A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa.

The progression of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Africa has so far been heterogeneous, and the full impact is not yet well understood. In this study, we describe the genomic epidemiology using a dataset of 8746 genomes from 33 African countries and two overseas territories. We show that the epidemics in most countries were initiated by importations predominantly from Europe, which diminished after the early introduction of international travel restrictions. As the pandemic progressed, ongoing transmission in many countries and increasing mobility led to the emergence and spread within the continent of many variants of concern and interest, such as B.1.351, B.1.525, A.23.1, and C.1.1. Although distorted by low sampling numbers and blind spots, the findings highlight that Africa must not be left behind in the global pandemic response, otherwise it could become a source for new variants

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance.

Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern-particularly Alpha, Beta, Delta, and Omicron-on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Middle to late Holocene sedimentary filling history of the Sebkha el Melah in south-eastern Tunisia

peer reviewedThe sedimentological and geochemical properties of a 146 cm long sediment core collected from Sebkha el Melah (Ml core) in south-eastern Tunisia have been used to infer the genesis and evolution of the Sebkha el Melah over the last 5000 years. Two main sedimentary units have been defined: a huntite [Mg3Ca(CO3)4] unit at the bottom of core Ml is covered by a second unit made up of siliciclastic and evaporitic materials. The huntite level is synchronous with Holocene marine transgression, which was followed by a regression of about 5300 years bp. Geochemical data (major and trace element), magnetic analyses, grain-size distribution and microtexture of quartz grains were performed to assess the sediment provenance. The upper unit of Ml core is characterized by alternations between fluvial, aeolian and evaporite deposits. Redox proxies displaying marked Fe/Ca and Rb/S peaks, in addition to high magnetic susceptibility (MS) values, polymodal grain-size frequency curves, as well as sub-angular grains with V-shaped percussion cracks, are suggestive of palaeohydrological events. In contrast, a decreasing trend in the low MS values, bi-modal grain-size frequency curves as well as well-rounded quartz grains with crescent percussion marks would indicate enhanced aeolian sand input in the sebkah el Melah sequence. Moreover, geochemical proxies suggest formation of evaporite facies under a strongly warm climate contemporary with marine intrusion

Semi-automatic news video annotation framework for Arabic text

In this paper, we present a semi-automatic news video annotation tool. The tool and its algorithms are dedicated to artificial Arabic text embedded in video news in the form of static text as well as scrolling one. It is performed at two different levels. Including specificities of Arabic script, the tool manages a global level which concerns the entire video and a local level which concerns any specific frame extracted from the video. The global annotation is performed manually thanks to a user interface. As a result of this step, we obtain the global xml file. The local annotation at the frame level is done automatically according to the information contained in the global metafile and a proposed text tracking algorithm. The main application of our tool is the ground truthing of textual information in video content. It is being used for this purpose in the Arabic Text in Video (AcTiV) database project in our lab. One of the functions that AcTiV provides, is a benchmark to compare existing and future Arabic video OCR systems

Data, protocol and algorithms for performance evaluation of text detection in Arabic news video

Benchmark datasets and their corresponding evaluation protocols are commonly used by the computer vision community, in a variety of application domains, to assess the performance of existing systems. Even though text detection and recognition in video has seen much progress in recent years, relatively little work has been done to propose standardized annotations and evaluation protocols especially for Arabic Video-OCR systems. In this paper, we present a framework for evaluating text detection in videos. Additionally, dataset, ground-truth annotations and evaluation protocols, are provided for Arabic text detection. Moreover, two published text detection algorithms are tested on a part of the AcTiV database and evaluated using a set of the proposed evaluation protocols

Multi-dimensional long short-term memory networks for artificial Arabic text recognition in news video

This study presents a novel approach for Arabic video text recognition based on recurrent neural networks. In fact, embedded texts in videos represent a rich source of information for indexing and automatically annotating multimedia documents. However, video text recognition is a non-trivial task due to many challenges like the variability of text patterns and the complexity of backgrounds. In the case of Arabic, the presence of diacritic marks, the cursive nature of the script and the non-uniform intra/inter word distances, may introduce many additional challenges. The proposed system presents a segmentation-free method that relies specifically on a multi-dimensional long short-term memory coupled with a connectionist temporal classification layer. It is shown that using an efficient pre-processing step and a compact representation of Arabic character models brings robust performance and yields a low-error rate than other recently published methods. The authors’ system is trained and evaluated using the public AcTiV-R dataset under different evaluation protocols. The obtained results are very interesting. They also outperform current state-of-the-art approaches on the public dataset ALIF in terms of recognition rates at both character and line levels

A dataset for Arabic text detection, tracking and recognition in news videos- AcTiV

Recently, promising results have been reported on video text detection and recognition. Most of the proposed methods are tested on private datasets with non-uniform evaluation metrics. We report here on the development of a publicly accessible annotated video dataset designed to assess the performance of different artificial Arabic text detection, tracking and recognition systems. The dataset includes 80 videos (more than 850,000 frames) collected from 4 different Arabic news channels. An attempt was made to ensure maximum diversities of the textual content in terms of size, position and background. This data is accompanied by detailed annotations for each textbox. We also present a region-based text detection approach in addition to a set of evaluation protocols on which the performance of different systems can be measured