Advances in the computational analysis of SARS-COV2 genome

Machado, J. A. Tenreiro; Rocha‐Neves, João; Azevedo, Filipe; Andrade, José Paulo

doi:10.1007/s11071-021-06836-y

Cited by 6 publications

(4 citation statements)

References 61 publications

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“…Furthermore, our platform is able to isolate the rare SARS-CoV-2 NP-specific clones, which typically recognize immunosubdominant epitopes. Owing to 90% homology between SARS-CoV-2 and SARS-CoV NP [ 32 , 33 ], many SARS-CoV-2 NP-binding antibodies cross-reacted SARS-CoV NP as well. Indeed, previously isolated antibodies from SARS-CoV NP-immunized mice were also reactive to SARS-CoV-2 , supporting that conserved epitopes between two viral NPs were immunodominant.…”

Section: Discussionmentioning

confidence: 99%

High-throughput isolation of SARS-CoV-2 nucleocapsid antibodies for improved antigen detection

Fujisawa

Adachi

Onodera

et al. 2023

Biochemical and Biophysical Research Communications

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Section: Discussionmentioning

confidence: 99%

High-throughput isolation of SARS-CoV-2 nucleocapsid antibodies for improved antigen detection

Fujisawa

Adachi

Onodera

et al. 2023

Biochemical and Biophysical Research Communications

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“…Odagaki 26 demonstrated that the oscillation of the infection curve could be self‐organized in the SIQR model, in which the net rate of change was a function of the number of infected individuals. Other approaches to the SARS‐CoV‐2 infection can be found in Tenreiro et al 27 (and references therein).…”

Section: Introductionmentioning

confidence: 99%

Modified SIQR model for the COVID‐19 outbreak in several countries

Pinto

Machado

Burgos³

2022

Math Methods in App Sciences

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In this paper, we propose a modified Susceptible‐Infected‐Quarantine‐Recovered (mSIQR) model, for the COVID‐19 pandemic. We start by proving the well‐posedness of the model and then compute its reproduction number and the corresponding sensitivity indices. We discuss the values of these indices for epidemiological relevant parameters, namely, the contact rate, the proportion of unknown infectious, and the recovering rate. The mSIQR model is simulated, and the outputs are fit to COVID‐19 pandemic data from several countries, including France, US, UK, and Portugal. We discuss the epidemiological relevance of the results and provide insights on future patterns, subjected to health policies.

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“…During the past few decades, various scientific approaches and mathematical methods were developed and applied to model the dynamics of the spread of infectious diseases [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Numerous studies have also examined the dynamics of SARS [ 21 , 22 , 23 , 24 ] and COVID-19 [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Complexity of COVID-19 Dynamics

Sivakumar

Deepthi

2021

Entropy

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With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. Although enormous efforts have been made to understand the spread of COVID-19, our knowledge of the COVID-19 dynamics still remains limited. The present study employs the concepts of chaos theory to examine the temporal dynamic complexity of COVID-19 around the world. The false nearest neighbor (FNN) method is applied to determine the dimensionality and, hence, the complexity of the COVID-19 dynamics. The methodology involves: (1) reconstruction of a single-variable COVID-19 time series in a multi-dimensional phase space to represent the underlying dynamics; and (2) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the COVID-19 series. For implementation, COVID-19 data from 40 countries/regions around the world are studied. Two types of COVID-19 data are analyzed: (1) daily COVID-19 cases; and (2) daily COVID-19 deaths. The results for the 40 countries/regions indicate that: (1) the dynamics of COVID-19 cases exhibit low- to medium-level complexity, with dimensionality in the range 3 to 7; and (2) the dynamics of COVID-19 deaths exhibit complexity anywhere from low to high, with dimensionality ranging from 3 to 13. The results also suggest that the complexity of the dynamics of COVID-19 deaths is greater than or at least equal to that of the dynamics of COVID-19 cases for most (three-fourths) of the countries/regions. These results have important implications for modeling and predicting the spread of COVID-19 (and other infectious diseases), especially in the identification of the appropriate complexity of models.

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Advances in the computational analysis of SARS-COV2 genome

Cited by 6 publications

References 61 publications

High-throughput isolation of SARS-CoV-2 nucleocapsid antibodies for improved antigen detection

High-throughput isolation of SARS-CoV-2 nucleocapsid antibodies for improved antigen detection

Modified SIQR model for the COVID‐19 outbreak in several countries

Complexity of COVID-19 Dynamics

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