Abstract:Recent history has provided us with one pandemic (Influenza A/H1N1) and two severe viral outbreaks (Ebola and Zika). In all three cases, post-hoc analyses have given us deep insights into what triggered these outbreaks, their timing, evolutionary dynamics, and phylogeography, but the genomic characteristics of outbreak viruses are still unclear. To address this outstanding question, we searched for a common denominator between these recent outbreaks, positing that the genome of outbreak viruses is in an unstab… Show more
“…On this, though detection times may vary greatly in respect virus types (influenza, Henipavirus (Nipah virus), Filoviruses like Ebola, and Flavivirus like Zika et cetera) [16,17] and other characteristics, the constant is that technological advancement is clearly aiding in reducing their detection time. This was evidenced in the recent case of COVID-19, taking only seven days for detection.…”
Section: A Brief Survey On Infectious Disease Outbreak In a 20-year Pmentioning
Predictive computing tools are increasingly being used and have demonstrated successfulness in providing insights that can lead to better health policy and management. However, as these technologies are still in their infancy stages, slow progress is being made in their adoption for serious consideration at national and international policy levels. However, a recent case evidences that the precision of Artificial Intelligence (AI) driven algorithms are gaining in accuracy. AI modelling driven by companies such as BlueDot and Metabiota anticipated the Coronavirus (COVID-19) in China before it caught the world by surprise in late 2019 by both scouting its impact and its spread. From a survey of past viral outbreaks over the last 20 years, this paper explores how early viral detection will reduce in time as computing technology is enhanced and as more data communication and libraries are ensured between varying data information systems. For this enhanced data sharing activity to take place, it is noted that efficient data protocols have to be enforced to ensure that data is shared across networks and systems while ensuring privacy and preventing oversight, especially in the case of medical data. This will render enhanced AI predictive tools which will influence future urban health policy internationally.
“…On this, though detection times may vary greatly in respect virus types (influenza, Henipavirus (Nipah virus), Filoviruses like Ebola, and Flavivirus like Zika et cetera) [16,17] and other characteristics, the constant is that technological advancement is clearly aiding in reducing their detection time. This was evidenced in the recent case of COVID-19, taking only seven days for detection.…”
Section: A Brief Survey On Infectious Disease Outbreak In a 20-year Pmentioning
Predictive computing tools are increasingly being used and have demonstrated successfulness in providing insights that can lead to better health policy and management. However, as these technologies are still in their infancy stages, slow progress is being made in their adoption for serious consideration at national and international policy levels. However, a recent case evidences that the precision of Artificial Intelligence (AI) driven algorithms are gaining in accuracy. AI modelling driven by companies such as BlueDot and Metabiota anticipated the Coronavirus (COVID-19) in China before it caught the world by surprise in late 2019 by both scouting its impact and its spread. From a survey of past viral outbreaks over the last 20 years, this paper explores how early viral detection will reduce in time as computing technology is enhanced and as more data communication and libraries are ensured between varying data information systems. For this enhanced data sharing activity to take place, it is noted that efficient data protocols have to be enforced to ensure that data is shared across networks and systems while ensuring privacy and preventing oversight, especially in the case of medical data. This will render enhanced AI predictive tools which will influence future urban health policy internationally.
“…Note however that this latter analysis is more informative than the former, as the density also shows that most of the sites under selection are involved in weak interactions. This is intriguingly reminiscent of the involvement of weakly interacting pairs of sites in severe outbreaks or pandemics (Aris-Brosou et al ., 2017).…”
Section: Resultsmentioning
confidence: 92%
“…These reconstructed mutational paths were then recoded as a binary matrix, with rows corresponding to branches and columns to a site of the alignment. The BGM was then used to identify the pairs of sites that exhibit correlated patterns of nonsynonymous substitutions according to their posterior probability, estimated with a Markov chain Monte Carlo sampler that was run for 10 5 steps, with a burn-in period of 10,000 steps sampling every 1,000 steps for inference (Aris-Brosou et al ., 2017).…”
Section: Methodsmentioning
confidence: 99%
“…This last point suggests that a seldom explored evolutionary process, correlated evolution, might play a key role in the evolution of these single-stranded viruses. Indeed, recent work uncovered pervasive evidence for correlated evolution in influenza viruses (Nshogozabahizi et al ., 2017), and both the Zika (Aris-Brosou et al ., 2017) and the Ebola viruses (Ibeh et al ., 2016), with evidence in the latter that sites evolving in a correlated manner can also be under episodic diversifying selection. However, this recent body of evidence is so far limited to a small number of ssRNA viruses, so that the combined role of correlated evolution and selection is difficult to generalize.…”
11Viruses are known to have some of the highest and most diverse mutation rates found 12 in any biological replicator, with single-stranded (ss) RNA viruses evolving the fastest, 13 and double-stranded (ds) DNA viruses having rates approaching those of bacteria. As 14 mutation rates are tightly and negatively correlated with genome size, selection is a clear 15 driver of viral evolution. However, the role of intragenomic interactions as drivers of 16 viral evolution is still unclear. To understand how these two processes affect the long-17 term evolution of viruses infecting humans, we comprehensively analyzed ssRNA, ssDNA, 18 dsRNA, and dsDNA viruses, to find which virus types and which functions show evidence 19 for episodic diversifying selection and correlated evolution. We show that selection mostly 20 affects single stranded viruses, that correlated evolution is more prevalent in DNA viruses, 21 and that both processes, taken independently, mostly affect viral replication. However, 22 the genes that are jointly affected by both processes are involved in key aspects of their life 23 cycle, favoring viral stability over proliferation. We further show that both evolutionary 24 processes are intimately linked at the amino acid level, which suggests that it is the 25 joint action of selection and correlated evolution, and not just selection, that shapes the 26 evolutionary trajectories of viruses -and possibly of their epidemiological potential.27
“…To date, however, correlated evolution has only sporadically been investigated in viral evolution, and these rare instances only focused on ssRNA viruses. Indeed, recent work uncovered pervasive evidence for correlated evolution in influenza viruses [14,15], and both the Zika [16] and the Ebola viruses [17]. Intriguingly, in this latter case (Ebola), evidence was found that sites evolving in a correlated manner could also be under positive selection—bearing the question as to how frequently these two processes, correlated evolution and positive selection, occur, possibly jointly, and if this co-occurrence is limited to ssRNA viruses, or can be generalized to all viruses.…”
Viruses are known to have some of the highest and most diverse mutation rates found in any biological replicator, with single-stranded (ss) RNA viruses evolving the fastest, and double-stranded (ds) DNA viruses having rates approaching those of bacteria. As mutation rates are tightly and negatively correlated with genome size, selection is a clear driver of viral evolution. However, the role of intragenomic interactions as drivers of viral evolution is still unclear. To understand how these two processes affect the long-term evolution of viruses infecting humans, we comprehensively analyzed ssRNA, ssDNA, dsRNA, and dsDNA viruses, to find which virus types and which functions show evidence for episodic diversifying selection and correlated evolution. We show that selection mostly affects single stranded viruses, that correlated evolution is more prevalent in DNA viruses, and that both processes, taken independently, mostly affect viral replication. However, the genes that are jointly affected by both processes are involved in key aspects of their life cycle, favoring viral stability over proliferation. We further show that both evolutionary processes are intimately linked at the amino acid level, which suggests that it is the joint action of selection and correlated evolution, and not just selection, that shapes the evolutionary trajectories of viruses—and possibly of their epidemiological potential.
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