scholarly journals Phylogenomics and phylodynamics of SARS-CoV-2 retrieved genomes from India

2020 ◽  
Author(s):  
Sameera Farah ◽  
Ashwin Atkulwar ◽  
Manas Ranjan Praharaj ◽  
Raja Khan ◽  
Ravikumar Gandham ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Vaccine Development ◽  
Reproduction Number ◽  
Bayesian Skyline Plot ◽  
Development Programs ◽  
Effective Population ◽  
Whole Genomes ◽  
Indian Isolates ◽  
Spanish Flu ◽  
Community Transmission

AbstractThe ongoing SARS-CoV-2 pandemic is one of the biggest outbreaks after the Spanish flu of 1918. Understanding the epidemiology of viral outbreaks is the first step towards vaccine development programs. This is the first phylodynamics study attempted on of SARS-CoV-2 genomes from India to infer its current evolution in the context of an ongoing pandemic. Out of 286 retrieved SARS-CoV-2 whole genomes from India, 138 haplotypes were generated and analyzed. Median-joining network was built to investigate the relatedness of SARS-CoV-2 haplotypes in India. The BDSIR package of BEAST2 was used to calculate the reproduction number (R0) and the infectious rate of the virus. Past and current population trend was investigated using the stamp date method in coalescent Bayesian skyline plot, implemented in BEAST2 and by exponential growth prior in BEAST 1.10.4. Median-joining network reveals two distinct ancestral clusters A and B showing genetic affinities with Wuhan outbreak sample. The network also illustrates the autochthonous development of isolates in a few instances. High basic reproduction number of SARS-nCoV-2 in India points towards the phase of active community transmission. The Bayesian skyline plot revel exponential rise in the effective population size (Ne) of Indian isolates from the first week of January to the first week of April 2020. More genome sequencing and analyses of the virus will be required in coming days to monitor COVID19 after the upliftment of lock down in India.

scholarly journals Phylogenomics and phylodynamics of SARS-CoV-2 genomes retrieved from India

2020 ◽  
Vol 15 (11) ◽  
pp. 747-753
Author(s):  
Sameera Farah ◽  
Ashwin Atkulwar ◽  
Manas Ranjan Praharaj ◽  
Raja Khan ◽  
Ravikumar Gandham ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Severe Acute Respiratory Syndrome ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Phylogenetic Network ◽  
Effective Population ◽  
Current State ◽  
Whole Genomes ◽  
Indian Isolates ◽  
Community Transmission

Background: This is the first phylodynamic study attempted on SARS-CoV-2 genomes from India to infer the current state of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution using phylogenetic network and growth trends. Materials & Methods: Out of 286 retrieved whole genomes from India, 138 haplotypes were used to build a phylogenetic network. The birth–death serial model (BDSIR) package of BEAST2 was used to calculate the reproduction number of SARS-CoV-2. Population dynamics were investigated using the stamp date method as implemented in BEAST2 and BEAST 1.10.4. Results: A median-joining network revealed two ancestral clusters. A high basic reproduction number of SARS-CoV-2 was found. An exponential rise in the effective population size of Indian isolates was detected. Conclusion: The phylogenetic network reveals dual ancestry and possibility of community transmission of SARS-CoV-2 in India.

scholarly journals The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: a modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fatima Khadadah ◽  
Abdullah A. Al-Shammari ◽  
Ahmad Alhashemi ◽  
Dari Alhuwail ◽  
Bader Al-Saif ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Model ◽  
Socioeconomic Groups ◽  
Before And After ◽  
Cross Transmission ◽  
Community Transmission ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
The Basic Reproduction Number

Abstract Background Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV-2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait. Methods We fit a modified metapopulation SEIR transmission model to reported cases stratified by two groups to estimate the impact of a partial lockdown on the effective reproduction number ($$ {\mathcal{R}}_e $$ R e ). We estimated the basic reproduction number ($$ {\mathcal{R}}_0 $$ R 0 ) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the partial lockdown. We estimated $$ {\mathcal{R}}_e $$ R e values of both groups before and after the partial curfew, simulated the effect of these values on the epidemic curves and explored a range of cross-transmission scenarios. Results We estimate $$ {\mathcal{R}}_e $$ R e at 1·08 (95% CI: 1·00–1·26) for P1 and 2·36 (2·03–2·71) for P2. On March 22nd, $$ {\mathcal{R}}_e $$ R e for P1 and P2 are estimated at 1·19 (1·04–1·34) and 1·75 (1·26–2·11) respectively. After the partial curfew had taken effect, $$ {\mathcal{R}}_e $$ R e for P1 dropped modestly to 1·05 (0·82–1·26) but almost doubled for P2 to 2·89 (2·30–3·70). Our simulated epidemic trajectories show that the partial curfew measure greatly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission between P1 and P2 greatly elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. Conclusion Our results indicate and quantify how the same lockdown intervention can accentuate disease transmission in some subpopulations while potentially controlling it in others. Any such control may further become compromised in the presence of cross-transmission between subpopulations. Future interventions and policies need to be sensitive to socioeconomic and health disparities.

SARS-CoV-2: Molecular biology and therapeutic target.

Coronaviruses ◽  
2021 ◽  
Vol 02 ◽  
Author(s):  
Vivek Pandey ◽  
Ankita Pathak ◽  
Mohammad Shahar Yar ◽  
Yuba Raj Pokharel
Keyword(s):  
Viral Genome ◽  
Vaccine Development ◽  
Viral Proteins ◽  
Current Review ◽  
The World ◽  
Spanish Flu ◽  
Repurposed Drugs ◽  
Key Features ◽  
Economic Infrastructure ◽  
Viral Outbreak

: A century after the outbreak of the Spanish flu, the world is suffering with another pandemic on because of the coronavirus. The virus took a toll of more than millions of lives worldwide and still continues to affect the health and socio-economic infrastructure all over the world. The study explores the epidemiology, etiology and transmission of the virus and its phylogenetic relationship with SARS and MERS coronavirus responsible for 2002 and 2012 viral outbreak. Highlights about the key features of the viral genome and essential viral proteins responsible for viral life cycle, evading host immune response, and viral immunopathology with therapeutics from “Recovery” and “Solidarity” trials, are major concern of the current review. The review culminated with a discussion on different classes of front-runners vaccines and their efficacy. An overall understanding of essential viral proteins and their role in pathogenesis, repurposed drugs and vaccine development is the rationale of the present review.

scholarly journals SARS-CoV-2 lineage dynamics in England from January to March 2021 inferred from representative community samples

2021 ◽  
Author(s):  
Oliver Eales ◽  
Andrew Page ◽  
Sonja N. Tang ◽  
Caroline E. Walters ◽  
Haowei Wang ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Sequence Data ◽  
Community Sample ◽  
Age Groups ◽  
Health Interventions ◽  
Wild Type ◽  
Rt Pcr ◽  
Community Surveys ◽  
Community Transmission ◽  
Middle Aged Adults

Genomic surveillance for SARS-CoV-2 lineages informs our understanding of possible future changes in transmissibility and vaccine efficacy. However, small changes in the frequency of one lineage over another are often difficult to interpret because surveillance samples are obtained from a variety of sources. Here, we describe lineage dynamics and phylogenetic relationships using sequences obtained from a random community sample who provided a throat and nose swab for rt-PCR during the first three months of 2021 as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Overall, diversity decreased during the first quarter of 2021, with the B.1.1.7 lineage (first identified in Kent) predominant, driven by a 0.3 unit higher reproduction number over the prior wild type. During January, positive samples were more likely B.1.1.7 in younger and middle-aged adults (aged 18 to 54) than in other age groups. Although individuals infected with the B.1.1.7 lineage were no more likely to report one or more classic COVID-19 symptoms compared to those infected with wild type, they were more likely to be antibody positive 6 weeks after infection. Viral load was higher in B.1.1.7 infection as measured by cycle threshold (Ct) values, but did not account for the increased rate of testing positive for antibodies. The presence of infections with non-imported B.1.351 lineage (first identified in South Africa) during January, but not during February or March, suggests initial establishment in the community followed by fade-out. However, this occurred during a period of stringent social distancing and targeted public health interventions and does not immediately imply similar lineages could not become established in the future. Sequence data from representative community surveys such as REACT-1 can augment routine genomic surveillance.

scholarly journals SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2

2021 ◽  
Author(s):  
Oliver Eales ◽  
Andrew Page ◽  
Leonardo de Oliveira Martins ◽  
Haowei Wang ◽  
Barbara Bodinier ◽  
...  
Keyword(s):  
Mutation Rate ◽  
Immune Escape ◽  
Evolutionary Rate ◽  
Reproduction Number ◽  
Natural Infection ◽  
Community Sample ◽  
Rt Pcr ◽  
Slowing Down ◽  
Routine Surveillance ◽  
Community Transmission

Since the emergence of SARS-CoV-2, evolutionary pressure has driven large increases in the transmissibility of the virus. However, with increasing levels of immunity through vaccination and natural infection the evolutionary pressure will switch towards immune escape. Here we present phylogenetic relationships and lineage dynamics within England (a country with high levels of immunity), as inferred from a random community sample of individuals who provided a self-administered throat and nose swab for rt-PCR testing as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. From 9 to 27 September 2021 (round 14) and 19 October to 5 November 2021 (round 15), all lineages sequenced within REACT-1 were Delta or a Delta sub-lineage with 44 unique lineages identified. The proportion of the original Delta variant (B.1.617.2) was found to be increasing between September and November 2021, which may reflect an increasing number of sub-lineages which have yet to be identified. The proportion of B.1.617.2 was greatest in London, which was further identified as a region with an increased level of genetic diversity. The Delta sub-lineage AY.4.2 was found to be robustly increasing in proportion, with a reproduction number 15% (8%, 23%) greater than its parent and most prevalent lineage, AY.4. Both AY.4.2 and AY.4 were found to be geographically clustered in September but this was no longer the case by late October/early November, with only the lineage AY.6 exhibiting clustering towards the South of England. Though no difference in the viral load based on cycle threshold (Ct) values was identified, a lower proportion of those infected with AY.4.2 had symptoms for which testing is usually recommend (loss or change of sense of taste, loss or change of sense of smell, new persistent cough, fever), compared to AY.4 (p = 0.026). The evolutionary rate of SARS-CoV-2, as measured by the mutation rate, was found to be slowing down during the study period, with AY.4.2 further found to have a reduced mutation rate relative to AY.4. As SARS-CoV-2 moves towards endemicity and new variants emerge, genomic data obtained from random community samples can augment routine surveillance data without the potential biases introduced due to higher sampling rates of symptomatic individuals.

scholarly journals Streamlined Subpopulation, Subtype, and Recombination Analysis of HIV-1 Half-Genome Sequences Generated by High-Throughput Sequencing

mSphere ◽  
2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Bhavna Hora ◽  
Naila Gulzar ◽  
Yue Chen ◽  
Konstantinos Karagiannis ◽  
Fangping Cai ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Vaccine Development ◽  
Consensus Sequence ◽  
Cost Effective ◽  
Recombination Analysis ◽  
Genome Sequences ◽  
Single Genome ◽  
Hiv 1

ABSTRACT High-throughput sequencing (HTS) has been widely used to characterize HIV-1 genome sequences. There are no algorithms currently that can directly determine genotype and quasispecies population using short HTS reads generated from long genome sequences without additional software. To establish a robust subpopulation, subtype, and recombination analysis workflow, we amplified the HIV-1 3′-half genome from plasma samples of 65 HIV-1-infected individuals and sequenced the entire amplicon (∼4,500 bp) by HTS. With direct analysis of raw reads using HIVE-hexahedron, we showed that 48% of samples harbored 2 to 13 subpopulations. We identified various subtypes (17 A1s, 4 Bs, 27 Cs, 6 CRF02_AGs, and 11 unique recombinant forms) and defined recombinant breakpoints of 10 recombinants. These results were validated with viral genome sequences generated by single genome sequencing (SGS) or the analysis of consensus sequence of the HTS reads. The HIVE-hexahedron workflow is more sensitive and accurate than just evaluating the consensus sequence and also more cost-effective than SGS. IMPORTANCE The highly recombinogenic nature of human immunodeficiency virus type 1 (HIV-1) leads to recombination and emergence of quasispecies. It is important to reliably identify subpopulations to understand the complexity of a viral population for drug resistance surveillance and vaccine development. High-throughput sequencing (HTS) provides improved resolution over Sanger sequencing for the analysis of heterogeneous viral subpopulations. However, current methods of analysis of HTS reads are unable to fully address accurate population reconstruction. Hence, there is a dire need for a more sensitive, accurate, user-friendly, and cost-effective method to analyze viral quasispecies. For this purpose, we have improved the HIVE-hexahedron algorithm that we previously developed with in silico short sequences to analyze raw HTS short reads. The significance of this study is that our standalone algorithm enables a streamlined analysis of quasispecies, subtype, and recombination patterns from long HIV-1 genome regions without the need of additional sequence analysis tools. Distinct viral populations and recombination patterns identified by HIVE-hexahedron are further validated by comparison with sequences obtained by single genome sequencing (SGS).

