In-host Modelling of COVID-19 in Humans
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Abstract
COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat for human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. In this work, several mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Considering different starting times of infection, parameters sets that represent infectivity of SARS-CoV-2 are computed and compared with other viral infections that can also cause pandemics.
Based on the target cell model, SARS-CoV-2 infecting time between susceptible cells (mean of 30 days approximately) is much slower than those reported for Ebola (about 3 times slower) and influenza (60 times slower). The within-host reproductive number for SARS-CoV-2 is consistent to the values of influenza infection (1.7-5.35). The best model to fit the data was including immune responses, which suggest a slow cell response peaking between 5 to 10 days post onset of symptoms. The model with eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, both, the target cell model and the model with immune responses, predict that virus may replicate very slowly in the first days after infection, and it could be below detection levels during the first 4 days post infection. A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work.
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SciScore for 10.1101/2020.03.26.20044487: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Mathematical models: Mathematical models based on Ordinary Differential Equations (ODEs) are solved using the MATLAB library ode45, which is considered for solving non-stiff differential equations [58] MATLABsuggested: (MATLAB, RRID:SCR_001622)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are technical limitations in this study that need to be highlighted. The …
SciScore for 10.1101/2020.03.26.20044487: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Mathematical models: Mathematical models based on Ordinary Differential Equations (ODEs) are solved using the MATLAB library ode45, which is considered for solving non-stiff differential equations [58] MATLABsuggested: (MATLAB, RRID:SCR_001622)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are technical limitations in this study that need to be highlighted. The data for SARS-CoV-2 kinetics in [21] is at the onset of symptoms. This is a key aspect that can render biased parameter estimation as the target cell regularly is assumed to initiate at the day of the infection. In fact, we could miss viral dynamics at the onset of symptoms. For example, from throat samples in Rhesus macaques infected with SARS-CoV-2, two peaks were reported on most animals at 1 and 5 dpi [47]. In a more technical aspect using only viral load on the target cell model to estimate parameters may lead to identifiability problems [48–51]. Thus, our parameter values should be taken with caution when parameters quantifications are interpreted to address within-host mechanisms. For the model with immune system, there is not data confrontation with immune response predictions, thus, new measurements on cytokines and T cell responses would uncover new information. The race to develop the first vaccine to tackle COVID-19 has started with the first clinical trial just 60 days after the genetic sequence of the virus. Modelling work developed in this paper paves the way for future mathematical models of COVID-19 to reveal prophylactic and therapeutic interventions at multi-scale levels [52–57]. Further insights into immunology and pathogenesis of SARS-CoV-2 will help to improve the outcome of this and future pandemics.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.03.26.20044487: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources MATERIAL AND METHODS Mathematical models Mathematical models based on Ordinary Differential Equations ( ODEs ) are solved using the MATLAB library ode45 , which is considered for solving non-stiff differential equations [ 58] . MATLABsuggested: (MATLAB, SCR_001622)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by …
SciScore for 10.1101/2020.03.26.20044487: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources MATERIAL AND METHODS Mathematical models Mathematical models based on Ordinary Differential Equations ( ODEs ) are solved using the MATLAB library ode45 , which is considered for solving non-stiff differential equations [ 58] . MATLABsuggested: (MATLAB, SCR_001622)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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