Common trends in the epidemic of Covid-19 disease
Matière et systèmes complexes, CNRS, Université de Paris, 75013, Paris, France
Accepted: 11 June 2020
Published online: 22 June 2020
The discovery of SARS-CoV-2, the responsible virus for the Covid-19 epidemic, has sparked a global health concern with many countries affected. Developing models that can interpret the epidemic and give common trend parameters are useful for prediction purposes by other countries that are at an earlier phase of the epidemic; it is also useful for future planning against viral respiratory diseases. One model is developed to interpret the fast-growth phase of the epidemic and another model for an interpretation of the entire data set. Both models agree reasonably with the data. It is shown by the first model that during the fast phase, the number of new infected cases depends on the total number of cases by a power-law relation with a scaling exponent equal to 0.82. The second model gives a duplication time in the range 1–3 days early in the start of the epidemic, and another parameter (α = 0.1–0.5) that deviates the progress of the epidemic from an exponential growth. Our models may be used for data interpretation and for guiding predictions regarding this disease, e.g., the onset of the maximum in the number of new cases.
© Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2020