical harmonic solutions and mascon solutions using isotropic constraints. We validate our results over Lake Malawi with water level from altimetry. Results show that weak constraints across regions in addition to intra-regional constraints are necessary, to reach reasonable mass variations. [ABSTRACT FROM AUTHOR]

In the challenging social/geographical environment in Africa, how to design a robust network while tolerating the traffic imbalance is studied in this paper. We propose the Africa TWO network which connects all countries in Africa by employing two concentric rings. We investigate an Integer Linear Programming model and develop heuristic algorithms to deal with some special issues in Africa, such as a large desert in North Africa, high-speed network connectivity to the rest of the world through a few existing undersea fiber cables, and traffic imbalance caused by the differential populations of the various countries. To further exploit the properties of the designed network, we propose Little-Arc-First (LAF) routing algorithm, First-Matching (FM) wavelength assignment, and Sharing Peer Protection (SPP) scheme. The proposed algorithms LAF and FM are more efficient than existing algorithms. Sharing Peer Protection (SPP) scheme achieves high reliability in a cost-effective manner. We also address how to upgrade the network to achieve longer network lifetime for overall economic benefit. Our simulation experiments demonstrate that the concentric two-ring network is a natural candidate not only for the African continent, but can also be extended to other countries and regions. [ABSTRACT FROM AUTHOR]

At the time the world is attempting to get over the damage caused by the COVID-19 spread, the monkeypox virus threatens to evolve into a global pandemic. Human monkeypox was first recognized in Africa and has recently emerged in 103 countries outside Africa. However, monkeypox diagnosis in an early stage is difficult because of the similarity between it, chickenpox, cowpox and measles. In some cases, computer-assisted detection of monkeypox lesions can be helpful for quick identification of suspected cases. Infected and uninfected cases have added to a growing dataset that is publicly accessible and may be utilized by machine and deep learning to predict the suspected cases at an early stage. Motivated by this, a diagnostic framework to categorize the cases of patients into four categories (i.e., normal, monkeypox, chicken pox and measles) is proposed. The diagnostic framework is a hybridization of pre-trained Convolution Neural Network (CNN) models, machine learning classifiers and a metaheuristic optimization algorithm. The hyperparameters of the five pre-trained models (i.e., VGG19, VGG16, Xception, MobileNet and MobileNetV2) are optimized using a Harris Hawks Optimizer (HHO) metaheuristic algorithm. After that, the features can be extracted from the feature extraction and reduction layers. These features are classified using seven machine learning models (i.e., Random Forest, AdaBoost, Histogram Gradient Boosting, Gradient Boosting, Support Vector Machine, Extra Trees and KNN). For each classifier, 10-fold cross-validation is used to train and test the classifiers on the features and the weighted average performance metrics are reported. The predictions from the pre-trained model and machine learning classifiers are then processed using majority voting. This study conducted the experiments on two datasets (i.e., Monkeypox Skin Images Dataset (MSID) and Monkeypox Images Dataset (MPID)). MSID dataset values 97.67%, 95.19%, 97.96%, 95.11%, 96.58%, 95.10%, 90.93% and 96.65% are achieved concerning accuracy, sensitivity, specificity, PPV, BAC, F1, IoU and ROC, respectively. While for the MPID dataset, values of 97.51%, 94.84%, 94.48%, 94.96%, 96.66%, 94.88%, 90.45% and 96.69% are achieved concerning accuracy, sensitivity, specificity, PPV, BAC, F1, IoU and ROC, respectively. [ABSTRACT FROM AUTHOR]

The present paper analyses the problem of the sustainable design and development of urban storm-water systems in response to the climate change and anthropic modification. In particular, the work focuses on the case of the African areas that are characterized by high vulnerability towards these issues. A disaggregation procedure is adopted to estimate the Intensity-Duration-Frequency (IDF) due to climate change starting from daily rainfall data. An integrated approach is used which combines an optimization algorithm (Harmony Search) and a hydraulic simulator to carry out the optimal design of intervention strategies with low economic impact. The methodology is described and discussed with reference to a case study located in the city of Dar es Salaam (Tanzania). [ABSTRACT FROM AUTHOR]

