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

A Novel Energy-Efficient and Reliable ACO-Based Routing Protocol for WSN-Enabled Forest Fires Detection

We address the problem of energy efficiency and reliability for forest fires monitored by a distributed bandwidth-constrained Wireless Sensor Network (WSN). To improve energy efficiency, data routing is an important approach that is being considered in the context of WSNs. An attractive and widely used method to find the optimal communication paths is the Ant Colony Optimization (ACO) algorithm. However, the traditional ACO-based routing protocols only consider the energy-efficiency while ignoring the overall network reliability (before and after failures) which is critical in the context of WSNs. In addition, the existing protocols are not application-specific (i.e., the parameters cannot be adapted to the application’s requirements). In this paper, we propose a novel Energy-efficient and Reliable ACO-based Routing Protocol (E-RARP) for WSNs. The proposed protocol not only guarantees high quality communication paths in terms of energy efficiency but also ensures the communication reliability. Critical events in delay-intolerant applications (e.g., forest fires detection) require reliable transmission in order to perform reliable decisions and take appropriate actions in a timely fashion. The simulations results reveal that E-RARP outperforms respectively Load Balanced Cluster-based Routing using ACO and Enhanced Ant-based QoS-aware routing protocol for Heterogeneous Wireless Sensor Networks protocols with a significant improvement of 30.55 % in network lifetime and 14.71 % in network response time

A reinforcement learning based routing protocol for software-defined networking enabled Wireless Sensor Network forest fire detection

Critical event reporting Wireless Sensor Networks (WSNs) applications need vital requirements (extended network lifetime, reliability, real time responsiveness, and scalability) to be met to ensure outstanding efficiency. Previous frameworks only consider few individual requirements, thus ignoring the other equally important ones. Ensuring that an active path is available at all times is crucial for enabling the timely transmission of critical data and maintaining the quality of service required to efficiently support delay-sensitive applications. This paper proposes an application-specific Routing Protocol based on Reinforcement Learning (RL) for Software Defined Network (SDN)-enabled WSN forest fire detection (RPLS). First, we designed a clustering algorithm that delays re-clustering to save energy by keeping the same topology for several rounds. Unlike existing works, this algorithm decreases the cluster radius based not only on the energy parameters but also on the quality of the links. After the network clustering, the power of the SDN controller is used to intelligently define using RL the optimal paths for the sensor nodes and accordingly reduce the load on these constrained nodes. For routing strategy, we formulate an RL-based reward function considering not only the energy efficiency parameters but also the anticipated and post-failure reliability parameters to ensure real time responsiveness and optimize energy consumption. Finally, we conducted comparisons by means of simulations in forest fires detection scenario. Compared to RL-SDWSN, the results show an improvement of 14.064 % in network operational lifetime and 16.41 % in response time