Effects of (Un)lockdown on COVID-19 transmission: A mathematical study of different phases in India

The novel coronavirus (SARS-CoV-2), identified in China at the end of the December 2019 is causing a potentially fatal respiratory syndrome (COVID-19), has meanwhile led to outbreak all over the globe. India has now become the third worst hit country globally with 16,38,870 confirmed cases and 35,747 confirmed deaths due to COVID-19 as of 31 July 2020. In this paper we have used mathematical modelling approach to study the effects of lockdowns and un-lockdowns on the pandemic evolution in India. This, study is based on SIDHARTHE model, which is an extension of classical SIR (Susceptible-Infected-Recovered) model. The SIDHARTHE model distinguish between the diagnosed and undiagnosed cases, which is very important because undiagnosed individuals are more likely to spread the virus than diagnosed individuals. We have stratified the lockdowns and un-lockdowns into seven phases and have computed the basic reproduction number R0 for each phase. We have calibrated our model results with real data from 20 March 2020 to 31 July 2020. Our results demonstrate that different strategies implemented by GoI, have delayed the peak of pandemic by approximately 100 days. But due to underdiagnosis of the infected asymptomatic subpopulation, a sudden outbreak of cases can be observed in India.

Multi-Omics and Integrated Network Approach to Unveil Evolutionary Patterns, Mutational Hotspots, Functional Crosstalk and Regulatory Interactions in SARS-CoV-2

SARS-CoV-2 pandemic resulted in 92 million cases in a span of one year. The study focuses on understanding population specific variations attributing its high rate of infections in specific geographical regions particularly in USA. Rigorous phylogenomic network analysis of complete SARS-CoV-2 genomes (245) inferred five central clades named a (ancestral), b, c, d and e (subtype e1 & e2). The clade d & e2 were found exclusively comprising of USA. Clades were distinguished by 10 co-mutational combinations in Nsp3, ORF8, Nsp13, S, Nsp12, Nsp2 and Nsp6. Our analysis revealed that only 67.46% of SNP mutations were at amino acid level. T1103P mutation in Nsp3 was predicted to increase protein stability in 238 strains except 6 strains which were marked as ancestral type; whereas co-mutation (P409L & Y446C) in Nsp13 were found in 64 genomes from USA highlighting its 100% co-occurrence. Docking highlighted mutation (D614G) caused reduction in binding of Spike proteins with ACE2, but it also showed better interaction with TMPRSS2 receptor contributing to high transmissibility among USA strains. We also found host proteins, MYO5A, MYO5B, MYO5C had maximum interaction with viral proteins (N, S, M). Thus, blocking the internalization pathway by inhibiting MYO5 proteins which could be an effective target for COVID-19 treatment. The functional annotations of the HPI network were found to be closely associated with hypoxia and thrombotic conditions confirming the vulnerability and severity of infection. We also screened CpG islands in Nsp1 & N conferring ability of SARS-CoV-2 to enter and trigger ZAP activity inside host cell. ImportanceIn the current study we presented a global view of mutational pattern observed in SARS-CoV-2 virus transmission. This provided a who-infect-whom geographical model since the early pandemic. This is hitherto the most comprehensive comparative genomics analysis of full-length genomes for co-mutations at different geographical regions specially in USA strains. Compositional structural biology results suggested that mutations have balance of contrary forces effect on pathogenicity suggesting only few mutations to effective at translation level but not all. Novel HPI analysis and CpG predictions elucidates the proof of concept of hypoxia and thrombotic conditions in several patients. Thus, the current study focuses the understanding of population specific variations attributing high rate of SARS-CoV-2 infections in specific geographical regions which may eventually be vital for the most severely affected countries and regions for sharp development of custom-made vindication strategies.

Complexity in SARS-CoV-2 genome data: Price theory of mutant isolates

SARS-CoV-2 is a highly virulent and deadly RNA virus causing the Covid-19 pandemic and several deaths across the world. The pandemic is so fast that any concrete theory of sudden widespread of this disease is still not known. In this work, we studied and analyzed a large number of publicly available SARS-CoV-2 genomes across the world using the multifractal approach. The mutation events in the isolates obey the Markov process and exhibit very high mutational rates, which occur in six specific genes and highest in orf1ab gene, leading to virulent nature. f () analysis indicated that the isolates are highly asymmetric (left-skewed), revealing the richness of complexity and dominance by large fluctuations in genome structure organization. The values of Hq and Dq are found to be significantly large, showing heterogeneous genome structure self-organization, strong positive correlation in organizing the isolates, and quite sensitive to fluctuations in and around it. We then present multiple-isolates hosts-virus interaction models, and derived Price equation for the model. The phase plane analysis of the model showed asymptotic stability type bifurcation. The competition among the mutant isolates drives the trade-off of the dominant mutant isolates, otherwise confined to the present hosts.

Design of an Epitope-Based Peptide Vaccine against the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2): A Vaccine Informatics Approach

The recurrent and recent global outbreak of SARS-CoV-2 has turned into a global concern which has infected more than 19-million people all over the globe, and this number is increasing in hours. Unfortunate no vaccine or specific treatment is available, which make it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides (including T and B cells) in the surface glycoprotein of SARS-CoV-2 which showed non-toxic nature, non-allergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that CD4+ T-cell peptides showed higher cumulative population coverage over to CD8+ peptides in the 16 different geographical regions of the world. We identified twelve peptides (LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are 80% - 90% identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that identified peptides are tightly bound in the groove of HLA molecules which can induce the T-cell response. Overall this study allows us to determine potent peptide antigen targets in surface glycoprotein on intuitive grounds which open up a new horizon in COVID-19 research. However, this study needs experimental validation by in vitro and in vivo.

ACE2 Homo-dimerization, Human Genomic variants and Interaction of Host Proteins Explain High Population Specific Differences in Outcomes of COVID19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive single-stranded RNA virus that causes a highly contagious Corona Virus Disease (COVID19). Entry of SARS-CoV-2 in human cells depends on binding of the viral spike (S) proteins to cellular receptor Angiotensin-converting enzyme 2 (ACE2) and on S-protein priming by host cell serine protease TMPRSS2. Recently, COVID19 has been declared pandemic by World Health Organization (WHO) yet high differences in disease outcomes across countries have been seen. We provide evidences to explain these population-level differences. One of the key factors of entry of the virus in host cells presumably is because of differential interaction of viral proteins with host cell proteins due to different genetic backgrounds. Based on our findings, we conclude that a higher expression of ACE2 is facilitated by natural variations, acting as Expression quantitative trait loci (eQTLs), with different frequencies in different populations. We suggest that high expression of ACE2 results in homo-dimerization, proving disadvantageous for TMPRSS2 mediated cleavage of ACE2; whereas, the monomeric ACE2 has higher preferential binding with SARS-CoV-2 S-Protein vis-a-vis its dimerized counterpart. Further, eQTLs in TMPRSS2 and natural structural variations in the gene may also result in differential outcomes towards priming of viral S-protein, a critical step for entry of the Virus in host cells. In addition, we suggest that several key host genes, like SLC6A19, ADAM17, RPS6, HNRNPA1, SUMO1, NACA, BTF3 and some other proteases as Cathepsins, might have a critical role. To conclude, understanding population specific differences in these genes may help in developing appropriate management strategies for COVID19 with better therapeutic interventions.