Many languages in Africa are written using Latin-based scripts, but often with extra diacritics (e.g. dots below in Igbo: $${\d i}, {\d o}, {\d u}$$) or modifications to the letters themselves (e.g. open vowels 'e' and 'o' in Lingala: ɛ, ɔ). While it is possible to render these characters accurately in Unicode, oftentimes keyboard input methods are not easily accessible or are cumbersome to use, and so the vast majority of electronic texts in many African languages are written in plain ASCII. We call the process of converting an ASCII text to its proper Unicode form unicodification. This paper describes an open-source package which performs automatic unicodification, implementing a variant of an algorithm described in previous work of De Pauw, Wagacha, and de Schryver. We have trained models for more than 100 languages using web data, and have evaluated each language using a range of feature sets. [ABSTRACT FROM AUTHOR]

There is an important, long-standing debate regarding the universality vs. specificity of trauma-related mental health symptoms in socio-culturally and linguistically diverse population groups, such as refugees and asylum seekers. Network theory, an emerging development in the field of psychological science, provides a novel data analytic methodology to evaluate and empirically examine long-standing questions about the structure and function of posttraumatic stress symptoms. We sought to empirically model the functional network of posttraumatic stress symptoms among East African refugees who survived multiple potentially traumatic events. A sample of 148 Sudanese and Eritrean male asylum seekers (M (SD)age = 32.60(7.13) were recruited from the community in Israel. The nature and function(s) of posttraumatic symptoms (Harvard Trauma Questionnaire) were modeled using regularized partial correlation models to derive a network of symptoms. Spinglass and exploratory graph analysis walktrap algorithms were then used to identify functional "circuits of symptoms" or clusters of nodes within the network. Analyses revealed a functional symptom circuitry that shares features with the predominant western model of posttraumatic stress disorder; as well as unique functional clusters of symptoms inconsistent with nosology and symptomatology observed in studies of Western populations. Findings may have important implications for theory, classification, assessment, candidate mechanisms that may drive and maintain posttraumatic stress, and in turn may inform prevention or treatment for socio-culturally diverse forcibly displaced population groups. [ABSTRACT FROM AUTHOR]

The article offers information on the challenging scientific problems faced by humanity. Topics discussed include information on the efforts to empower Africans to join the global neuroscience community; discussions on the BRAIN initiative in the U.S. and the Human Brain Project in Europe; and the information on the limiting factor for computational neuroscience development in Africa.

Malaria remains among the world’s deadliest diseases, and control efforts depend critically on the availability of effective diagnostic tools, particularly for the identification of asymptomatic infections, which play a key role in disease persistence and may account for most instances of transmission but often evade detection by current screening methods. Research on humans and in animal models has shown that infection by malaria parasites elicits changes in host odors that influence vector attraction, suggesting that such changes might yield robust biomarkers of infection status. Here we present findings based on extensive collections of skin volatiles from human populations with high rates of malaria infection in Kenya. We report broad and consistent effects of malaria infection on human volatile profiles, as well as significant divergence in the effects of symptomatic and asymptomatic infections. Furthermore, predictive models based on machine learning algorithms reliably determined infection status based on volatile biomarkers. Critically, our models identified asymptomatic infections with 100% sensitivity, even in the case of low-level infections not detectable by microscopy, far exceeding the performance of currently available rapid diagnostic tests in this regard. We also identified a set of individual compounds that emerged as consistently important predictors of infection status. These findings suggest that volatile biomarkers may have significant potential for the development of a robust, noninvasive screening method for detecting malaria infections under field conditions. [ABSTRACT FROM AUTHOR]

Objective: We sought to classify causes of stillbirth for six low-middle-income countries using a prospectively defined algorithm.Design: Prospective, observational study.Setting: Communities in India, Pakistan, Guatemala, Democratic Republic of Congo, Zambia and Kenya.Population: Pregnant women residing in defined study regions.Methods: Basic data regarding conditions present during pregnancy and delivery were collected. Using these data, a computer-based hierarchal algorithm assigned cause of stillbirth. Causes included birth trauma, congenital anomaly, infection, asphyxia, and preterm birth, based on existing cause of death classifications and included contributing maternal conditions.Main Outcome Measures: Primary cause of stillbirth.Results: Of 109 911 women who were enrolled and delivered (99% of those screened in pregnancy), 2847 had a stillbirth (a rate of 27.2 per 1000 births). Asphyxia was the cause of 46.6% of the stillbirths, followed by infection (20.8%), congenital anomalies (8.4%) and prematurity (6.6%). Among those caused by asphyxia, 38% had prolonged or obstructed labour, 19% antepartum haemorrhage and 18% pre-eclampsia/eclampsia. About two-thirds (67.4%) of the stillbirths did not have signs of maceration.Conclusions: Our algorithm determined cause of stillbirth from basic data obtained from lay-health providers. The major cause of stillbirth was fetal asphyxia associated with prolonged or obstructed labour, pre-eclampsia and antepartum haemorrhage. In the African sites, infection also was an important contributor to stillbirth. Using this algorithm, we documented cause of stillbirth and its trends to inform public health programs, using consistency, transparency, and comparability across time or regions with minimal burden on the healthcare system.Tweetable Abstract: Major causes of stillbirth are asphyxia, pre-eclampsia and haemorrhage. Infections are important in Africa. [ABSTRACT FROM AUTHOR]

