Highly Distinguished Amino Acid Sequences of 2019-nCoV (Wuhan Coronavirus)

Beal, Jacob; Mitchell, Tom; Wyschogrod, Daniel; Manthey, Jeff; Clore, Adam

doi:10.1101/2020.01.31.929497

Cited by 3 publications

(2 citation statements)

References 3 publications

(1 reference statement)

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“…To the best of our knowledge, there have been two bibliometric studies on CoV-related research in English before the COVID-19 pandemic ( 16 , 17 ). One study published in 2016 assessed the characteristics of publications only focused on the MERS-CoV ( 18 ). Another study ( 19 ) analyzed the global research trends of the World Health Organization's top eight emerging infections including Ebola, Marburg, MERS, Severe Acute Respiratory Syndrome (SARS), and so on, but publications related to CoV were not systematically analyzed.…”

Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis and Systematic Review of Global Coronavirus Research Trends Before COVID-19: Prospects and Implications for COVID-19 Research

Yan

Jing

et al. 2021

Front. Med.

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Coronaviruses (CoV) cause respiratory and intestinal infections. We conducted this bibliometric analysis and systematical review to explore the CoV-related research trends from before COVID-19. We systematically searched the Ovid MEDLINE, Ovid Embase, and Web of Science (WOS) databases for published bibliometric analyses of CoV from database inception to January 24, 2021. The WOS Collection was searched from inception to January 31, 2020, to acquire the CoV-related publications before COVID-19. One-Way ANOVA and Bonferroni multiple-comparison tests were used to compare differences. Visualization mapping and keyword cluster graphs were made to illustrate the research topics and hotpots. We included 14,141 CoV-related publications for the bibliometric analysis and 16 (12 articles) CoV-related bibliometric analyses for the systematic review. Both the systematic review and bibliometric analysis showed (1) the number of publications showed two steep upward trajectories in 2003–2004 and in 2012–2014; (2) the research hotpots mainly focused on the mechanism, pathology, epidemiology, clinical diagnosis, and treatment of the coronavirus in MERS-CoV and SARS-Cov; (3) the USA, and China; the University of Hong Kong; and Yuen KY, came from the University of Hong Kong contributed most; (4) the Journal of Virology had the largest number of CoV related studies. More studies should focus on prevention, diagnosis, and treatment in the future.

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Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis and Systematic Review of Global Coronavirus Research Trends Before COVID-19: Prospects and Implications for COVID-19 Research

Yan

Jing

et al. 2021

Front. Med.

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“…Since initial cases of clustered pneumonia infection in Wuhan were first reported to the WHO on December 31, 2019 [6] , worldwide medical researchers have been closely watching the situation and engaged in scientific analysis from a lot of dimensions. While some of them study the genome and protein characteristics of the COVID-19 for a better understanding [7][8][9][10][11] , others look into its potential primary reservoirs to prevent future zoonotic disease [12][13] . There are also research works upon medicine treatment [14] , vaccine development [15] , effectiveness of airport screen detecting [16] , etc.…”

Section: Related Workmentioning

confidence: 99%

D2EA: Depict the Epidemic Picture of COVID-19

Liu

Zhao

Liu

et al. 2020

J. Shanghai Jiaotong Univ. (Sci.)

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The outbreak of coronavirus disease 2019 has aroused a global alert. To release social panic and guide future schedules, this article proposes a novel mathematical model, the Delay Differential Epidemic Analyzer (D 2 EA), to analyze the dynamics of epidemic and forecast its future trends. Based on the traditional Susceptible-Exposed-Infectious-Recovered (SEIR) model, the D 2 EA model innovatively introduces a set of quarantine states and applies both ordinary differential equations and delay differential equations to describe the transition between two states. Potential variations of practical factors are further considered to reveal the true epidemic picture. In the experiment part, we use the D 2 EA model to simulate the epidemic in Hubei Province. Fitting to the collected real data as non-linear optimization, the D 2 EA model forecasts that the accumulated confirmed infected cases in Hubei Province will reach the peak at the end of February and then steady down. We also evaluate the effectiveness of the quarantine measures and schedule the date to reopen Hubei Province.Nomenclature D(t)-Deaths of disease, person E(t)-Free exposed population (also infectious), person I(t)-Free infectious population, person N (t)-Total of free populations, person QE(t)-Quarantined exposed population, person QI (t)-Quarantined confirmed infected cases, person QN (t)-Quarantined suspected but negative cases, person QP (t)-Quarantined suspected and positive cases, person QS(t)-Quarantined healthy susceptible population, person r(t)-Number of contact with one person per unit time, person/d R(t)-Recovered population (also free), person S(t)-Free susceptible population, person t-Time, d T -Time needed to confirm one suspected case, d α-Death rate of QI (t), d −1 αI -Death rate of I(t), d −1 βE-Probability of transmission per contact with E(t) βI -Probability of transmission per contact with I(t) γ(t)-Recovery rate of QI (t), d −1 γE-Recovery rate of E(t), d −1 γI -Recovery rate of I(t), d −1 δ(t)-Quarantine rate of I(t), d −1 ε-Rate of progression from exposed state to infectious state, d −1 η-Ratio of common cold sufferers to all susceptibles τ -Quarantine duration, d

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Review of the SARS-CoV-2 in Wuhan and Analysis as Well as Prediction of Therapeutic Drugs

Zhang

YanMei

2021

Viral Immunology

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Highly Distinguished Amino Acid Sequences of 2019-nCoV (Wuhan Coronavirus)

Cited by 3 publications

References 3 publications

Bibliometric Analysis and Systematic Review of Global Coronavirus Research Trends Before COVID-19: Prospects and Implications for COVID-19 Research

Bibliometric Analysis and Systematic Review of Global Coronavirus Research Trends Before COVID-19: Prospects and Implications for COVID-19 Research

D2EA: Depict the Epidemic Picture of COVID-19

Review of the SARS-CoV-2 in Wuhan and Analysis as Well as Prediction of Therapeutic Drugs

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