tetano
Editor, Senior Moderator
Transbound Emerg Dis
. 2020 Aug 4.
doi: 10.1111/tbed.13764. Online ahead of print.
Exploring the Growth of COVID-19 Cases using Exponential Modelling Across 42 Countries and Predicting Signs of Early Containment using Machine Learning
Dharun Kasilingam[SUP] 1 [/SUP], S P Sathiya Prabhakaran[SUP] 2 [/SUP], R Dinesh Kumar[SUP] 3 [/SUP], Varthini Rajagopal[SUP] 4 [/SUP], T Santhosh Kumar[SUP] 5 [/SUP], Ajitha Soundararaj[SUP] 6 [/SUP]
Affiliations
Abstract
COVID-19 pandemic disease spread by the SARS-COV-2 single-strand structure RNA virus, belongs to the 7[SUP]th[/SUP] generation of the coronavirus family. Following an unusual replication mechanism, it's extreme ease of transmissivity has put many counties under lockdown. With 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 the COVID-19 virus and identifies countries that have shown early signs of containment until 26[SUP]th[/SUP] March 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% to 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed.
Keywords: COVID-19; Corona Virus; Exponential Growth Model; Machine Learning; Pandemic; SARS-CoV-2.
. 2020 Aug 4.
doi: 10.1111/tbed.13764. Online ahead of print.
Exploring the Growth of COVID-19 Cases using Exponential Modelling Across 42 Countries and Predicting Signs of Early Containment using Machine Learning
Dharun Kasilingam[SUP] 1 [/SUP], S P Sathiya Prabhakaran[SUP] 2 [/SUP], R Dinesh Kumar[SUP] 3 [/SUP], Varthini Rajagopal[SUP] 4 [/SUP], T Santhosh Kumar[SUP] 5 [/SUP], Ajitha Soundararaj[SUP] 6 [/SUP]
Affiliations
- PMID: 32749759
- DOI: 10.1111/tbed.13764
Abstract
COVID-19 pandemic disease spread by the SARS-COV-2 single-strand structure RNA virus, belongs to the 7[SUP]th[/SUP] generation of the coronavirus family. Following an unusual replication mechanism, it's extreme ease of transmissivity has put many counties under lockdown. With 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 the COVID-19 virus and identifies countries that have shown early signs of containment until 26[SUP]th[/SUP] March 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% to 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed.
Keywords: COVID-19; Corona Virus; Exponential Growth Model; Machine Learning; Pandemic; SARS-CoV-2.