Background: A functional national health research system (NHRS) is crucial in strengthening a country's health system to promote, restore and maintain the health status of its population. Progress towards the goal of universal health coverage in the post-2015 sustainable development agenda will be difficult for African countries without strengthening of their NHRS to yield the required evidence for decision-making. This study aims to develop a barometer to facilitate monitoring of the development and performance of NHRSs in the African Region of WHO.Methods: The African national health research systems barometer algorithm was developed in response to a recommendation of the African Advisory Committee for Health Research and Development of WHO. Survey data collected from all the 47 Member States in the WHO African Region using a questionnaire were entered into an Excel spreadsheet and analysed. The barometer scores for each country were calculated and the performance interpreted according to a set of values ranging from 0% to 100%.Results: The overall NHRS barometer score for the African Region was 42%, which is below the average of 50%. Among the 47 countries, the average NHRS performance was less than 20% in 10 countries, 20-40% in 11 countries, 41-60% in 16 countries, 61-80% in nine countries, and over 80% in one country. The performance of NHRSs in 30 (64%) countries was below 50%.Conclusion: An African NHRS barometer with four functions and 17 sub-functions was developed to identify the gaps in and facilitate monitoring of NHRS development and performance. The NHRS scores for the individual sub-functions can guide policymakers to locate sources of poor performance and to design interventions to address them. [ABSTRACT FROM AUTHOR]

The outbreak of Ebola virus disease in West Africa is the largest ever recorded. Numerous treatment alternatives for Ebola have been considered, including widely available repurposed drugs, but initiation of enrollment into clinical trials has been limited. The proposed trial is an adaptive platform design. Multiple agents and combinations will be investigated simultaneously. Additionally, new agents may enter the trial as they become available, and failing agents may be removed. In order to accommodate the many possible agents and combinations, a critical feature of this design is the use of response adaptive randomization to assign treatment regimens. As the trial progresses, the randomization ratio evolves to favor the arms that are performing better, making the design also suitable for all-cause pandemic preparedness planning. The study was approved by US and Sierra Leone ethics committees, and reviewed by the US Food and Drug Administration. Additionally, data management, drug supply lines, and local sites were prepared. However, in response to the declining epidemic seen in February 2015, the trial was not initiated. Sierra Leone remains ready to rapidly activate the protocol as an emergency response trial in the event of a resurgence of Ebola. (ClinicalTrials.gov Identifier: NCT02380625.) In summary, we have designed a single controlled trial capable of efficiently identifying highly effective or failing regimens among a rapidly evolving list of proposed therapeutic alternatives for Ebola virus disease and to treat the patients within the trial effectively based on accruing data. Provision of these regimens, if found safe and effective, would have a major impact on future epidemics by providing effective treatment options. [ABSTRACT FROM AUTHOR]

We report on the first systematic ground-based validation of the US Air Force Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) night lights imagery to detect rural electrification in the developing world. Drawing upon a unique survey of villages in Senegal and Mali, this study compares night-time light output from the DMSP-OLS against ground-based survey data on electricity use in 232 electrified villages and additional administrative data on 899 unelectrified villages. The analysis reveals that electrified villages are consistently brighter than unelectrified villages across annual composites, monthly composites, and a time series of nightly imagery. Electrified villages appear brighter because of the presence of streetlights, and brighter villages tend to have more streetlights. By contrast, the correlation of light output with household electricity use and access is low. We further demonstrate that a detection algorithm using data on night-time light output and the geographic location of settlements can accurately classify electrified villages. This research highlights the potential to use night lights imagery for the planning and monitoring of ongoing efforts to connect the 1.4 billion people who lack electricity around the world. [ABSTRACT FROM AUTHOR]

