Can we predict the occurrence of COVID-19 cases? Considerations using a simple model of growth

Abstract

This study aimed to present a simple model to follow the evolution of the COVID-19 (CV-19) pandemic in different countries. The cumulative distribution function (CDF) and its first derivative were employed for this task. The simulations showed that it is almost impossible to predict based on the initial CV-19 cases (1st 2nd or 3rd weeks) how the pandemic will evolve. However, the results presented here revealed that this approach can be used as an alternative for the exponential growth model, traditionally employed as a prediction model, and serve as a valuable tool for investigating how protective measures are changing the evolution of the pandemic.

Keywords: Coronavirus; Cumulative distribution function; Pandemic; SARS-CoV-2.

Graphical abstract

Fig. 1

The number of confirmed (circles) and new daily cases (triangles) in China. The red square dot indicates an unusual occurrence when 14 thousand new daily cases were confirmed in China (21st day of the pandemic). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

The number of confirmed (circles) and new daily cases (triangles) in Italy. The dashed lines exhibit the model predictions calculated using Eq. 1 (black dashed line) and 2 (red dashed line). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Exponential model (EM) used for fitting the actual data up to 14 days from the first case. On the 21st day, the deviations from the actual data and the EM are larger than 100%.

Fig. 4

Eq. 1 (black dashed line) and 2 (red dashed line) employed for fitting some other European countries, Spain, Germany, and Austria. D_o is provided by Eq. 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Confirmed and predicted cases using Eqs. 1 and 2 (inset scatterplot) for Austria. A 1:1 line is presented in both figures.

Fig. 6

The effect of varying p (top) and D_o (bottom) values in Eq. 1 (solid line) and 2 (dashed line). D_o = 32 and p = 4.6 are the average values of these parameters for the European modeled cases.