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

Rohit Salgotra, Mostafa Gandomi and Amir H Gandomi

Chaos, Solitons & Fractals, 2020, vol. 138, issue C

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; SARS-CoV-2; Time series forecasting; Genetic programming; India (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920303441

DOI: 10.1016/j.chaos.2020.109945

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