Daily precipitation retrievals from three algorithms [the Tropical Rainfall Measuring Mission 3B42 rain product (TRMM-3B42), the Climate Prediction Center morphing technique (CMORPH), and the second version (RFEv2) of the Famine Early Warning System (FEWS)] and CloudSat retrievals of cloud liquid water, ice amount, and cloud fraction are used to document the cloud structures associated with rainfall location and intensity in the West African monsoon. The different rainfall retrieval approaches lead to contrasting cloud sensitivities between all three algorithms most apparent in the onset period of June and July. During the monsoon preonset phase, CMORPH produces a precipitation peak at around 12°N associated with upper-level cirrus clouds, while FEWS and TRMM both produce rainfall maxima collocated with the tropospheric-deep convective cloud structures at 4°-6°N. In July similar relative displacements of the rainfall maxima are observed. Conditional sampling of several hundred convection systems proves that, while upper-level cirrus is advected northward relative to the motion of the convective system cores, the reduced cover and water content of lower-tropospheric clouds in the northern zone could be due to signal attenuation as the systems there appear to be more intense, producing higher ice water contents. Thus, while CMORPH may overestimate rainfall in the northern zone due to its reliance on cloud ice, TRMM and FEWS are likely underestimating precipitation in this zone, potentially due to the use of infrared based products in TRMM and FEWS when microwave is not available. Mapping the CloudSat retrievals as a function of rain rate confirms the greater sensitivity of CMORPH to ice cloud and indicates that high-intensity rainfall events are associated with systems that are deeper and of a greater spatial scale. [ABSTRACT FROM AUTHOR]

Six satellite-based rainfall estimates (SRFE)-namely, Climate Prediction Center (CPC) morphing technique (CMORPH), the Rainfall Estimation Algorithm, version 2 (RFE2.0), Tropical Rainfall Measuring Mission (TRMM) 3B42, Goddard profiling algorithm, version 6 (GPROF 6.0), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMap MVK), and one reanalysis product [the interim ECMWF Re-Analysis (ERA-Interim)]-were validated against 205 rain gauge stations over four African river basins (Zambezi, Volta, Juba-Shabelle, and Baro-Akobo). Validation focused on rainfall characteristics relevant to hydrological applications, such as annual catchment totals, spatial distribution patterns, seasonality, number of rainy days per year, and timing and volume of heavy rainfall events. Validation was done at three spatially aggregated levels: point-to-pixel, subcatchment, and river basin for the period 2003-06. Performance of satellite-based rainfall estimation (SRFE) was assessed using standard statistical methods and visual inspection. SRFE showed 1) accuracy in reproducing precipitation on a monthly basis during the dry season, 2) an ability to replicate bimodal precipitation patterns, 3) superior performance over the tropical wet and dry zone than over semiarid or mountainous regions, 4) increasing uncertainty in the estimation of higher-end percentiles of daily precipitation, 5) low accuracy in detecting heavy rainfall events over semiarid areas, 6) general underestimation of heavy rainfall events, and 7) overestimation of number of rainy days in the tropics. In respect to SRFE performance, GPROF 6.0 and GSMaP-MKV were the least accurate, and RFE 2.0 and TRMM 3B42 were the most accurate. These results allow discrimination between the available products and the reduction of potential errors caused by selecting a product that is not suitable for particular morphoclimatic conditions. For hydrometeorological applications, results support the use of a performance-based merged product that combines the strength of multiple SRFEs. [ABSTRACT FROM AUTHOR]

Multispectral Spinning Enhanced Visible and IR Interferometer (SEVIRI) data, calibrated with daily rain gauge estimates, were used to produce daily high-resolution rainfall estimates over Africa. An artificial neural network (ANN) approach was used, producing an output of satellite pixel-scale daily rainfall totals. This product, known as the Rainfall Intensity Artificial Neural Network African Algorithm (RIANNAA), was calibrated and validated using gauge data from the highland Oromiya region of Ethiopia. Validation was performed at a variety of spatial and temporal scales, and results were also compared against Tropical Applications of Meteorology Using Satellite Data (TAMSAT) single-channel IR-based rainfall estimates. Several versions of RIANNAA, with different combinations of SEVIRI channels as inputs, were developed. RIANNAA was an improvement over TAMSAT at all validation scales, for all versions of RIANNAA. However, the addition of multispectral data to RIANNAA only provided a statistically significant improvement over the single-channel RIANNAA at the highest spatial and temporal-resolution validation scale. It appears that multispectral data add more value to rainfall estimates at high-resolution scales than at averaged time scales, where the cloud microphysical information that they provide may be less important for determining rainfall totals than larger-scale processes such as total moisture advection aloft. [ABSTRACT FROM AUTHOR]

