Cooperative virus propagation in COVID-19 transmission
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Abstract
The global pandemic due to the emergence of a novel coronavirus (COVID-19) is a threat to humanity. There remains an urgent need to understand its transmission characteristics and design effective interventions to mitigate its spread. In this study, we define a non-linear (known in biochemistry models as allosteric or cooperative) relationship between viral shedding, viral dose and COVID-19 infection propagation. We develop a mathematical model of the dynamics of COVID-19 to link quantitative features of viral shedding, human exposure and transmission in nine countries impacted by the ongoing COVID-19 pandemic, and state-wide transmission in the United States of America (USA). The model was then used to evaluate the efficacy of interventions against virus transmission. We found that cooperativity was important to capture country-specific transmission dynamics and leads to resistance to mitigating transmission in mild or moderate interventions. The behaviors of the model emphasize that strict interventions greatly limiting both virus shedding and human exposure are indispensable to achieving effective containment of COVID-19. Finally, in the USA we find that by the summer of 2021, a difference of about 1.5 million deaths may be observed depending on whether the interventions are to be maintained strictly or lifted in entirety.
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SciScore for 10.1101/2020.05.05.20092361: (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 Thus, we have differential equations for the dynamics of the reduced SIRD model with virus pool and cooperative infection (i.e. the cooperative infection model): Simulation and parameter space sampling of the models: Models were implemented with MATLAB scripts and solved using the function ode23s() for solving stiff ordinary differential equations. MATLABsuggested: (MATLAB, RRID:SCR_001622)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We also note that, as with any …
SciScore for 10.1101/2020.05.05.20092361: (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 Thus, we have differential equations for the dynamics of the reduced SIRD model with virus pool and cooperative infection (i.e. the cooperative infection model): Simulation and parameter space sampling of the models: Models were implemented with MATLAB scripts and solved using the function ode23s() for solving stiff ordinary differential equations. MATLABsuggested: (MATLAB, RRID:SCR_001622)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We also note that, as with any mathematical model for a complicated process, this model is not without limitations. Like all models in computational epidemiology, this model does not account for many factors that contributes to the transmission of COVID-19. First, this model assumes that there is no regional heterogeneity in the country or state of interest, and that the population is homogenously mixed, while it has been demonstrated that the susceptibility to and fatality of COVID-19 depends on many demographic factors, such as gender and age11,31, and life style related variables, such as smoking history32. Countries and state are assumed to be isolated from each other, which means no international and interstate travels occur during the time period, while imported cases likely contribute to many COVID-19 outbreaks around the world. Moreover, since a data-driven approach was used to parameterize the models, it might underestimate the severity of the pandemic because of the existence of undocumented infections due to the limitation of diagnostic capacity33. Other factors not included in the model, such as seasonality in transmission of coronaviruses14, existence of asymptotic infections34, and change of case definition35 may also affect the epidemic curve of COVID-19. Nevertheless, the model, while possibly underestimating the severity of COVID-19 due to simplification, still forecasts the rapid escalation in COVID-19 spread, and highlights the extreme urgency of stronger i...
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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