Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting

TL;DR

This paper combines SIR modeling and machine learning techniques to analyze and forecast the COVID-19 pandemic, estimating key parameters and predicting inflection points and end times for various regions.

Contribution

It introduces a hybrid approach using SIR models and machine learning to analyze real-world COVID-19 data and make predictions for different countries.

Findings

Estimated key pandemic parameters from data.

Predicted inflection points and possible ending times.

Forecasted pandemic trends for multiple countries.

Abstract

This work is a trial in which we propose SIR model and machine learning tools to analyze the coronavirus pandemic in the real world. Based on the public data from \cite{datahub}, we estimate main key pandemic parameters and make predictions on the inflection point and possible ending time for the real world and specifically for Senegal. The coronavirus disease 2019, by World Health Organization, rapidly spread out in the whole China and then in the whole world. Under optimistic estimation, the pandemic in some countries will end soon, while for most part of countries in the world (US, Italy, etc.), the hit of anti-pandemic will be no later than the end of April.