Information related to land surface is immensely important to global change science. For example, land surface changes can alter regional climate through its effects on fluxes of water, energy, and carbon. In the past decades, data sources and methodologies for characterizing land surface heterogeneity (e.g., land cover, leaf area index, fractional vegetation cover, bare soil, and vegetation albedos) from remote sensing have evolved rapidly. The double ECOCLIMAP database-constituted of a land cover map and land surface variables and derived from Advanced Very High Resolution Radiometer (AVHRR) observations acquired between April 1992 and March 1993-was developed to support investigations that require information related to spatiotemporal dynamics of land surface. Here is the description of ECOCLIMAP-II: a new characterization of the land surface heterogeneity based on the latest generation of sensors, which represents an update of the ECOCLIMAP-I database over Africa. Owing to the many features of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors (more accurate in spatial resolution and spectral information compared to the AVHRR sensor), a variety of methods have been developed for an extended period of 8 yr (2000-07) to strengthen consistency between land surface variables as required by the meteorological and ecological communities. The relative accuracy (or performance) quality of ECOCLIMAP-II was assessed (i.e., by comparison with other global datasets). Results illustrate a substantial refinement; for instance, the fractional vegetation cover resulting in a root-mean-square error of 34%% instead of 64%% in comparison with the original version of ECOCLIMAP. [ABSTRACT FROM AUTHOR]

This study reveals homogeneous sub-regions over the poorly studied area of western equatorial Africa (10S-7N and 7E-30E). Monthly totals of 141 stations covering the period 1955-1984 are used. The stations are grouped based on the similarity of their interannual rainfall variability. In addition to annual totals, four different seasons are examined separately for regionalization, an approach that has lacked in previous studies. The four 3-month seasons are defined as follows: January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS), and October-November-December (OND). Two different algorithms are applied and compared: the rotated principal component analysis (RPCA) in conjunction with Ward's method, and the RPCA in conjunction with k-means method. The principal components that explain about 65% of total variance are retained and then varimax rotated. The corresponding scores are utilized as input for cluster analysis. Using Ward's method, five sub-regions are recognized for AMJ, JAS and OND and 4 sub-regions for JFM and annual data. The regions are geographically well distributed over the area and consist of roughly the same number of stations. The F-test is used to evaluate the homogeneity of each sub-region. The results show that all sub-regions are strongly homogeneous. Assuming the same number of clusters, the k-means method provides comparable spatial patterns with those of Ward's method. However, there are some differences, which are more evident in JAS and OND. Like Ward's method, the values of F-ratio for the k-means algorithm also confirm the homogeneity of all seasons/sub-regions. The interannual variability of rainfall for each season/sub-region is also provided and compared. [ABSTRACT FROM AUTHOR]

Background: Many HIV voluntary testing and counselling centres in Africa use rapid antibody tests, in parallel or in sequence, to establish same-day HIV status. The interpretation of indeterminate or discrepant results between different rapid tests on one sample poses a challenge. We investigated the use of an algorithm using three serial rapid HIV tests in cohabiting couples to resolve unclear serostatuses. Methods: Heterosexual couples visited the Rwanda Zambia HIV Research Group testing centres in Kigali, Rwanda, and Lusaka, Zambia, to assess HIV infection status. Individuals with unclear HIV rapid antibody test results (indeterminate) or discrepant results were asked to return for repeat testing to resolve HIV status. If either partner of a couple tested positive or indeterminate with the screening test, both partners were tested with a confirmatory test. Individuals with indeterminate or discrepant results were further tested with a tiebreaker and monthly retesting. HIV-RNA viral load was determined when HIV status was not resolved by follow-up rapid testing. Individuals were classified based on two of three initial tests as "Positive", "Negative" or "Other". Follow-up testing and/or HIV-RNA viral load testing determined them as "Infected", "Uninfected" or "Unresolved". Results: Of 45,820 individuals tested as couples, 2.3% (4.1% of couples) had at least one discrepant or indeterminate rapid result. A total of 65% of those individuals had follow-up testing and of those individuals initially classified as "Negative" by three initial rapid tests, less than 1% were resolved as "Infected". In contrast, of those individuals with at least one discrepant or indeterminate result who were initially classified as "Positive", only 46% were resolved as "Infected", while the remainder was resolved as "Uninfected" (46%) or "Unresolved" (8%). A positive HIV serostatus of one of the partners was a strong predictor of infection in the other partner as 48% of individuals who resolved as "Infected" had an HIV-infected spouse. Conclusions: In more than 45,000 individuals counselled and tested as couples, only 5% of individuals with indeterminate or discrepant rapid HIV test results were HIV infected. This represented only 0.1% of all individuals tested. Thus, algorithms using screening, confirmatory and tie-breaker rapid tests are reliable with two of three tests negative, but not when two of three tests are positive. False positive antibody tests may persist. HIV-positive partner serostatus should prompt repeat testing. [ABSTRACT FROM AUTHOR]

