Why do we need to care about transboundary aquifers and how do we solve their issues?

As the reliance on transboundary groundwater is increasing globally, it is important to understand and address the specifc issues raised by the assessment and management of transboundary aquifers (TBAs). Building on 20 years of TBA experience and through a three-pillar framework (assessment, cooperation-collaboration, shared management), the key elements to addressing TBA issues are described, including a multidisciplinary approach, identifcation of hotspot zones, local vs border-wide approaches, appropriate funding models, and an increased recognition of the role and value of each TBA

A comparison of ensemble and deep learning algorithms to model groundwater levels in a data-scarce aquifer of Southern Africa

Machine learning and deep learning have demonstrated usefulness in modelling various groundwater phenomena. However, these techniques require large amounts of data to develop reliable models. In the Southern African Development Community, groundwater datasets are generally poorly developed. Hence, the question arises as to whether machine learning can be a reliable tool to support groundwater management in the data-scarce environments of Southern Africa. This study tests two machine learning algorithms, a gradient-boosted decision tree (GBDT) and a long short-term memory neural network (LSTM-NN), to model groundwater level (GWL) changes in the Shire Valley Alluvial Aquifer

Big data analytics and its role to support groundwater management in the Southern African development community

Big data analytics (BDA) is a novel concept focusing on leveraging large volumes of heterogeneous data through advanced analytics to drive information discovery. This paper aims to highlight the potential role BDA can play to improve groundwater management in the Southern African Development Community (SADC) region in Africa. Through a review of the literature, this paper defines the concepts of big data, big data sources in groundwater, big data analytics, big data platforms and framework and how they can be used to support groundwater management in the SADC region. BDA may support groundwater management in SADC region by filling in data gaps and transforming these data into useful information. In recent times, machine learning and artificial intelligence have stood out as a novel tool for data-driven modeling. Managing big data from collection to information delivery requires critical application of selected tools, techniques and methods. Hence, in this paper we present a conceptual framework that can be used to manage the implementation of BDA in a groundwater management context. Then, we highlight challenges limiting the application of BDA which included technological constraints and institutional barriers. In conclusion, the paper shows that sufficient big data exist in groundwater domain and that BDA exists to be used in groundwater sciences thereby providing the basis to further explore data-driven sciences in groundwater management

Why do we need to care about transboundary aquifers and how do we solve their issues?

As the reliance on transboundary groundwater is increasing globally, it is important to understand and address the specific issues raised by the assessment and management of transboundary aquifers (TBAs). Building on 20 years of TBA experience and through a three-pillar framework (assessment, cooperation-collaboration, shared management), the key elements to addressing TBA issues are described, including a multidisciplinary approach, identification of hotspot zones, local vs border-wide approaches, appropriate funding models, and an increased recognition of the role and value of each TBA

Outcomes, infectiousness, and transmission dynamics of patients with extensively drug-resistant tuberculosis and home-discharged patients with programmatically incurable tuberculosis: a prospective cohort study.

