Time series analysis and forecast of the COVID-19 pandemic in india using genetic programming
Autor
Salgotra, Rohit
Gandomi, Mostafa
Gandomi, Amir H
Institución
Resumen
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.