The sequences of 16S rDNA, cytochrome oxidase subunit I ( COI) and cytochrome b ( Cyt b) genes from nine field collections (seven provinces in China: Guangxi, Hubei, Guangdong, Guizhou, Zhejiang, Hunan, Jiangxi, and also southern and northern part of Iran), plus the sequences of Africa, Europe, Americas and Japan obtained form GenBank were used to re-analyse the genetic variation in the southern green stink bug Nezara viridula (Linnaeus). The phylogeographic re-analysis by using four algorithms (NJ, MP, ML and Bayesian) showed three main lineages. The Iranian haplotypes fell into lineage II formed from Europe and America, rather than in lineage III from Asia; the Chinese haplotypes fell into the Asian clade. Our results suggested that African and non-African gene pools have been isolated since the Miocene era with the molecular clock calibrations for Heteroptera mtDNA, and not since the Pliocene as mentioned previously. The corresponding age of the separation of the eastern and western Asia clades is estimated to be 4.0-1.6 million years ago, coinciding with the Pliocene-Pleistocene epoch and with acute rising events in the Tibet Plateau locates in the western China, which may have formed the barriers observed today. [ABSTRACT FROM AUTHOR]

Background: Long-lasting insecticidal nets are an effective tool for malaria prevention, and "universal coverage" with such nets is increasingly the goal of national malaria control programmes. However, national level campaigns in several countries have run out of nets in the course of distribution, indicating a problem in the method used to estimate the quantity needed. Presentation of hypothesis: A major reason for the shortfall in estimation is the mismatch between the quantification factor used to plan procurement and the allocation algorithm used at community level, in particular the effect of needing to add an additional net to households with an odd number of inhabitants. To solve this problem a revised quantification factor is suggested. Testing hypothesis: Based on data from a broad range of household surveys across Africa, the effect of odd-numbered households on numbers of nets distributed is estimated via two frequently used allocation methods. The impact of these algorithms on the proportion of households reaching a person to net ratio of 2:1, a frequently used marker of universal coverage is then calculated. Implications: In order to avoid stock-outs of nets during national coverage campaigns, it is recommended to use a quantification factor of 1.78 people per net, with an additional allocation factor suggested to account for other common problems at the community level resulting in a final recommended ratio of 1.60 people per net. It is also recommend that community level allocation procedures be aligned with procurement estimates to reduce shortages of nets during campaign distributions. These analyses should enable programme managers to make evidence-based decisions and support a more efficient and effective use of LLIN distribution campaign resources. [ABSTRACT FROM AUTHOR]

Wavelet-based empirical orthogonal function (WEOF) analysis was used to analyze the nonstationary spatial, temporal, and frequency regimes of the regional variability in southern African summer (October–March) rainfall. The leading modes of rainfall variability were then used to establish associations with gridded scale-averaged wavelet power of the sea surface temperature (SST) for the Indian and Atlantic Oceans. The WEOF revealed that southern African rainfall is out of phase between areas north and south of 25°S and that areas north of 25°S and northern South Africa experienced decreased rainfall between 1970 and 1997. The decrease in rainfall was modulated by periods of between 2 and 8 years. Using judiciously selected windows of April-May-June SST data for the Atlantic and Indian Oceans as predictors in the artificial neural network-genetic algorithm (ANN-GA), high prediction skill of standardized summer rainfall of southern Africa was achieved. For the validation period 1988–97, Pearson correlation between 0.83 and 0.98 (i.e., 69–96% of observed rainfall variability), Hanssen Kuipers skill scores of between 0.2 and 1.0, and root-mean-square errors of between 0.25 and 0.72 mm of standardized rainfall were found between observed and predicted summer rainfall at a 3-month lead time. [ABSTRACT FROM AUTHOR]

