Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

9An Epidemic Graph’s Modeling Application to the COVID-19 Outbreak

Hemanta Kumar Bhuyan¹* and Subhendu Kumar Pani²†

¹Department of Information Technology, Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, India

²Krupajal Computer Academy, Odisha, India

Abstract

The furious disease named COVID-19 is an outbreak in the current scenario. To control the spreading of this disease, new models were developed which utilized established methodologies to analyze how different containment strategies will influence the spread of the virus. It presents a novel machine learning approach that can estimate any epidemiological model’s parameters based on two types of information: either static or dynamic. It primarily utilizes the Graph model using deep learning approaches and Long-term memories (LSTMs) to obtain mobility data’s spatial and temporal properties of SIR and SIRD models. It runs and simulates using data on the Italian COVID dynamics and compares the model predictions to previously observed epidemics.

Keywords: COVID-19, epidemic diffusion modeling, data analytics, data model, spatiotemporal data mining, deep learning, graph machine learning

9.1 Introduction

The first documented typical case of pneumonia from which the SARS-CoV-2 emerged in December 2019 occurred in Wuhan, China [1−3]. China Severe Acute Respiratory Syndrome (SARS) was caused by a coronavirus, a virus that belongs to the same virus family as that which erupted in South China ...