Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics.
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ABSTRACT:
SUBMITTER: Standl F
PROVIDER: S-EPMC9759800 | biostudies-literature | 2021 Apr
REPOSITORIES: biostudies-literature
Publications
Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics.
Standl Fabian F Jöckel Karl-Heinz KH Brune Bastian B Schmidt Börge B Stang Andreas A
The Lancet. Infectious diseases 20200811 4
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Project description:The objective of this Personal View is to compare transmissibility, hospitalisation, and mortality rates for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with those of other epidemic coronaviruses, such as severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), and pandemic influenza viruses. The basic reproductive rate (R0) for SARS-CoV-2 is estimated to be 2·5 (range 1·8-3·6) compared with 2·0-3·0 for SARS-CoV and the 1918 influenza pandemic, 0·9 for MERS-CoV, and 1·5 for the 2009 influenza pandemic. SARS-CoV-2 causes mild or asymptomatic disease in most cases; however, severe to critical illness occurs in a small proportion of infected individuals, with the highest rate seen in people older than 70 years. The measured case fatality rate varies between countries, probably because of differences in testing strategies. Population-based mortality estimates vary widely across Europe, ranging from zero to high. Numbers from the first affected region in Italy, Lombardy, show an all age mortality rate of 154 per 100 000 population. Differences are most likely due to varying demographic structures, among other factors. However, this new virus has a focal dissemination; therefore, some areas have a higher disease burden and are affected more than others for reasons that are still not understood. Nevertheless, early introduction of strict physical distancing and hygiene measures have proven effective in sharply reducing R0 and associated mortality and could in part explain the geographical differences.
| S-EPMC7333991 | biostudies-literature
Project description:Abstract The large spatial scale, geographical overlap, and similarities in transmission mode between the 1918 H1N1 influenza and 2020 SARS-CoV-2 pandemics together provide a novel opportunity to investigate relationships between transmission of two different diseases in the same location. To this end, we use initial exponential growth rates in a Bayesian hierarchical framework to estimate the basic reproductive number, R0, of both disease outbreaks in a common set of 43 cities in the United States. By leveraging multiple epidemic time series across a large spatial area, we are able to better characterize the variation in R0 across the United States. Additionally, we provide one of the first city-level comparisons of R0 between these two pandemics and explore how demography and outbreak timing are related to R0. Despite similarities in transmission modes and a common set of locations, R0 estimates for COVID-19 were uncorrelated with estimates of pandemic influenza R0 in the same cities. Also, the relationships between R0 and key population or epidemic traits differed between diseases. For example, epidemics that started later tended to be less severe for COVID-19, while influenza epidemics exhibited an opposite pattern. Our results suggest that despite similarities between diseases, epidemics starting in the same location may differ markedly in their initial progression.
| S-EPMC9802102 | biostudies-literature
Project description:IntroductionVaccination continues to be the most effective method for controlling COVID-19 infectious diseases. Nonetheless, SARS-CoV-2 variants continue to evolve and emerge, resulting in significant public concerns worldwide, even after more than 2 years since the COVID-19 pandemic. It is important to better understand how different COVID-19 vaccine platforms work, why SARS-CoV-2 variants continue to emerge, and what options for improving COVID-19 vaccines can be considered to fight against SARS-CoV-2 variants and future pandemics.Area coveredHere, we reviewed the innate immune sensors in the recognition of SARS-CoV-2 virus, innate and adaptive immunity including neutralizing antibodies by different COVID-19 vaccines. Efficacy comparison of the several COVID-19 vaccine platforms approved for use in humans, concerns about SARS-CoV-2 variants and breakthrough infections, and the options for developing future COIVD-19 vaccines were also covered.Expert opinionOwing to the continuous emergence of novel pathogens and the reemergence of variants, safer and more effective new vaccines are needed. This review also aims to provide the knowledge basis for the development of next-generation COVID-19 and pan-coronavirus vaccines to provide cross-protection against new SARS-CoV-2 variants and future coronavirus pandemics.
