Simulations of the spread of COVID-19 and control policies in Tunisia
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Abstract
Background: On March 11, 2020, the WHO announced that the COVID-19 outbreak had become pandemic, indicating that it was au- tonomous on several continents. Tunisia’s targeted containment and screen- ing strategy aligns with the WHO’s initial guidelines. This method is now showing its limitations. Mass screening in some countries shows that asymptomatic patients play an important role in spreading the virus through the population.Objective: Our goals are first to assess Tunisia’s COVID-19 control policies, and then understand the effect of various detection, quarantine and confinement strategies and the rule of asymptomatic patients on the spread of the virus in the Tunisian population. Methods: We develop and analyze a mathematical and epidemiologi- cal models for COVID- 19 in Tunisia. The data come from the Tunisian Health Commission dataset. Results: We calibrate different parameters of the model based on the Tunisian data, we calculate the expression of the basic reproduction num- ber R0 as a function of the model parameters and, finally, we carry out simulations of interventions and compare different strategies for suppress- ing and controlling the epidemic. Conclusions: We show that Tunisia’s control policies are effective in screening infected and asymptomatic persons.
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SciScore for 10.1101/2020.05.02.20088492: (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 Therefore, Step 3 Parameters τ1 et τ2 was estimated using Metropolis-Hastings (MH) algorithm developed in the pymcmcst python package [5] pythonsuggested: (IPython, RRID:SCR_001658)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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were …
SciScore for 10.1101/2020.05.02.20088492: (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 Therefore, Step 3 Parameters τ1 et τ2 was estimated using Metropolis-Hastings (MH) algorithm developed in the pymcmcst python package [5] pythonsuggested: (IPython, RRID:SCR_001658)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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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