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. 2020 Nov:140:110118.
doi: 10.1016/j.chaos.2020.110118. Epub 2020 Jul 17.

Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries

Rohit Salgotra  1 , Mostafa Gandomi  2 , Amir H Gandomi  3
Affiliations

Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries

Rohit Salgotra et al. Chaos Solitons Fractals. 2020 Nov.

Abstract

COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries.

Keywords: COVID-19; Coronavirus; Countries of the world; Gene expression programming (GEP); SARS-CoV-2; Time series forecasting.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Representation of an ET.
Fig. 2
Fig. 2
Representation of a GEP algorithm.
Fig. 3
Fig. 3
Experimental versus predicted cases for COVID-19 in USA using GEP model.
Fig. 4
Fig. 4
Prediction of new confirmed cases of COVID-19 per day in USA.
Fig. 5
Fig. 5
Expression trees (ETs) for the modelling of COVID-19 in USA.
Algorithm 1
Algorithm 1
Model for CC in USA.
Algorithm 2
Algorithm 2
Model for DC in USA.
Fig. 6
Fig. 6
Contribution of predictor variables for COVID-19 in USA.
Fig. 7
Fig. 7
Experimental versus predicted cases for COVID-19 in Canada using GEP model.
Fig. 8
Fig. 8
Prediction of new confirmed cases of COVID-19 per day in Canada.
Fig. 9
Fig. 9
Expression trees (ETs) for the modelling of COVID-19 in Canada.
Algorithm 3
Algorithm 3
Model for CC in Canada.
Algorithm 4
Algorithm 4
Model for DC in Canada.
Fig. 10
Fig. 10
Contribution of predictor variables for COVID-19 in Canada.
Fig. 11
Fig. 11
Experimental versus predicted cases for COVID-19 in Germany using GEP model.
Fig. 12
Fig. 12
Prediction of new confirmed cases of COVID-19 per day in Germany.
Fig. 13
Fig. 13
Expression trees (ETs) for the modelling of COVID-19 in Germany.
Algorithm 5
Algorithm 5
Model for CC in Germany.
Algorithm 6
Algorithm 6
Model for DC in Germany.
Fig. 14
Fig. 14
Contribution of predictor variables for COVID-19 in Germany.
Fig. 15
Fig. 15
Experimental versus predicted cases for COVID-19 in Brazil using GEP model.
Fig. 16
Fig. 16
Prediction of new confirmed cases of COVID-19 per day in Brazil.
Fig. 17
Fig. 17
Expression trees (ETs) for the modelling of COVID-19 in Brazil.
Algorithm 7
Algorithm 7
Model for CC in Brazil.
Algorithm 8
Algorithm 8
Model for DC in Brazil.
Fig. 18
Fig. 18
Contribution of predictor variables for COVID-19 in Brazil.
Fig. 19
Fig. 19
Experimental versus predicted cases for COVID-19 in Mexico using GEP model.
Fig. 20
Fig. 20
Prediction of new confirmed cases of COVID-19 per day in Mexico.
Fig. 21
Fig. 21
Expression trees (ETs) for the modelling of COVID-19 in Mexico.
Algorithm 9
Algorithm 9
Model for CC in Mexico.
Algorithm 10
Algorithm 10
Model for DC in Mexico.
Fig. 22
Fig. 22
Contribution of predictor variables for COVID-19 in Mexico.
Fig. 23
Fig. 23
Experimental versus predicted cases for COVID-19 in UK using GEP model.
Algorithm 11
Algorithm 11
Model for CC in UK.
Algorithm 12
Algorithm 12
Model for DC in UK.
Fig. 24
Fig. 24
Prediction of new confirmed cases of COVID-19 per day in United Kingdom.
Fig. 25
Fig. 25
Expression trees (ETs) for the modelling of COVID-19 in UK.
Fig. 26
Fig. 26
Contribution of predictor variables for COVID-19 in UK.
Fig. 27
Fig. 27
Experimental versus predicted cases for COVID-19 in Russia using GEP model.
Fig. 28
Fig. 28
Prediction of new confirmed cases of COVID-19 per day in Russia.
Fig. 29
Fig. 29
Expression trees (ETs) for the modelling of COVID-19 in Russia.
Algorithm 13
Algorithm 13
Model for CC in Russia.
Algorithm 14
Algorithm 14
Model for DC in Russia.
Fig. 30
Fig. 30
Contribution of predictor variables for COVID-19 in Russia.
Fig. 31
Fig. 31
Experimental versus predicted cases for COVID-19 in Spain using GEP model.
Fig. 32
Fig. 32
Prediction of new confirmed cases of COVID-19 per day in Spain.
Fig. 33
Fig. 33
Expression trees (ETs) for the modelling of COVID-19 in Spain.
Algorithm 15
Algorithm 15
Model for CC in Spain.
Algorithm 16
Algorithm 16
Model for DC in Spain.
Fig. 34
Fig. 34
Contribution of predictor variables for COVID-19 in Spain.
Fig. 35
Fig. 35
Experimental versus predicted cases for COVID-19 in Italy using GEP model.
Fig. 36
Fig. 36
Prediction of new confirmed cases of COVID-19 per day in Italy.
Fig. 37
Fig. 37
Expression trees (ETs) for the modelling of COVID-19 in Italy.
Algorithm 17
Algorithm 17
PModel for CC in Italy.
Algorithm 18
Algorithm 18
Model for DC in Italy.
Fig. 38
Fig. 38
Contribution of predictor variables for COVID-19 in Italy.
Fig. 39
Fig. 39
Experimental versus predicted cases for COVID-19 in France using GEP model.
Fig. 40
Fig. 40
Prediction of new confirmed cases of COVID-19 per day in France.
Fig. 41
Fig. 41
Expression trees (ETs) for the modelling of COVID-19 in France.
Algorithm 19
Algorithm 19
Model for CC in France.
Algorithm 20
Algorithm 20
Model for DC in France.
Fig. 42
Fig. 42
Contribution of predictor variables for COVID-19 in France.
Fig. 43
Fig. 43
Experimental versus predicted cases for COVID-19 in Turkey using GEP model.
Fig. 44
Fig. 44
Prediction of new confirmed cases of COVID-19 per day in Russia.
Fig. 45
Fig. 45
Expression trees (ETs) for the modelling of COVID-19 in Turkey.
Algorithm 21
Algorithm 21
Model for CC in Turkey.
Algorithm 22
Algorithm 22
Model for DC in Turkey.
Fig. 46
Fig. 46
Contribution of predictor variables for COVID-19 in Turkey.
Fig. 47
Fig. 47
Experimental versus predicted cases for COVID-19 in Iran using GEP model.
Fig. 48
Fig. 48
Prediction of new confirmed cases of COVID-19 per day in Iran.
Fig. 49
Fig. 49
Expression trees (ETs) for the modelling of COVID-19 in Iran.
Algorithm 23
Algorithm 23
Model for CC in Iran.
Algorithm 24
Algorithm 24
Model for DC in Iran.
Fig. 50
Fig. 50
Contribution of predictor variables for COVID-19 in Iran.
Fig. 51
Fig. 51
Experimental versus predicted cases for COVID-19 in China using GEP model.
Fig. 52
Fig. 52
Expression trees (ETs) for the modelling of COVID-19 in China.
Fig. 53
Fig. 53
Contribution of predictor variables for COVID-19 in China.
Algorithm 25
Algorithm 25
Model for CC in China.
Algorithm 26
Algorithm 26
Model for DC in China.
Fig. 54
Fig. 54
Experimental versus predicted cases for COVID-19 in South Africa using GEP model.
Fig. 55
Fig. 55
Prediction of new confirmed cases of COVID-19 per day in South Africa.
Fig. 56
Fig. 56
Expression trees (ETs) for the modelling of COVID-19 in South Aftrica.
Algorithm 27
Algorithm 27
Model for CC in South Africa.
Algorithm 28
Algorithm 28
Model for DC in South Africa.
Fig. 57
Fig. 57
Contribution of predictor variables for COVID-19 in South Africa.
Fig. 58
Fig. 58
Experimental versus predicted cases for COVID-19 in Singapore using GEP model.
Fig. 59
Fig. 59
Expression trees (ETs) for the modelling of COVID-19 in Singapore.
Algorithm 29
Algorithm 29
Model for CC in Singapore.
Algorithm 30
Algorithm 30
Model for DC in Singapore.
Fig. 60
Fig. 60
Contribution of predictor variables for COVID-19 in Singapore.
Fig. 61
Fig. 61
Daily Rise in the number of confirmed and Death cases to the total population of the country.
Fig. 62
Fig. 62
Daily Rise in Death count to Daily Rise in Confirmed cases for each Country.
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