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Exploring the Growth of COVID-19 Cases using Exponential Modelling Across 42 Countries and Predicting Signs of Initial Containment using Machine learning
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  • Dharun Kasilingam,
  • Sathiya Prabhakaran,
  • Dinesh Kumar,
  • Varthini Rajagopal,
  • Santhosh Kumar T
Dharun Kasilingam
Mudra Institute of Communications, Ahmedabad
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Sathiya Prabhakaran
NIT-Tiruchirappalli
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Dinesh Kumar
National Institute of Technology Goa
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Varthini Rajagopal
NIT-Tiruchirappalli
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Santhosh Kumar T
JIPMER Department of Pediatrics
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Abstract

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.

Peer review status:ACCEPTED

30 Mar 2020Submitted to Transboundary and Emerging Diseases
02 Apr 2020Submission Checks Completed
02 Apr 2020Assigned to Editor
02 Apr 2020Review(s) Completed, Editorial Evaluation Pending
02 Apr 2020Editorial Decision: Revise Major
03 Apr 20201st Revision Received
04 Apr 2020Submission Checks Completed
04 Apr 2020Assigned to Editor
06 Apr 2020Reviewer(s) Assigned
26 Apr 2020Review(s) Completed, Editorial Evaluation Pending
08 May 2020Editorial Decision: Revise Major
05 Jul 20202nd Revision Received
06 Jul 2020Submission Checks Completed
06 Jul 2020Assigned to Editor
07 Jul 2020Reviewer(s) Assigned
25 Jul 2020Review(s) Completed, Editorial Evaluation Pending
26 Jul 2020Editorial Decision: Revise Minor
27 Jul 20203rd Revision Received
27 Jul 2020Submission Checks Completed
27 Jul 2020Assigned to Editor
29 Jul 2020Review(s) Completed, Editorial Evaluation Pending
29 Jul 2020Editorial Decision: Accept