Introduction: Antimicrobial resistance (AMR) poses a global threat. High levels of AMR to commonly used antibiotics have been reported in East Africa. A situation analysis of AMR in Ethiopia also indicated high resistance levels. To prevent and contain AMR, Ethiopia established a national surveillance network.Objectives: This article describes the steps taken to prioritise AMR and establish the National Antimicrobial Resistance Surveillance System in Ethiopia, as well as present the challenges and lessons learned through implementation.Methods: In April 2017, Ethiopia had developed and approved the National AMR Surveillance Plan for laboratory-based AMR surveillance. The World Health Organization recommendations and Ethiopias’s current microbiology capacity were used to prioritise organisms for reporting. The surveillance system is comprised of a network linking the national reference laboratory with surveillance sentinel sites. Roll-out of the AMR surveillance network occurred in three phases in order to ensure successful implementation.Results: Electronic capture and transmission of data, supply chain for the microbiology laboratory and communication problems were challenges observed after implementation started. Support from Ethiopian Public Health Institute focal persons for data entry, regular scheduled communication establishment and procurement of supplies by the American Society for Microbiology were some of the measures taken to address the challenges.Conclusion: Ethiopia has demonstrated that setting up AMR surveillance in lower resource settings is possible with strong leadership and stakeholder engagement.

Accurate quantitative mineralogical data has significant implications in mining operations. However, quantitative analysis of minerals is challenging for most of the sensor outputs. Thus, it requires advances in data analytics. In this work, data fusion approaches for integrating datasets pertaining to the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions are proposed, aiming to facilitate more accurate prediction of SiO₂, Al₂O₃, and Fe₂O₃ concentrations in a polymetallic sulphide deposit. Two approaches of low-level data fusion were applied to these datasets. In the first approach, the pre-processed blocks of MWIR and LWIR data were concatenated to form a fused data block. In the second approach, a prior variable selection was performed to extract the most important features from the MWIR and LWIR datasets. The extracted informative features were subsequently concatenated to form a new fused data block. Next, prediction models that link the mineralogical concentrations with the infrared reflectance spectra were developed using partial-least squares regression (PLSR), principal component regression (PCR) and support vector regression (SVR) analytical techniques. These models were applied to the fused data blocks as well as the individual (MWIR and LWIR) data blocks. The obtained results indicate that SiO₂, Al₂O₃, and Fe₂O₃ mineral concentrations can be successfully predicted using both MWIR and LWIR spectra individually, but the prediction performance greatly improved with data fusion; where the PLSR, PCR, and SVR models provided good and acceptable results. The proposed approach could be extended for online analysis of mineral concentrations in different deposit types. Thus, it would be highly beneficial in mining operations, where indications of mineralogical concentrations can have significant financial implications.

The increasing availability of complex multivariate data yielded by sensor technologies permits qualitative and quantitative data analysis for material characterization. Multivariate data are hard to understand by visual inspection and intuition. Thus, data-driven models are required to derive study-specific insights from large datasets. In the present study, a partial least squares regression (PLSR) model was used for the prediction of elemental concentrations using the mineralogical techniques mid-wave infrared (MWIR) and long-wave infrared (LWIR) combined with data fusion approaches. In achieving the study objectives, the usability of the individual MWIR and LWIR datasets for the prediction of the concentration of elements in a polymetallic sulphide deposit was assessed, and the results were compared with the outputs of low- and mid-level data fusion methods. Prior to low-level data fusion implementation, data filtering techniques were applied to the MWIR and LWIR datasets. The pre-processed data were concatenated and a PLSR model was developed using the fused data. The mid-level data fusion was implemented by extracting features using principal component analysis (PCA) scores. As the models were applied to the MWIR, LWIR, and fused datasets, an improved prediction was achieved using the low-level data fusion approach. Overall, the acquired results indicate that the MWIR data can be used to reliably predict a combined Pb–Zn concentration, whereas LWIR data has a good correlation with the Fe concentration. The proposed approach could be extended for generating indicative element concentrations in polymetallic sulphide deposits in real-time using infrared reflectance data. Thus, it is beneficial in providing elemental concentration insights in mining operations.

Sensor technologies provide relevant information on the key geological attributes in mining. The integration of data from multiple sources is advantageous in making use of the synergy among the outputs for the enhanced characterisation of materials. Sensors produce various types of data. Thus, the fusion of these data requires innovative data-driven strategies. In the present study, the fusion of image and point data is proposed, aiming for the enhanced classification of ore and waste materials in a polymetallic sulphide deposit at 3%, 5% and 7% cut-off grades. The image data were acquired in the visible-near infrared (VNIR) and short-wave infrared (SWIR) regions of the electromagnetic spectrum. The point data cover the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions. A multi-step methodological approach was developed for the fusion of the image and point data at multiple levels using the supervised and unsupervised classification techniques. Several possible combinations of the data blocks were evaluated to select the optimal combinations in an optimised way. The obtained results indicate that the individual image and point techniques resulted in a successful classification of ore and waste materials. However, the classification performance greatly improved with the fusion of image and point data, where the K-means and support vector classification (SVC) models provided acceptable results. The proposed approach enables a significant reduction in data volume while maintaining the relevant information in the spectra. This is principally beneficial for the integration of data from high-throughput and large data volume sources. Thus, the effectiveness and practicality of the approach can permit the enhanced separation of ore and waste materials in operational mines.

The African Journal of Laboratory Medicine focuses on the role of the laboratory and its professionals in the clinical and public healthcare sectors, and is specifically based on an African frame of reference.

Despite significant recent advancements in the sensor technologies, the use of sensors for raw material characterization in the mining industry remains limited. The aim of the present study was to assess the utility of applying the mid-wave infrared (MWIR) reflectance data acquired by the use of a handheld Fourier-transform infrared spectrometer (FTIR), combined with partial least squares-discriminant analysis (PLS-DA), for the characterization of a polymetallic sulphide ore deposit. In achieving the study objectives, focus was given to the MWIR portion of the FTIR dataset, as it is the least explored region of the infrared spectrum in mineral characterization studies. Three datasets—covering different wavelength ranges—were generated from the FTIR spectral data, namely the full FTIR range (2.5–15 µm), MWIR (2.5–7 µm) and long-wave infrared (LWIR: 7–15 µm), in order to investigate the associated information level of each defined wavelength region separately. Design of experiment was developed to determine the optimal data filtering techniques. Using the processed data and PLS-DA, a series of calibration and prediction models were developed for ore and waste materials separately. As the models applied to the MWIR data showed a successful classification rate of 86.3% for sulphide ore–waste discrimination, similarly using the full spectral FTIR dataset, a correct classification rate of 89.5% was achieved. This indicates that MWIR spectral range includes informative signals that are sufficient for classifying the material into ore or waste. The proposed approach could be extended for automating the sulphide ore–waste discrimination process, thus greatly benefiting marginally economical mining operations.