An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms

Abstract

<p>The whole world is experiencing a novel infection called Coronavirus brought about by a Covid since 2019. The main concern about this disease is the absence of proficient authentic medicine The World Health Organization (WHO) proposed a few precautionary measures to manage the spread of illness and to lessen the defilement in this manner decreasing cases. In this paper, we analyzed the Coronavirus dataset accessible in Kaggle. The past contributions from a few researchers of comparative work covered a limited number of days. Our paper used the covid19 data till May 2021. The number of confirmed cases, recovered cases, and death cases are considered for analysis. The corona cases are analyzed in a daily, weekly manner to get insight into the dataset. After extensive analysis, we proposed machine learning regressors for covid 19 predictions. We applied linear regression, polynomial regression, Decision Tree Regressor, Random Forest Regressor. Decision Tree and Random Forest given an r-square value of 0.99. We also predicted future cases with these four algorithms. We can able to predict future cases better with the polynomial regression technique. This prediction can help to take preventive measures to control covid19 in near future. All the experiments are conducted with python language</p>

Internet of Things and Blockchain-Based Framework for Coronavirus (COVID-19) Disease

“…Internetconnected devices may communicate with one another via a variety of transmission methods. Sending data to the mobile devices of patients and healthcare professionals is how this data is acquired [23], [24], [25]. Patients and medical professionals must work together to devise a safe way to exchange patient data.…”

Life Expectancy Prediction through Analysis of Immunization and HDI Factors using Machine Learning Regression Algorithms

“…Heart rate and breathing rate are negatively proportional to life expectancy. In [9], the authors applied various machine learning algorithms for analyzing covid death cases worldwide. Nataliya Boyko [10] et al investigated ML classifiers and regressors for life expectancy prediction.…”

Simulation Optimization for Location and Allocation of Emergency Medical Service

“…Then this study also tries to involve consolidation between several small bases so as to form a wider cover area such as the future work suggested by [9]. Especially during the covid-19 pandemic and the new normal era makes this problem very interesting to discuss [12]. For this reason, this study tries to overcome the problem of location and EMS allocation by using a combination of metaheuristics and simulation technique because calculations with the optimal approach are very complex.…”