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Updated: Nov 8, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks.

Khondoker Nazmoon Nabi¹, Md Toki Tahmid², Abdur Rafi²

¹Department of Mathematics, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.

Results in Physics

|April 26, 2021

Summary

Convolutional Neural Networks (CNNs) effectively forecast COVID-19 outbreaks, outperforming Long Short-Term Memory (LSTM) models. CNNs offer robust long-term predictions, crucial for pandemic preparedness.

Keywords:

Convolutional neural network (CNN)Deep learning Long short term memory (LSTM)Time series analysis

Related Experiment Videos

Last Updated: Nov 8, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Area of Science:

Epidemiology
Data Science
Computational Biology

Background:

Global COVID-19 surges persist despite mass vaccination programs.
New variants and public apathy challenge pandemic control efforts.
Accurate forecasting is vital for strategic preparedness and response planning.

Purpose of the Study:

To forecast probable future COVID-19 outbreak scenarios in Brazil, Russia, and the United Kingdom.
To compare the efficacy of deep learning models for time-series forecasting in epidemiology.

Main Methods:

Utilized four deep learning models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Multivariate Convolutional Neural Network (MCNN).
Applied models to analyze time-series data for COVID-19 outbreak prediction.
Evaluated models based on validation accuracy and forecasting consistency.

Main Results:

Convolutional Neural Network (CNN) demonstrated superior performance over other models.
CNN achieved higher validation accuracy and forecasting consistency.
LSTM models showed poor prediction accuracy due to the absence of seasonality in the data.

Conclusions:

Convolutional Neural Networks (CNNs) are highly effective for long-term COVID-19 forecasting, especially with limited features and historical data.
CNNs excel due to their ability to learn essential features, distortion invariance, and temporal dependencies.
CNNs show promise as a superior alternative to Recurrent Neural Networks for epidemiological time-series forecasting.