BACKGROUND: The emergence of programmatically incurable tuberculosis threatens to destabilise control efforts. The aim of this study was to collect prospective patient-level data to inform treatment and containment strategies. METHODS: In a prospective cohort study, 273 South African patients with extensively drug-resistant tuberculosis, or resistance beyond extensively drug-resistant tuberculosis, were followed up over a period of 6 years. Transmission dynamics, infectiousness, and drug susceptibility were analysed in a subset of patients from the Western Cape using whole-genome sequencing (WGS; n=149), a cough aerosol sampling system (CASS; n=26), and phenotypic testing for 18 drugs (n=179). FINDINGS: Between Oct 1, 2008, and Oct 31, 2012, we enrolled and followed up 273 patients for a median of 20·3 months (IQR 9·6-27·8). 203 (74%) had programmatically incurable tuberculosis and unfavourable outcomes (treatment failure, relapse, default, or death despite treatment with a regimen based on capreomycin, aminosalicylic acid, or both). 172 (63%) patients were discharged home, of whom 104 (60%) had an unfavourable outcome. 54 (31%) home-discharged patients had failed treatment, with a median time to death after discharge of 9·9 months (IQR 4·2-17·4). 35 (20%) home-discharged cases were smear-positive at discharge. Using CASS, six (23%) of 26 home-discharged cases with data available expectorated infectious culture-positive cough aerosols in the respirable range (<5 μm), and most reported inter-person contact with suboptimal protective mask usage. WGS identified 17 (19%) of the 90 patients (with available sequence data) that were discharged home before the diagnosis of 20 downstream cases of extensively drug-resistant tuberculosis with almost identical sequencing profiles suggestive of community-based transmission (five or fewer single nucleotide polymorphisms different and with identical resistance-encoding mutations for 14 drugs). 11 (55%) of these downstream cases had HIV co-infection and ten (50%) had died by the end of the study. 22 (56%) of 39 isolates in patients discharged home after treatment failure were resistant to eight or more drugs. However, five (16%) of 31 isolates were susceptible to rifabutin and more than 90% were likely to be sensitive to linezolid, bedaquiline, and delamanid. INTERPRETATION: More than half of the patients with programmatically incurable tuberculosis were discharged into the community where they remained for an average of 16 months, were at risk of expectorating infectious cough aerosols, and posed a threat of transmission of extensively drug-resistant tuberculosis. Urgent action, including appropriate containment strategies, is needed to address this situation. Access to delamanid, bedaquiline, linezolid, and rifabutin, when appropriate, must be accelerated along with comprehensive drug susceptibility testing. FUNDING: UK Medical Research Council, South African Medical Research Council, South African National Research Foundation, European & Developing Countries Clinical Trials Partnership, Oppenheimer Foundation, Newton Fund, Biotechnology and Biological Sciences Research Council, King Abdullah University of Science & Technology

The bii4africa dataset of faunal and floral population intactness estimates across Africa’s major land uses

Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species’ population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate ‘intactness scores’: the remaining proportion of an ‘intact’ reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region’s major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/ taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems

Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community

Big data analytics (BDA) is a novel concept focusing on leveraging large volumes of heterogeneous data through advanced analytics to drive information discovery. This paper aims to highlight the potential role BDA can play to improve groundwater management in the Southern African Development Community (SADC) region in Africa. Through a review of the literature, this paper defines the concepts of big data, big data sources in groundwater, big data analytics, big data platforms and framework and how they can be used to support groundwater management in the SADC region. BDA may support groundwater management in SADC region by filling in data gaps and transforming these data into useful information. In recent times, machine learning and artificial intelligence have stood out as a novel tool for data-driven modeling. Managing big data from collection to information delivery requires critical application of selected tools, techniques and methods. Hence, in this paper we present a conceptual framework that can be used to manage the implementation of BDA in a groundwater management context. Then, we highlight challenges limiting the application of BDA which included technological constraints and institutional barriers. In conclusion, the paper shows that sufficient big data exist in groundwater domain and that BDA exists to be used in groundwater sciences thereby providing the basis to further explore data-driven sciences in groundwater management

Bacterial and host determinants of cough aerosol culture positivity in patients with drug-resistant versus drug-susceptible tuberculosis.

A burgeoning epidemic of drug-resistant tuberculosis (TB) threatens to derail global control efforts. Although the mechanisms remain poorly clarified, drug-resistant strains are widely believed to be less infectious than drug-susceptible strains. Consequently, we hypothesized that lower proportions of patients with drug-resistant TB would have culturable Mycobacterium tuberculosis from respirable, cough-generated aerosols compared to patients with drug-susceptible TB, and that multiple factors, including mycobacterial genomic variation, would predict culturable cough aerosol production. We enumerated the colony forming units in aerosols (≤10 µm) from 452 patients with TB (227 with drug resistance), compared clinical characteristics, and performed mycobacterial whole-genome sequencing, dormancy phenotyping and drug-susceptibility analyses on M. tuberculosis from sputum. After considering treatment duration, we found that almost half of the patients with drug-resistant TB were cough aerosol culture-positive. Surprisingly, neither mycobacterial genomic variants, lineage, nor dormancy status predicted cough aerosol culture positivity. However, mycobacterial sputum bacillary load and clinical characteristics, including a lower symptom score and stronger cough, were strongly predictive, thereby supporting targeted transmission-limiting interventions. Effective treatment largely abrogated cough aerosol culture positivity; however, this was not always rapid. These data question current paradigms, inform public health strategies and suggest the need to redirect TB transmission-associated research efforts toward host-pathogen interactions