Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model
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Abstract
The COVID-19 disease is rapidly spreading in whole globe, affecting millions of people and pushing governments to take drastic measures to contain the outbreaks. The understanding of the dynamics of the epidemic is of great interest for the governments and health authorities that are facing COVID-19 outbreaks. The scarce presence of epidemiologic data, due to the still ongoing outbreaks, makes prediction difficult and mainly based on heuristic (fitting) models. However, these models with non-physical based parameters, can only give limited insight in the evolution of the outbreaks. In this work a SIRD compartmental model was developed to describe and predict the evolution of the Chinese and Italian outbreaks. Exploiting the similarities of the measures taken by the governments to contain the virus and of the total population number of Hubei province and Italy, the model was tuned on the Chinese outbreak (almost extinguished) and by perturbation the Italian outbreak was describe and predicted.
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SciScore for 10.1101/2020.03.19.20039388: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
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 rtransp…SciScore for 10.1101/2020.03.19.20039388: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
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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