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Exploring the growth of COVID‐19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning
Author(s) -
Kasilingam Dharun,
Sathiya Prabhakaran Sakthivel Puvaneswaran,
Rajendran Dinesh Kumar,
Rajagopal Varthini,
Santhosh Kumar Thangaraj,
Soundararaj Ajitha
Publication year - 2021
Publication title -
transboundary and emerging diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.392
H-Index - 63
eISSN - 1865-1682
pISSN - 1865-1674
DOI - 10.1111/tbed.13764
Subject(s) - logistic regression , pandemic , decision tree , containment (computer programming) , coronavirus , covid-19 , business , government (linguistics) , artificial intelligence , computer science , machine learning , medicine , disease , pathology , infectious disease (medical specialty) , linguistics , philosophy , programming language
The coronavirus disease 2019 (COVID‐19) pandemic spread by the single‐stranded RNA severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) belongs to the seventh generation of the coronavirus family. Following an unusual replication mechanism, its extreme ease of transmissivity has put many countries under lockdown. With the uncertainty of developing a cure/vaccine for the infection in the near future, the onus currently lies on healthcare infrastructure, policies, government activities, and behaviour of the people to contain the virus. This research uses exponential growth modelling studies to understand the spreading patterns of SARS‐CoV‐2 and identifies countries that showed early signs of containment until March 26, 2020. Predictive supervised machine learning models are built using infrastructure, environment, policies, and infection‐related independent variables to predict early containment. COVID‐19 infection data across 42 countries are used. Logistic regression results show a positive significant relationship between healthcare infrastructure and lockdown policies, and signs of early containment. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines are developed and show accuracies between 76.2% and 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed.