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Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers.

ABSTRACT: India became one of the most COVID-19 affected countries with more than 4 million infected cases and 71,000 deaths by September 2020. We studied the temporal dynamics and geographic distribution of SARS-CoV-2 subtypes in India. Moreover, we analysed the RGD motif and D614G mutation in the spike protein of SARS-CoV-2. We used a previously proposed viral subtyping method based upon informative subtype markers (ISMs). The ISMs were identified on the basis of information entropy using 94,515 genome sequences of SARS-CoV-2 available publicly at the Global Initiative on Sharing All Influenza Data (GISAID). We identified 11 distinct positions in the SARS-CoV-2 genomes for defining ISMs resulting in 798 unique ISMs. The most abundant ISM in India was transferred from European countries. In contrast, the second most abundant ISM in India was found to be transferred via Australia. Moreover, the eastern regions in India were infected by the ISM most abundant in China due to geographical linkage. Our analysis confirmed higher rates of new cases in the countries abundant with S-G614 strain compared to countries with abundant S-D614 strain. In India, overall S-G614 was most prevalent compared to S-D614, except a few regions including New Delhi, Bihar, and Rajasthan.

SUBMITTER: Mathur P

PROVIDER: S-EPMC8342543 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:We propose an efficient framework for genetic subtyping of SARS-CoV-2, the novel coronavirus that causes the COVID-19 pandemic. Efficient viral subtyping enables visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Subtyping thereby advances the development of effective containment strategies and, potentially, therapeutic and vaccine strategies. However, identifying viral subtypes in real-time is challenging: SARS-CoV-2 is a novel virus, and the pandemic is rapidly expanding. Viral subtypes may be difficult to detect due to rapid evolution; founder effects are more significant than selection pressure; and the clustering threshold for subtyping is not standardized. We propose to identify mutational signatures of available SARS-CoV-2 sequences using a population-based approach: an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Through ISM compression, we find that certain distant nucleotide variants covary, including non-coding and ORF1ab sites covarying with the D614G spike protein mutation which has become increasingly prevalent as the pandemic has spread. ISMs are also useful for downstream analyses, such as spatiotemporal visualization of viral dynamics. By analyzing sequence data available in the GISAID database, we validate the utility of ISM-based subtyping by comparing spatiotemporal analyses using ISMs to epidemiological studies of viral transmission in Asia, Europe, and the United States. In addition, we show the relationship of ISMs to phylogenetic reconstructions of SARS-CoV-2 evolution, and therefore, ISMs can play an important complementary role to phylogenetic tree-based analysis, such as is done in the Nextstrain project. The developed pipeline dynamically generates ISMs for newly added SARS-CoV-2 sequences and updates the visualization of pandemic spatiotemporal dynamics, and is available on Github at https://github.com/EESI/ISM (Jupyter notebook), https://github.com/EESI/ncov_ism (command line tool) and via an interactive website at https://covid19-ism.coe.drexel.edu/.

Project description:The novel coronavirus 2019 (COVID-19) global pandemic has drastically affected the world economy, raised public anxiety, and placed a substantial psychological burden on the governments and healthcare professionals by affecting over 4.7 million people worldwide. As a preventive measure to minimise the risk of community transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in India, a nationwide lockdown was imposed initially for 21 days to limit the movement of 1.3 billion people. These restrictions continue in most areas, with a conditional relaxation occurring in a few Indian states. In an attempt to assess the emerging mutants of SARS-CoV-2 and determine their spread in India, we analysed 112 complete genomes of SARS-CoV-2 in a time-lapse manner. We found 72 distinct SARS-CoV-2 haplotypes, defined by 143 polymorphic sites and high haplotype diversity, suggesting that this virus possesses a high evolutionary potential. We also demonstrated that early introduction of SARS-CoV-2 into India was from China, Italy and Iran and observed signs of community spread of the virus following its rapid demographic expansion since its first outbreak in the country. Additionally, we identified 18 mutations in the SARS-CoV-2 spike glycoprotein and a few selected mutations showed to increase stability, binding affinity, and molecular flexibility in the overall tertiary structure of the protein that may facilitate interaction between the receptor binding domain (RBD) of spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptor. The study provides a pragmatic view of haplotype-dependent spread of SARS-CoV-2 in India which could be important in tailoring the pharmacologic treatments to be more effective for those infected with the most common haplotypes. The findings based on the time-lapse sentinel surveillance of SARS-CoV-2 will aid in the development of a real-time practical framework to tackle the ongoing, fast-evolving epidemic challenges in the country.

