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Register a new userEffective and scalable clustering of SARS-CoV-2 sequences
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SARS-CoV-2, like any other virus, continues to mutate as it spreads, according to an evolutionary process. Unlike any other virus, the number of currently available sequences of SARS-CoV-2 in public databases such as GISAID is already several million. This amount of data has the potential to uncover the evolutionary dynamics of a virus like never before. However, a million is already several orders of magnitude beyond what can be processed by the traditional methods designed to reconstruct a viruss evolutionary history, such as those that build a phylogenetic tree. Hence, new and scalable methods will need to be devised in order to make use of the ever increasing number of viral sequences being collected. Since identifying variants is an important part of understanding the evolution of a virus, in this paper, we propose an approach based on clustering sequences to identify the current major SARS-CoV-2 variants. Using a $k$-mer based feature vector generation and efficient feature selection methods, our approach is effective in identifying variants, as well as being efficient and scalable to millions of sequences. Such a clustering method allows us to show the relative proportion of each variant over time, giving the rate of spread of each variant in different locations -- something which is important for vaccine development and distribution. We also compute the importance of each amino acid position of the spike protein in identifying a given variant in terms of information gain. Positions of high variant-specific importance tend to agree with those reported by the USAs Centers for Disease Control and Prevention (CDC), further demonstrating our approach.
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Solar UV$-$C photons do not reach Earths surface, but are known to be endowed with germicidal properties that are also effective on viruses. The effect of softer UV$-$B and UV$-$A photons, which copiously reach the Earths surface, on viruses are instead little studied, particularly on single$-$stranded RNA viruses. Here we combine our measurements of the action spectrum of Covid$-$19 in response to UV light, Solar irradiation measurements on Earth during the SARS$-$CoV$-$2 pandemics, worldwide recorded Covid$-$19 mortality data and our Solar$-$Pump diffusive model of epidemics to show that (a) UV$-$B$/$A photons have a powerful virucidal effect on the single$-$stranded RNA virus Covid$-$19 and that (b) the Solar radiation that reaches temperate regions of the Earth at noon during summers, is sufficient to inactivate 63perc of virions in open$-$space concentrations (1.5 x 103 TCID50$/$mL, higher than typical aerosol) in less than 2 min. We conclude that the characteristic seasonality imprint displayed world$-$wide by the SARS$-$Cov$-$2 mortality time$-$series throughout the diffusion of the outbreak (with temperate regions showing clear seasonal trends and equatorial regions suffering, on average, a systematically lower mortality), might have been efficiently set by the different intensity of UV$-$B$/$A Solar radiation hitting different Earths locations at different times of the year. Our results suggest that Solar UV$-$B$/$A play an important role in planning strategies of confinement of the epidemics, which should be worked out and set up during spring$/$summer months and fully implemented during low$-$solar$-$irradiation periods.
A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals is responsible for the majority of new infections. The existence of superspreading implies a fat-tailed distribution of infectiousness (new secondary infections caused per day) among different individuals. Here, we present a simple method to estimate the variation in infectiousness by examining the variation in early-time growth rates of new cases among different subpopulations. We use this method to estimate the mean and variance in the infectiousness, $beta$, for SARS-CoV-2 transmission during the early stages of the pandemic within the United States. We find that $sigma_beta/mu_beta gtrsim 3.2$, where $mu_beta$ is the mean infectiousness and $sigma_beta$ its standard deviation, which implies pervasive superspreading. This result allows us to estimate that in the early stages of the pandemic in the USA, over 81% of new cases were a result of the top 10% of most infectious individuals.
Understanding the behaviour of hosts of SARS-CoV-2 is crucial to our understanding of the virus. A comparison of environmental features related to the incidence of SARS-CoV-2 with those of its potential hosts is critical. We examine the distribution of coronaviruses among bats. We analyse the distribution of SARS-CoV-2 in a nine-week period following lockdown in Italy, Spain, and Australia. We correlate its incidence with environmental variables particularly ultraviolet radiation, temperature, and humidity. We establish a clear negative relationship between COVID-19 and ultraviolet radiation, modulated by temperature and humidity. We relate our results with data showing that the bat species most vulnerable to coronavirus infection are those which live in environmental conditions that are similar to those that appear to be most favourable to the spread of COVID-19. The SARS-CoV-2 ecological niche has been the product of long-term coevolution of coronaviruses with their host species. Understanding the key parameters of that niche in host species allows us to predict circumstances where its spread will be most favourable. Such conditions can be summarised under the headings of nocturnality and seasonality. High ultraviolet radiation, in particular, is proposed as a key limiting variable. We therefore expect the risk of spread of COVID-19 to be highest in winter conditions, and in low light environments. Human activities resembling those of highly social cave-dwelling bats (e.g. large nocturnal gatherings or high density indoor activities) will only serve to compound the problem of COVID-19.
SARS-CoV-2 causing COVID-19 disease has moved rapidly around the globe, infecting millions and killing hundreds of thousands. The basic reproduction number, which has been widely used and misused to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, is dominated by a small number of individuals, and is driven by super-spreading events (SSEs). The distinct transmission features, such as high stochasticity under low prevalence, and the central role played by SSEs on transmission dynamics, should not be overlooked. Many explosive SSEs have occurred in indoor settings stoking the pandemic and shaping its spread, such as long-term care facilities, prisons, meat-packing plants, fish factories, cruise ships, family gatherings, parties and night clubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity that outbreak can be effectively contained with targeted interventions to eliminate SSEs. Here, we describe the potential types of SSEs, how they influence transmission, and give recommendations for control of SARS-CoV-2.
The COVID-19 pandemic has lead to a worldwide effort to characterize its evolution through the mapping of mutations in the genome of the coronavirus SARS-CoV-2. Ideally, one would like to quickly identify new mutations that could confer adaptive advantages (e.g. higher infectivity or immune evasion) by leveraging the large number of genomes. One way of identifying adaptive mutations is by looking at convergent mutations, mutations in the same genomic position that occur independently. However, the large number of currently available genomes precludes the efficient use of phylogeny-based techniques. Here, we establish a fast and scalable Topological Data Analysis approach for the early warning and surveillance of emerging adaptive mutations based on persistent homology. It identifies convergent events merely by their topological footprint and thus overcomes limitations of current phylogenetic inference techniques. This allows for an unbiased and rapid analysis of large viral datasets. We introduce a new topological measure for convergent evolution and apply it to the GISAID dataset as of February 2021, comprising 303,651 high-quality SARS-CoV-2 isolates collected since the beginning of the pandemic. We find that topologically salient mutations on the receptor-binding domain appear in several variants of concern and are linked with an increase in infectivity and immune escape, and for many adaptive mutations the topological signal precedes an increase in prevalence. We show that our method effectively identifies emerging adaptive mutations at an early stage. By localizing topological signals in the dataset, we extract geo-temporal information about the early occurrence of emerging adaptive mutations. The identification of these mutations can help to develop an alert system to monitor mutations of concern and guide experimentalists to focus the study of specific circulating variants.
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