Abstract

Corona Virus disease 2019 (COVID-19) has caused a worldwide pandemic of cough, fever, headache, body aches, and respiratory ailments. COVID- 19 has now become a severe disease and one of the leading causes of death globally. Modeling and prediction of COVID-19 have become inevitable as it has affected people worldwide. With the availability of a large-scale universal COVID-19 dataset, machine learning (ML) techniques and algorithms occur to be the best choice for the analysis, modeling, and forecasting of this disease. In this research study, we used one deep learning algorithm called Artificial Neural Network (ANN) and several ML algorithms such as Support Vector Machine (SVM), polynomial regression, and Bayesian ridge regression (BRR) modeling for analysis, modeling, and spread prediction of COVID-19. COVID-19 dataset, maintained and updated by JOHNS HOPKINS UNIVERSITY was used for ML models training, testing, and modeling. The cost and error generated during ANN training process was reduced using technique called back propagation which dynamically adjust the synapses weights to perform better predictions. The ANN architecture included one input layer with 441 neurons, 4 hidden layers each have 90 neurons and one output layer. ANN along with other ML algorithms were trained to model the prediction of COVID-19 spread for the next 10 days. Experimental results showed that BRR technique overall performed better prediction of COVID-19 for the next 10 days. The modeling of infectious diseases can help relevant countries to take the necessary steps and make timely decisions.

Highlights

  • In December 2019, a new type of coronavirus was called COVID-19 was identified in Wuhan, China.The rapid spread of the disease affected thousands of people early in China and within few months reached almost every country worldwide [1]

  • With the availability of a large-scale universal COVID-19 dataset, machine learning (ML) techniques and algorithms occur to be the best choice for the analysis, modeling, and forecasting of this disease

  • Artificial Neural Network (ANN) along with other ML algorithms were trained to model the prediction of COVID-19 spread for the 10 days

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Summary

Introduction

In December 2019, a new type of coronavirus was called COVID-19 was identified in Wuhan, China. The rapid spread of the disease affected thousands of people early in China and within few months reached almost every country worldwide [1]. In early 2020, the World Health Organization (WHO) has declared this virus a “global pandemic” compelling researchers globally to work collectively to find the disease cause and subsequently stop it [4]. The new type of virus appears to be more harmful and has caused a great threat to human safety due to its potential harm and fastspreading power. This pandemic has become the prime topic of current research. Due to the COVID-19 high spread across the world, this study aims to understand, analyze, and model its spread trends

COVID-19 Analysis Using Python Libraries
Data Preprocessing
Analyzing COVID-19 Spread
Methodology
Architecture and Working of Neural Networks
COVID-19 Modeling and Global Predictions
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