Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning model for prediction and prevention of COVID-19 outbreak

Deep Learning model for prediction and prevention of COVID-19 outbreak

Romaric, Cheuteu Tazopap Joseph

Paperback

General Mathematics

ISBN10: 365988894X
ISBN13: 9783659888946
Publisher: LAP Lambert Academic Publishing
Published: Mar 17 2025
Pages: 112
Weight: 0.35
Height: 0.27 Width: 6.00 Depth: 9.00
Language: English
This study focuses on mathematical modeling of COVID-19 transmission in Cameroon using deep learning techniques. Initially, we established a mathematical model that characterizes the transmission dynamics of COVID-19 within the Cameroonian population, incorporating both time-dependent and constant parameters. A theoretical analysis of the model is presented, proving the existence, uniqueness, positivity, and boundedness of the solutions, as well as identifying equilibrium points. Additionally, we implement two deep learning models, an LSTM (Long Short-Term Memory) and a GRU (Gated Recurrent Unit) to identify the time-dependent parameters based on available daily data. Through training, testing, and predictions, our models achieve high accuracy, with coefficients of determination ranging from 0.96 to 0.98. We further aim to extend this study by exploring various artificial intelligence models, including machine learning approaches, to refine our COVID-19 transmission model for Cameroon and adapt it for application in other countries.

Also in

General Mathematics

View All