Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt

Abstract

COVID-19 is spreading within the sort of an enormous epidemic for the globe. This epidemic infects a lot of individuals in Egypt. The World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory spray. On these days, Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this epidemic. In this paper displayed, the real database of COVID-19 for Egypt has been analysed from February 15, 2020, to June 15, 2020, and predicted with the number of patients that will be infected with COVID-19, and estimated the epidemic final size. Several regression analysis models have been applied for data analysis of COVID-19 of Egypt. In this study, we've been applied seven regression analysis-based models that are exponential polynomial, quadratic, third-degree, fourth-degree, fifth-degree, sixth-degree, and logit growth respectively for the COVID-19 dataset. Thus, the exponential, fourth-degree, fifth-degree, and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one month. In addition, we have applied the well-known logit growth regression model and we obtained the following epidemiological insights: Firstly, the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September 2020. Secondly, the final total size for cases 1.6676E+05 cases. The action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.

Keywords: COVID-19; Epidemic model; Regression analysis model.

Fig. 1

Real data for Cumulative total new confirmed cases and died cases.

Fig. 2

(a) Fitted curves with training data based on regression (b) Comparison of the real case and the predicted models: exponential, quadratic, third degree, fourth-degree, results of the proposed models: exponential, quadratic, third- Fifth-degree and sixth-degree polynomial degree, fourth-degree, fifth-degree and, sixth-degree. Polynomial on the testing dataset of Egypt COVID-19.

Fig. 2

(a) Fitted curves with training data based on regression (b) Comparison of the real case and the predicted models: exponential, quadratic, third degree, fourth-degree, results of the proposed models: exponential, quadratic, third- Fifth-degree and sixth-degree polynomial degree, fourth-degree, fifth-degree and, sixth-degree. Polynomial on the testing dataset of Egypt COVID-19.

Fig. 3

Represent residual for proposed models: (a) exponential, (b).quadratic, (c) third-degree, (d) fourth-degree, (e) fifth-degree and (f) sixth-degree polynomial for dataset Egypt COVID-19

Fig. 4

The predicted final size of epidemic in Egypt after one month.

Fig. 5

(a).Fitted curves with training data based on logit Fig. 5. (b) Comparison of the real case and the predicted growth regression mode. Results of the logit model applied on the testing dataset of Egypt COVID19.

Fig. 6

The predicted final of coronavirus in Egypt for epidemic (data until 15 Jun 2020).

Fig. 7

Illustrated the phases of coronavirus epidemic in Egypt the first phase at 3-Mar, second phase at 15-May and third phase (peak) at 22-Jun and final epidemic at 8-Sep.