Complete genome sequencing and evolutionary analysis of Indian isolates of Dengue virus type 2

2013 ◽  
Vol 436 (3) ◽  
pp. 478-485 ◽  
Author(s):  
Paban Kumar Dash ◽  
Shashi Sharma ◽  
Manisha Soni ◽  
Ankita Agarwal ◽  
Manmohan Parida ◽  
...  
Keyword(s):  
Dengue Virus ◽  
Genome Sequencing ◽  
Virus Type ◽  
Complete Genome ◽  
Evolutionary Analysis ◽  
Complete Genome Sequencing ◽  
Indian Isolates ◽  
Dengue Virus Type 2

scholarly journals Comparative estimation of the reproduction number for pandemic influenza from daily case notification data

2006 ◽  
Vol 4 (12) ◽  
pp. 155-166 ◽  
Author(s):  
Gerardo Chowell ◽  
Hiroshi Nishiura ◽  
Luís M.A Bettencourt
Keyword(s):  
Growth Phase ◽  
Pandemic Influenza ◽  
Exponential Growth ◽  
Goodness Of Fit ◽  
Reproduction Number ◽  
Exponential Growth Phase ◽  
Initial Growth ◽  
Epidemic Curve ◽  
Spanish Flu ◽  
The City

The reproduction number, , defined as the average number of secondary cases generated by a primary case, is a crucial quantity for identifying the intensity of interventions required to control an epidemic. Current estimates of the reproduction number for seasonal influenza show wide variation and, in particular, uncertainty bounds for for the pandemic strain from 1918 to 1919 have been obtained only in a few recent studies and are yet to be fully clarified. Here, we estimate using daily case notifications during the autumn wave of the influenza pandemic (Spanish flu) in the city of San Francisco, California, from 1918 to 1919. In order to elucidate the effects from adopting different estimation approaches, four different methods are used: estimation of using the early exponential-growth rate (Method 1), a simple susceptible–exposed–infectious–recovered (SEIR) model (Method 2), a more complex SEIR-type model that accounts for asymptomatic and hospitalized cases (Method 3), and a stochastic susceptible–infectious–removed (SIR) with Bayesian estimation (Method 4) that determines the effective reproduction number at a given time t . The first three methods fit the initial exponential-growth phase of the epidemic, which was explicitly determined by the goodness-of-fit test. Moreover, Method 3 was also fitted to the whole epidemic curve. Whereas the values of obtained using the first three methods based on the initial growth phase were estimated to be 2.98 (95% confidence interval (CI): 2.73, 3.25), 2.38 (2.16, 2.60) and 2.20 (1.55, 2.84), the third method with the entire epidemic curve yielded a value of 3.53 (3.45, 3.62). This larger value could be an overestimate since the goodness-of-fit to the initial exponential phase worsened when we fitted the model to the entire epidemic curve, and because the model is established as an autonomous system without time-varying assumptions. These estimates were shown to be robust to parameter uncertainties, but the theoretical exponential-growth approximation (Method 1) shows wide uncertainty. Method 4 provided a maximum-likelihood effective reproduction number 2.10 (1.21, 2.95) using the first 17 epidemic days, which is consistent with estimates obtained from the other methods and an estimate of 2.36 (2.07, 2.65) for the entire autumn wave. We conclude that the reproduction number for pandemic influenza (Spanish flu) at the city level can be robustly assessed to lie in the range of 2.0–3.0, in broad agreement with previous estimates using distinct data.

