COVID-19 Epidemic Forecast using Machine Learning Regression Techniques

The COVID-19 Coronavirus (SARS-CoV-2), has caused destruction all around the world, since December 2019. It is still managing to grow at an unprecedented scale. It was declared as a health emergency for the entire globe by the World Health Organization (WHO) in January 2022. The virus continues to impact the lives of millions of people. An early detection system warning about the repercussions of the virus at a county level can be favorable for the residents as well and aid the government to enforce appropriate safety measures. This research aims at modeling such a warning system which predicts the positivity rate of COVID-19 for a geographical location. The proposed solution uses supervised machine learning techniques such as Random Forest, Linear Regression, Naive Bayes, and Gradient Boosting Regression. The prediction is made based on the analysis of the past data in each time frame with temporal input such as the population of the area, number of tests conducted, number of positive tests, reported cases in that area among others. The Gradient Boosting algorithm outperforms all the other algorithms used in this research. Machine learning based recommendation system for COVID-19 spread can help the public and government to take necessary precautions for suppressing its effect. The proposed modeling approach provides a reliable tool to predict COVID-19 transmission with an accuracy of 99.4%. © 2022 IEEE.

Supervised Machine Learning Models for Covid-19 Diagnosis using a Combination of Clinical and Laboratory Data

An epidemic caused by a new type of Coronavirus family, called COVID-19, has created a global crisis involving all countries of the world. In this regard, designing an early detection system using heuristic and noninvasive methods can be a good and decisive factor in detecting the disease early and consequently decreasing the prevalence of the virus. In recent years, to rapidly diagnose diseases, machine learning techniques have increasingly grown to predict and diagnose patients, and researchers have used them in various studies. In this regard, since the outbreak of COVID-19, several researchers have tried to use the machine learning approach as a potential tool for identifying and diagnosing this disease. Due to the importance and role of using clinical and laboratory data in the diagnosis of afflicted people with COVID-19, in this paper, the models of K-NN, SVM, Decision Tree, Random Forest, Naive Bayes, Neural Network, and XGBoost as the most common machine learning models were used on a database with 1354 records consisting of clinical and laboratory data of COVID and non-COVID patients to diagnose COVID-19. Evaluation results based on Accuracy, Precision, Recall, and F-Score criteria showed that a XGBoost and K-NN with accuracy of 97% and 96% could be considered a suitable predictive model to diagnose the COVID-19 disease. © 2022 IEEE.