Exploring the Growth of COVID-19 Cases using Exponential Modelling
Across 42 Countries and Predicting Signs of Initial Containment using
COVID-19 pandemic disease spread by SARS-COV-2 single-strand structure
RNA virus belongs to the 7th generation of the coronavirus family.
Following an unusual replication mechanism, its extreme ease of
transmissibility has put many counties under lockdown. With a cure for
the infection uncertain in the near future, the pressure currently lies
in the current healthcare infrastructure, policies, government
activities, and behaviour of the people to contain the virus. This
research seeks to understand the spreading patterns of the COVID-19
virus through exponential growth modelling and identifies countries that
have showed an initial sign of containment until 26th March 2020. Post
identification of countries that have shown an initial sign of
containment, predictive supervised machine learning models were built
with infrastructure, environment, policies, and infection related
independent variables. For the purpose, COVID-19 infection data across
42 countries were used. Logistic regression results shows a positive
significant relationship of healthcare infrastructure and lockdown
policies on the sign of early containment in countries. Machine learning
models based on logistic regression, decision tree, random forest, and
support vector machines were developed and are seen to have accuracies
between 76.2% to 92.9% to predict early sign of infection containment.
Other policies and activities taken by countries to contain the
infection are also discussed.