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. 2020 Sep:138:109945.
doi: 10.1016/j.chaos.2020.109945. Epub 2020 May 30.

Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming

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

Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming

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

Abstract

COVID-19 declared as a global pandemic by WHO, has emerged as the most aggressive disease, impacting more than 90% countries of the world. The virus started from a single human being in China, is now increasing globally at a rate of 3% to 5% daily and has become a never ending process. Some studies even predict that the virus will stay with us forever. India being the second most populous country of the world, is also not saved, and the virus is spreading as a community level transmitter. Therefore, it become really important to analyse the possible impact of COVID-19 in India and forecast how it will behave in the days to come. In present work, prediction models based on genetic programming (GP) have been developed for confirmed cases (CC) and death cases (DC) across three most affected states namely Maharashtra, Gujarat and Delhi as well as whole India. The proposed prediction models are presented using explicit formula, and impotence of prediction variables are studied. Here, statistical parameters and metrics have been used for evaluated and validate the evolved models. From the results, it has been found that the proposed GEP-based models use simple linkage functions and are highly reliable for time series prediction of COVID-19 cases in India.

Keywords: COVID-19; Coronavirus; Genetic programming; India; 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 India using GEP model.
Fig. 4
Fig. 4
Expression trees (ETs) for the modelling of COVID-19 in India.
Algorithm 1
Algorithm 1
Time Series prediction model generated for CC across India.
Algorithm 2
Algorithm 2
Time Series prediction model generated for DC across India.
Fig. 5
Fig. 5
Contribution of predictor variables for COVID-19 in India.
Fig. 6
Fig. 6
Experimental versus predicted cases for COVID-19 in Maharashtra using GEP model.
Fig. 7
Fig. 7
Expression trees (ETs) for COVID-19 in Maharashtra.
Algorithm 3
Algorithm 3
Time Series prediction model generated for CC in Maharashtra.
Algorithm 4
Algorithm 4
Time Series prediction model generated for DC in Maharashtra.
Fig. 8
Fig. 8
Contribution of predictor variables for COVID-19 in Maharashtra.
Fig. 9
Fig. 9
Experimental versus predicted cases for COVID-19 in Gujarat using GEP model.
Fig. 10
Fig. 10
Expression trees (ETs) for COVID-19 in Gujarat.
Fig. 11
Fig. 11
Contribution of predictor variables for COVID-19 in Gujarat.
Algorithm 5
Algorithm 5
Time Series prediction model generated for CC in Gujarat.
Algorithm 6
Algorithm 6
Time Series prediction model generated for DC inn Gujarat.
Fig. 12
Fig. 12
Experimental versus predicted cases for COVID-19 in Delhi using GEP model.
Fig. 13
Fig. 13
Expression trees (ETs) for COVID-19 in Delhi.
Algorithm 7
Algorithm 7
Time Series prediction model generated for CC in Delhi.
Algorithm 8
Algorithm 8
Time Series prediction model generated for DC in Delhi.
Fig. 14
Fig. 14
Contribution of predictor variables for COVID-19 in Delhi.
See this image and copyright information in PMC

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