Simulation of the Effects of Targeted Immunization on the Peak Number of Infections in Complex Networks

In this paper we study the effect of targeted immunization on the peak number of infections in an epidemic outbreak. For this we extend a previously developed python-based dashboard environment for the time efficient simulation-based study of SIR epidemics spread on complex network topologies, using realistic Continuous Time Markov Chain (CTMC) simulations by means of Gillespie's stochastic simulation algorithm. The new components make it possible to study targeted immunization by means of state-of-the-art methods and to visualize typical paths of infection during the temporal evolution of an epidemic. We show results obtained with different centrality measures (eigenvalue centrality of the adjacency and non-backtracking matrix, degree centrality, and average path length centrality), used in targeted immunization. In the results we focus on studying the peak number of infections (PNI). The PNI is very relevant when it comes to the practical management of an epidemic, as it determines, for instance, the number of intensive care units that are needed to offer an appropriate treatment of the disease in critical cases. However, the PNI has received much less attention in studies than the epidemic threshold, which is more relevant in the early stage of an epidemic. Our example study on classical network topologies reveal that the choice of the centrality measure for targeted immunization as well as the number of targeted nodes will have a strong impact on the drop of the PNI. Our simulation-based results on scale-free Barabasi-Albert networks show that the PNI reduction that can be achieved by using modern centrality metrics such as the non-backtracking eigenvalue drop, can lead to up to 40% lower peaks than those achieved with naïve methods such as degree based immunization (immunization of the biggest node(s)) in case of immunization of 2.5% nodes. These results underpins the crucial importance of the correct choice of the centrality metric in targeted immunization. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

New Approaches to Extract Information from Posts on COVID-19 Published on Reddit

In the last two years, we have seen a huge number of debates and discussions on COVID-19 in social media. Many authors have analyzed these debates on Facebook and Twitter, while very few ones have considered Reddit. In this paper, we focus on this social network and propose three approaches to extract information from posts on COVID-19 published in it. The first performs a semi-automatic and dynamic classification of Reddit posts. The second automatically constructs virtual subreddits, each characterized by homogeneous themes. The third automatically identifies virtual communities of users with homogeneous themes. The three approaches represent an advance over the past literature. In fact, the latter lacks studies regarding classification algorithms capable of outlining the differences among the thousands of posts on COVID-19 in Reddit. Analogously, it lacks approaches able to build virtual subreddits with homogeneous topics or virtual communities of users with common interests.

Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis

Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step heading of a human for instance, with spatial information is an ubiquitous way to correct for the cumulative noises. This so-called map-matching process can be achieved in several ways. In this paper, a novel real-time map-matching approach was developed, using a backtracking particle filter that benefits from the implemented geospatial analysis, which reduces the complexity of spatial queries and provides flexibility in the use of different kinds of spatial constraints. The goal was to generalize the algorithm to permit the use of any kind of odometry data calculated by different sensors and approaches as the input. Further research, development, and comparisons have been done by the easy implementation of different spatial constraints and use cases due to the modular structure. Additionally, a simple map-based optimization using transition areas between floors has been developed. The developed algorithm could achieve accuracies of up to 3 m at approximately the 90th percentile for two different experiments in a complex building structure.

CoV-er all the bases: Structural perspectives of SARS-CoV-2 RNA synthesis.

The ongoing Covid-19 pandemic has spurred research in the biology of the nidovirus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Much focus has been on the viral RNA synthesis machinery due to its fundamental role in viral propagation. The central and essential enzyme of the RNA synthesis process, the RNA-dependent RNA polymerase (RdRp), functions in conjunction with a coterie of viral-encoded enzymes that mediate crucial nucleic acid transactions. Some of these enzymes share common features with other RNA viruses, while others play roles unique to nidoviruses or CoVs. The RdRps are proven targets for viral pathogens, and many of the other nucleic acid processing enzymes are promising targets. The purpose of this review is to summarize recent advances in our understanding of the mechanisms of RNA synthesis in CoVs. By reflecting on these studies, we hope to emphasize the remaining gaps in our knowledge. The recent onslaught of structural information related to SARS-CoV-2 RNA synthesis, in combination with previous structural, genetic and biochemical studies, have vastly improved our understanding of how CoVs replicate and process their genomic RNA. Structural biology not only provides a blueprint for understanding the function of the enzymes and cofactors in molecular detail, but also provides a basis for drug design and optimization. The concerted efforts of researchers around the world, in combination with the renewed urgency toward understanding this deadly family of viruses, may eventually yield new and improved antivirals that provide relief to the current global devastation.

The nucleotide addition cycle of the SARS-CoV-2 polymerase.

Coronaviruses have evolved elaborate multisubunit machines to replicate and transcribe their genomes. Central to these machines are the RNA-dependent RNA polymerase subunit (nsp12) and its intimately associated cofactors (nsp7 and nsp8). We use a high-throughput magnetic-tweezers approach to develop a mechanochemical description of this core polymerase. The core polymerase exists in at least three catalytically distinct conformations, one being kinetically consistent with incorporation of incorrect nucleotides. We provide evidence that the RNA-dependent RNA polymerase (RdRp) uses a thermal ratchet instead of a power stroke to transition from the pre- to post-translocated state. Ultra-stable magnetic tweezers enable the direct observation of coronavirus polymerase deep and long-lived backtracking that is strongly stimulated by secondary structures in the template. The framework we present here elucidates one of the most important structure-dynamics-function relationships in human health today and will form the grounds for understanding the regulation of this complex.

Structural basis for backtracking by the SARS-CoV-2 replication-transcription complex.

Backtracking, the reverse motion of the transcriptase enzyme on the nucleic acid template, is a universal regulatory feature of transcription in cellular organisms but its role in viruses is not established. Here we present evidence that backtracking extends into the viral realm, where backtracking by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA-dependent RNA polymerase (RdRp) may aid viral transcription and replication. Structures of SARS-CoV-2 RdRp bound to the essential nsp13 helicase and RNA suggested the helicase facilitates backtracking. We use cryo-electron microscopy, RNA-protein cross-linking, and unbiased molecular dynamics simulations to characterize SARS-CoV-2 RdRp backtracking. The results establish that the single-stranded 3' segment of the product RNA generated by backtracking extrudes through the RdRp nucleoside triphosphate (NTP) entry tunnel, that a mismatched nucleotide at the product RNA 3' end frays and enters the NTP entry tunnel to initiate backtracking, and that nsp13 stimulates RdRp backtracking. Backtracking may aid proofreading, a crucial process for SARS-CoV-2 resistance against antivirals.

ReCognizing SUspect and PredictiNg ThE SpRead of Contagion Based on Mobile Phone LoCation DaTa (COUNTERACT): A system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence.

Human movement is a significant factor in extensive spatial-transmission models of contagious viruses. The proposed COUNTERACT system recognizes infectious sites by retrieving location data from a mobile phone device linked with a particular infected subject. The proposed approach is computing an incubation phase for the subject's infection, backpropagation through the subjects' location data to investigate a location where the subject has been during the incubation period. Classifying to each such site as a contagious site, informing exposed suspects who have been to the contagious location, and seeking near real-time or real-time feedback from suspects to affirm, discard, or improve the recognition of the infectious site. This technique is based on the contraption to gather confirmed infected subject and possibly carrier suspect area location, correlating location for the incubation days. Security and privacy are a specific thing in the present research, and the system is used only through authentication and authorization. The proposed approach is for healthcare officials primarily. It is different from other existing systems where all the subjects have to install the application. The cell phone associated with the global positioning system (GPS) location data is collected from the COVID-19 subjects.