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

arXiv: https://arxiv.org/abs/2004.01574 · HTML: https://mlsanity.com/paper/2004.01574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Containing a spread through sequential learning: to exploit or to explore?](https://mlsanity.com/paper/2303.00141/) (2023)
- [Epidemic Forecasting with a Hybrid Deep Learning Method Using CNN-LSTM With WOA-GWO Parameter Optimization: Global COVID-19 Case Study](https://mlsanity.com/paper/2503.12813/) (2025)
- [Neural Network Augmented Compartmental Pandemic Models](https://mlsanity.com/paper/2212.08481/) (2022)

## Abstract (arXiv)

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 citedatahub, 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.