scholarly journals Predicting the course of Covid-19 and other epidemic and endemic disease

2021 ◽  
Author(s):  
Ana Cascon ◽  
William F Shadwick
Keyword(s):  
Intensive Care ◽  
Predictive Power ◽  
Respiratory Virus ◽  
Linear Growth ◽  
Hospital Admissions ◽  
Reproduction Number ◽  
Value Theory ◽  
Gompertz Function ◽  
Peak Loads ◽  
Spanish Flu

The Gompertz Function is an accurate model for epidemics from Cholera in 1853 to Spanish Flu in 1918 and Ebola in 2014. It also describes the acute phase of annual outbreaks of endemic influenza and in all of these instances it has significant predictive power. For Covid-19, we show that the Gompertz Function provides accurate forecasts not just for cases and deaths but, independently, for hospitalisations, intensive care admissions and other medical requirements. In particular Gompertz Function projections of healthcare requirements have been reliable enough to allow planning for: hospital admissions,intensive care admissions,ventilator usage, peak loads and duration. Analysis of data from the Spanish Flu pandemic and the endemic influenza cycle reveals alternating periods of Gompertz Function growth and linear growth in cumulative cases or deaths. Linear growth means the Reproduction Number is equal to 1 which in turn indicates endemicity. The same pattern has been observed with Covid-19. All the initial outbreaks ended in linear growth. Each new outbreak has been preceded by a period of linear growth and has ended with a transition from Gompertz Function growth to linear growth. This suggests that each of these outbreak cycles ended with a transition to endemicity for the current dominant strain and that the normal seasonal respiratory virus periods will continue to see new outbreaks. It remains to be seen if widespread vaccination will disrupt this cyclicality. Because both Gompertz Function Growth and linear growth are accurately predictable, the forecasting problem is reduced to identifying the transition between these modes and to improving the performance in the early Gompertz Function growth phase where its predictive power is lowest. The dynamics of the Gompertz Function are determined by the Gumbel probability distribution. This is an exceptional distribution with respect to the geometry determined by the affine group on the line which is the key to the Gumbel distribution's role as an Extreme Value Theory attractor. We show that this, together with the empirically observed asymmetry in epidemic data, makes the Gompertz Function growth essentially inevitable in epidemic models which agree with observations.

scholarly journals Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models

2019 ◽  
Author(s):  
Kris V Parag ◽  
Christl A Donnelly
Keyword(s):  
Reproduction Number ◽  
Adaptive Estimation ◽  
Minimum Description Length ◽  
Target Population ◽  
Information Criteria ◽  
Classification Problems ◽  
Effective Population ◽  
Piecewise Constant ◽  
Statistical Complexity ◽  
Future Growth

AbstractEstimating temporal changes in a target population from phylogenetic or count data is an important problem in ecology and epidemiology. Reliable estimates can provide key insights into the climatic and biological drivers influencing the diversity or structure of that population and evidence hypotheses concerning its future growth or decline. In infectious disease applications, the individuals infected across an epidemic form the target population. The renewal model estimates the effective reproduction number, R, of the epidemic from counts of its observed cases. The skyline model infers the effective population size, N, underlying a phylogeny of sequences sampled from that epidemic. Practically, R measures ongoing epidemic growth while N informs on historical caseload. While both models solve distinct problems, the reliability of their estimates depends on p-dimensional piecewise-constant functions. If p is misspecified, the model might underfit significant changes or overfit noise and promote a spurious understanding of the epidemic, which might misguide intervention policies or misinform forecasts. Surprisingly, no transparent yet principled approach for optimising p exists. Usually, p is heuristically set, or obscurely controlled via complex algorithms. We present a computable and interpretable p-selection method based on the minimum description length (MDL) formalism of information theory. Unlike many standard model selection techniques, MDL accounts for the additional statistical complexity induced by how parameters interact. As a result, our method optimises p so that R and N estimates properly adapt to the available data. It also outperforms comparable Akaike and Bayesian information criteria on several classification problems. Our approach requires some knowledge of the parameter space and exposes the similarities between renewal and skyline models.

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