An extensive evaluation of 10 different satellite rainfall products was performed using station network over a complex topography, where elevation varies from below sea level to 4620 m. Evaluation was for two groups of products. The first group had low spatial (2.5°) and temporal (monthly) resolution and included the Global Precipitation Climatology Project (GPCP), the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) merged analysis (CMAP), and the Tropical Rainfall Measuring Mission (TRMM-3B43). The second group comprised products with relatively high spatial (0.1° to 1°) and temporal (3-hourly to 10-daily) resolution. These included the NOAA-CPC African rainfall estimation algorithm, GPCP one-degree-daily (1DD), TRMM-3B42, Tropical Applications of Meteorology using SATellite and other data (TAMSAT) estimates, and the CPC morphing technique (CMORPH). These products were aggregated to a 10-day total and remapped to spatial resolutions of 1°, 0.5° and 0.25°. TRMM-3B43 and CMAP from the first group and CMORPH, TAMSAT and TRMM-3B42 from the second group performed reasonably well. [ABSTRACT FROM AUTHOR]

Background: In order to detect potential disease clusters where a putative source cannot be specified, classical procedures scan the geographical area with circular windows through a specified grid imposed to the map. However, the choice of the windows' shapes, sizes and centers is critical and different choices may not provide exactly the same results. The aim of our work was to use an Oblique Decision Tree model (ODT) which provides potential clusters without pre-specifying shapes, sizes or centers. For this purpose, we have developed an ODT-algorithm to find an oblique partition of the space defined by the geographic coordinates. Methods: ODT is based on the classification and regression tree (CART). As CART finds out rectangular partitions of the covariate space, ODT provides oblique partitions maximizing the interclass variance of the independent variable. Since it is a NP-Hard problem in RN, classical ODT-algorithms use evolutionary procedures or heuristics. We have developed an optimal ODT-algorithm in R2, based on the directions defined by each couple of point locations. This partition provided potential clusters which can be tested with Monte-Carlo inference. We applied the ODT-model to a dataset in order to identify potential high risk clusters of malaria in a village in Western Africa during the dry season. The ODT results were compared with those of the Kulldorff' s SaTScan. Results: The ODT procedure provided four classes of risk of infection. In the first high risk class 60%, 95% confidence interval (CI95%) [52.22-67.55], of the children was infected. Monte-Carlo inference showed that the spatial pattern issued from the ODT-model was significant (p < 0.0001). Satscan results yielded one significant cluster where the risk of disease was high with an infectious rate of 54.21%, CI95% [47.51-60.75]. Obviously, his center was located within the first high risk ODT class. Both procedures provided similar results identifying a high risk cluster in the western part of the village where a mosquito breeding point was located. Conclusion: ODT-models improve the classical scanning procedures by detecting potential disease clusters independently of any specification of the shapes, sizes or centers of the clusters. [ABSTRACT FROM AUTHOR]

The article reports on a study which considers syndromic management for sexually transmitted disease as a key component of the global response to the HIV epidemic. The trial demonstrated a reduction in community HIV incidence of approximately 40 percent when syndromic management was used in local primary health care facilities.

Jesus Crespo Cuaresma (How different is Africa? A comment on Masanjala and Papageorgiou. Journal of Applied Econometrics, 2011) shows that the results in Masanjala and Papageorgiou (Rough and lonely road to prosperity: a reexamination of the sources of growth in Africa using Bayesian model averaging. Journal of Applied Econometrics 2008; 23(5): 671-682) are sensitive to an alternative prior model structure in considering interaction terms. As a side issue, the algorithm for averaging over models is also challenged. In this reply I show that the prior used in Masanjala and Papageorgiou is as sensible as the prior suggested by Crespo Cuaresma. What we learn from Crespo Cuaresma's comment and this reply is that further effort should be dedicated to the study of parameter heterogeneity in the framework of BMA methods. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]