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

I/O Sharing in a Multi-core Kernel for Mixed-criticality Applications

Part 8: Real-Time Aspects in Distributed SystemsInternational audienceIn a mixed-criticality system, applications with different safety criticality levels are usually required to be implemented upon one platform for several reasons( reducing hardware cost, space, power consumption). Partitioning technology is used to enable the integration of mixed-criticality applications with reduced certification cost. In the partitioning architecture of strong spatial and temporal isolation, fault propagation can be prevented among mixed-criticality applications (regarded as partitions). However, I/O sharing between partitions could be the path of fault propagation that hinders the partitioning. E.g. a crashed partition generates incorrect outputs to shared I/Os, which affects the functioning of another partition. This paper focuses on a message-based approach of I/O sharing in the HARTEX real-time kernel on a multi-core platform. Based on a simple multi-core partitioning architecture, a certifiable I/O sharing approach is implemented based on a safe message mechanism, in order to support the partitioning architecture, enable individual certification of mixed-criticality applications and thus achieve minimized total certification cost of the entire system

Industrial networks of the future: review of research and practice

Academic researchers have followed closely the interest of companies in establishing industrial networks by studying aspects such as social interaction and contractual relationships. But what patterns underlie the emergence of industrial networks and what support should research provide for practitioners? First, it appears that manufacturing is becoming a commodity rather than a unique capability, which accounts especially for low-technology approaches in downstream parts of the network, for example, in assembly operations. Second, the increased tendency towards specialisation has forced other, upstream, parts of industrial networks to introduce advanced manufacturing technologies for niche markets. Third, the capital market for investments in capacity, and the trade in manufacturing as a commodity, dominates resource allocation to a larger extent than was previously the case. Fourth, there is becoming a continuous move towards more loosely connected entities that comprise manufacturing networks. Finally, in these networks, concepts for supply chain management should address collaboration and information technology that supports decentralised decision-making, in particular to address sustainable and green supply chains. More traditional concepts, such as the keiretsu and chaibol networks of some Asian economies, do not sufficiently support the demands now being placed on networks. Research should address these five fundamental challenges to prepare for the industrial networks of 2020 and beyond

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine. © 2021, The Author(s)

Critical care usage after major gastrointestinal and liver surgery: a prospective, multicentre observational study

Background Patient selection for critical care admission must balance patient safety with optimal resource allocation. This study aimed to determine the relationship between critical care admission, and postoperative mortality after abdominal surgery. Methods This prespecified secondary analysis of a multicentre, prospective, observational study included consecutive patients enrolled in the DISCOVER study from UK and Republic of Ireland undergoing major gastrointestinal and liver surgery between October and December 2014. The primary outcome was 30-day mortality. Multivariate logistic regression was used to explore associations between critical care admission (planned and unplanned) and mortality, and inter-centre variation in critical care admission after emergency laparotomy. Results Of 4529 patients included, 37.8% (n=1713) underwent planned critical care admissions from theatre. Some 3.1% (n=86/2816) admitted to ward-level care subsequently underwent unplanned critical care admission. Overall 30-day mortality was 2.9% (n=133/4519), and the risk-adjusted association between 30-day mortality and critical care admission was higher in unplanned [odds ratio (OR): 8.65, 95% confidence interval (CI): 3.51–19.97) than planned admissions (OR: 2.32, 95% CI: 1.43–3.85). Some 26.7% of patients (n=1210/4529) underwent emergency laparotomies. After adjustment, 49.3% (95% CI: 46.8–51.9%, P<0.001) were predicted to have planned critical care admissions, with 7% (n=10/145) of centres outside the 95% CI. Conclusions After risk adjustment, no 30-day survival benefit was identified for either planned or unplanned postoperative admissions to critical care within this cohort. This likely represents appropriate admission of the highest-risk patients. Planned admissions in selected, intermediate-risk patients may present a strategy to mitigate the risk of unplanned admission. Substantial inter-centre variation exists in planned critical care admissions after emergency laparotomies

Transverse momentum spectra of charged particles in proton-proton collisions at 1as=900 GeV with ALICE at the LHC

The inclusive charged particle transverse momentum distribution is measured in proton-proton collisions at s=900 GeV at the LHC using the ALICE detector. The measurement is performed in the central pseudorapidity region (|\u3b7|<0.8) over the transverse momentum range 0.15<10 GeV/c. The correlation between transverse momentum and particle multiplicity is also studied. Results are presented for inelastic (INEL) and non-single-diffractive (NSD) events. The average transverse momentum for |\u3b7|<0.8 is \u3008pT\u3009INEL=0.483\ub10.001 (stat.)\ub10.007 (syst.) GeV/c and \u3008pT\u3009NSD=0.489\ub10.001 (stat.)\ub10.007 (syst.) GeV/c, respectively. The data exhibit a slightly larger \u3008pT\u3009 than measurements in wider pseudorapidity intervals. The results are compared to simulations with the Monte Carlo event generators PYTHIA and PHOJET. \ua9 2010