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A Pandemic Predictive Model with Convolutional Neural Networks and Deep Reinforcement Learning using Simulated Partial Differential Equations Data.

International Journal of Emerging Trends in Science and Technology, , 4 January 2024
https://doi.org/10.18535/ijetst/v2024.03

Abstract

Detecting the spread of pandemics will greatly reduce human and economic loss. Existing Epidemiological models used for coronavirus disease 2019 (COVID-19) prediction models are too slow and fail to capture epidemic development thoroughly. This research presents a Physics-based Machine Learning Architecture (PMLA) to improve the processing speed and accuracy of epidemic forecasting governed by susceptible–exposed–infected–recovered–deceased (SEIRD) model equations. The dynamics of the epidemic were extracted using Convolutional Neural Networks (CNN) and Deep Reinforcement Learning (Deep RL) from data simulated with Partial Differential Equations (PDEs). The PMLA accuracy is measured using mean squared error. The PMLA prediction model enhances the ability of health authorities to predict the spread of COVID-19 in real time efficiently and effectively.


 


Keywords: COVID-19, Convolutional Neural Networks, Deep Reinforcement Learning, Partial Differential Equations, Machine learning, Finite Element Method.

How to Cite

Betgeri, S. N., & Vadyala, S. R. (2024). A Pandemic Predictive Model with Convolutional Neural Networks and Deep Reinforcement Learning using Simulated Partial Differential Equations Data . International Journal of Emerging Trends in Science and Technology. https://doi.org/10.18535/ijetst/v2024.03

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