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High-throughput Detection Method for Influenza Virus

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Published on: February 4, 2012

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and

Liuyang Yang^1,2, Gang Li³, Jin Yang²

¹Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, China.

Journal of Medical Internet Research

|February 13, 2023

Summary

A new deep learning model effectively predicts influenza trends in megacities using diverse data, improving early warning systems for respiratory diseases.

Keywords:

ILI MAL model deep learning influenza megacity multisource heterogeneous data

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Published on: February 4, 2012

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

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Area of Science:

Epidemiology
Artificial Intelligence
Public Health

Background:

Megacities require enhanced forecasting for acute respiratory infectious diseases.
Current influenza prediction models have limitations, necessitating improved methods.

Purpose of the Study:

To develop a novel, high-performing deep learning model for predicting influenza trends in megacities.
To utilize multisource heterogeneous data for improved influenza forecasting.

Main Methods:

Collected diverse data (2012-2019): influenza-like illness (ILI) cases, virological surveillance, climate, demographics, and search engine data.
Developed a Multi-Attention Long Short-Term Memory (MAL-LSTM) deep learning model.
Evaluated model performance using R², explained variance, MAE, and MSE, comparing against other ML/DL models.

Main Results:

The MAL model combined with climate, demographic, and search engine data demonstrated superior prediction accuracy for ILI% and ILI%×positive%.
The best model achieved an explained variance of 0.78 and R² of 0.76 for ILI% prediction.
The MAL model outperformed Random Forest, XGBoost, LSTM, and GRU models in influenza trend prediction.

Conclusions:

The novel MAL model significantly improves upon existing influenza forecasting methods in megacities.
Natural factors and search engine data are crucial for accurate ILI pattern prediction.
Enhanced prediction capabilities enable better preparedness and reduced public health impact from respiratory diseases.