| S-EPMC9979704 | biostudies-literature
Project description:BackgroundMany new variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been termed variants of concern/interest (VOC/I) because of the greater risk they pose due to possible enhanced transmissibility and/or severity, immune escape, diagnostic and/or treatment failure, and reduced vaccine efficacy.AimsWe sought to review the current knowledge of emerging SARS-CoV-2 variants, particularly those deemed VOC/Is: B.1.351, B.1.1.7, and P.1.SourcesMEDLINE and BioRxiv databases, as well as the grey literature, were searched for reports of SARS-CoV-2 variants since November 2020. Relevant articles and their references were screened.ContentMutations on the spike protein in particular may affect both affinity for the SARS-CoV-2 cell receptor ACEII and antibody binding. These VOC/Is often share similar mutation sets. The N501Y mutation is shared by the three main VOCs: B.1.1.7, first identified in the United Kingdom, P.1, originating from Brazil, and B.1.351, first described in South Africa. This mutation likely increases transmissibility by increasing affinity for ACEII. The B.1.351 and P.1 variants also display the E484K mutation which decreases binding of neutralizing antibodies, leading to partial immune escape; this favours reinfections, and decreases the in vitro efficacy of some antibody therapies or vaccines. Those mutations may also have phenotypical repercussions of greater severity. Furthermore, the accumulation of mutations poses a diagnostic risk (lowered when using multiplex assays), as seen for some assays targeting the S gene. With ongoing surveillance, many new VOC/Is have been identified. The emergence of the E484K mutation independently in different parts of the globe may reflect the adaptation of SARS-CoV-2 to humans against a background of increasing immunity.ImplicationsThese VOC/Is are increasing in frequency globally and pose challenges to any herd immunity approach to managing the pandemic. While vaccination is ongoing, vaccine updates may be prudent. The virus continues to adapt to transmission in humans, and further divergence from the initial Wuhan sequences is expected.
| S-EPMC8127517 | biostudies-literature
Project description:Ebola virus is among the most dangerous, contagious and deadly etiological causes of viral diseases. However, Ebola virus has never extensively spread in human population and never have led to a pandemic. Why? The mechanistic biophysical model revealing the biothermodynamic background of virus-host interaction) could help us to understand pathogenesis of Ebola virus disease (earlier known as the Ebola hemorrhagic fever). In this paper for the first time the empirical formula, thermodynamic properties of biosynthesis (including the driving force of virus multiplication in the susceptible host), binding constant and thermodynamic properties of binding are reported. Thermodynamic data for Ebola virus were compared with data for SARS-CoV-2 to explain why SARS-CoV-2 has caused a pandemic, while Ebola remains on local epidemic level. The empirical formula of the Ebola virus was found to be CH1.569O0.3281N0.2786P0.00173S0.00258. Standard Gibbs energy of biosynthesis of the Ebola virus nucleocapsid is -151.59 kJ/C-mol.
| S-EPMC9597532 | biostudies-literature
Project description:SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein-protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.
| S-EPMC8988119 | biostudies-literature
Project description:BackgroundWith an influenza pandemic seemingly imminent, we constructed a model simulating the spread of influenza within the community, in order to test the impact of various interventions.MethodsThe model includes an individual level, in which the risk of influenza virus infection and the dynamics of viral shedding are simulated according to age, treatment, and vaccination status; and a community level, in which meetings between individuals are simulated on randomly generated graphs. We used data on real pandemics to calibrate some parameters of the model. The reference scenario assumes no vaccination, no use of antiviral drugs, and no preexisting herd immunity. We explored the impact of interventions such as vaccination, treatment/prophylaxis with neuraminidase inhibitors, quarantine, and closure of schools or workplaces.ResultsIn the reference scenario, 57% of realizations lead to an explosive outbreak, lasting a mean of 82 days (standard deviation (SD) 12 days) and affecting 46.8% of the population on average. Interventions aimed at reducing the number of meetings, combined with measures reducing individual transmissibility, would be partly effective: coverage of 70% of affected households, with treatment of the index patient, prophylaxis of household contacts, and confinement to home of all household members, would reduce the probability of an outbreak by 52%, and the remaining outbreaks would be limited to 17% of the population (range 0.8%-25%). Reactive vaccination of 70% of the susceptible population would significantly reduce the frequency, size, and mean duration of outbreaks, but the benefit would depend markedly on the interval between identification of the first case and the beginning of mass vaccination. The epidemic would affect 4% of the population if vaccination started immediately, 17% if there was a 14-day delay, and 36% if there was a 28-day delay. Closing schools when the number of infections in the community exceeded 50 would be very effective, limiting the size of outbreaks to 10% of the population (range 0.9%-22%).ConclusionThis flexible tool can help to determine the interventions most likely to contain an influenza pandemic. These results support the stockpiling of antiviral drugs and accelerated vaccine development.
| S-EPMC1626479 | biostudies-literature
Project description:Lower and upper respiratory infections are the fourth highest cause of global mortality (Lozano et al., 2012). Epidemic and pandemic outbreaks of respiratory infection are a major medical concern, often causing considerable disease and a high death toll, typically over a relatively short period of time. Influenza is a major cause of epidemic and pandemic infection. Bacterial co/secondary infection further increases morbidity and mortality of influenza infection, with Streptococcus pneumoniae, Haemophilus influenzae, and Staphylococcus aureus reported as the most common causes. With increased antibiotic resistance and vaccine evasion it is important to monitor the epidemiology of pathogens in circulation to inform clinical treatment and development, particularly in the setting of an influenza epidemic/pandemic.
| S-EPMC5481322 | biostudies-other
Project description: Not available
| S-BSST379 | biostudies-other
Project description: Not available
| S-EPMC1283192 | biostudies-literature