Project description:BackgroundAlthough an increasing body of data reports the detection of SARS-CoV-2 RNA in air, this does not correlate to the presence of infectious viruses, thus not evaluating the risk for airborne COVID-19. Hence there is a marked knowledge gap that requires urgent attention. Therefore, in this systematic review, viability/stability of airborne SARS-CoV-2, SARS-CoV and MERS-CoV viruses is discussed.MethodsA systematic literature review was performed on PubMed/MEDLINE, Web of Science and Scopus to assess the stability and viability of SARS-CoV, MERS-CoV and SARS-CoV-2 on air samples.Results and discussionThe initial search identified 27 articles. Following screening of titles and abstracts and removing duplicates, 11 articles were considered relevant. Temperatures ranging from 20 °C to 25 °C and relative humidity ranging from 40% to 50% were reported to have a protective effect on viral viability for airborne SARS-CoV and MERS-CoV. As no data is yet available on the conditions influencing viability for airborne SARS-CoV-2, and given the genetic similarity to SARS-CoV and MERS-CoV, one could extrapolate that the same conditions would apply. Nonetheless, the effect of these conditions seems to be residual considering the increasing number of cases in the south of USA, Brazil and India, where high temperatures and humidities have been observed.ConclusionHigher temperatures and high relative humidity can have a modest effect on SARS-CoV-2 viability in the environment, as reported in previous studies to this date. However, these studies are experimental, and do not support the fact that the virus has efficiently spread in the tropical regions of the globe, with other transmission routes such as the contact and droplet ones probably being responsible for the majority of cases reported in these regions, along with other factors such as human mobility patterns and contact rates. Further studies are needed to investigate the extent of aerosol transmission of SARS-CoV-2 as this would have important implications for public health and infection-control policies.

Project description:BackgroundThe spread of SARS-CoV-2, originating in Wuhan, China, was classified as a pandemic by the World Health Organization on March 11, 2020. The governments of affected countries have implemented various measures to limit the spread of the virus. The starting point of this paper is the different government approaches, in terms of promulgating new legislative regulations to limit the virus diffusion and to contain negative effects on the populations.ObjectiveThis paper aims to study how the spread of SARS-CoV-2 is linked to government policies and to analyze how different policies have produced different results on public health.MethodsConsidering the official data provided by 4 countries (Italy, Germany, Sweden, and Brazil) and from the measures implemented by each government, we built an agent-based model to study the effects that these measures will have over time on different variables such as the total number of COVID-19 cases, intensive care unit (ICU) bed occupancy rates, and recovery and case-fatality rates. The model we implemented provides the possibility of modifying some starting variables, and it was thus possible to study the effects that some policies (eg, keeping the national borders closed or increasing the ICU beds) would have had on the spread of the infection.ResultsThe 4 considered countries have adopted different containment measures for COVID-19, and the forecasts provided by the model for the considered variables have given different results. Italy and Germany seem to be able to limit the spread of the infection and any eventual second wave, while Sweden and Brazil do not seem to have the situation under control. This situation is also reflected in the forecasts of pressure on the National Health Services, which see Sweden and Brazil with a high occupancy rate of ICU beds in the coming months, with a consequent high number of deaths.ConclusionsIn line with what we expected, the obtained results showed that the countries that have taken restrictive measures in terms of limiting the population mobility have managed more successfully than others to contain the spread of COVID-19. Moreover, the model demonstrated that herd immunity cannot be reached even in countries that have relied on a strategy without strict containment measures.

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Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers.

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