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Updated: Jan 16, 2026

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

Published on: April 28, 2019

Dengue fever prediction based on meteorological features and deep learning models.

Yunyun Cheng^1,2, Rong Cheng³, Ting Xu³

¹Shanxi University of Electronic Science and Technology, Linfen, 041000, China.

Infectious Disease Modelling

|January 15, 2026

Summary

This study introduces a novel hybrid model for dengue fever prediction, enhancing accuracy by integrating meteorological data. The model effectively forecasts epidemic trends, crucial for public health surveillance.

Keywords:

Deep learning model Dengue early warning Hybrid models Signal decomposition Time window

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A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

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Published on: April 28, 2019

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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Published on: October 27, 2023

Area of Science:

Epidemiology
Environmental Science
Data Science

Background:

Dengue fever epidemics pose a significant global health challenge, necessitating accurate prediction of epidemiological trends.
Meteorological factors like temperature, humidity, and precipitation are known to influence dengue fever's occurrence and prevalence.
Existing prediction models may not fully capture the complex interplay of multidimensional meteorological features.

Purpose of the Study:

To propose an effective hybrid model for improving dengue fever prediction performance.
To incorporate multidimensional meteorological features into dengue trend forecasting.
To address data scarcity issues in epidemiological datasets.

Main Methods:

Data augmentation using Time-series Generative Adversarial Networks (TimeGAN).
Meteorological data decomposition via Symplectic Geometry Mode Decomposition (SGMD) and reconstruction using Sample Entropy (SE).
Feature extraction and fusion using bidirectional temporal convolutional networks (BiTCN) and attention-based bidirectional long and short-term memory networks (BiLSTM).

Main Results:

The proposed hybrid model demonstrated accurate prediction of dengue fever epidemic trends in Guangdong Province, China.
Achieved a Mean Absolute Error (MAE) of 192.98759 and a Mean Absolute Percentage Error (MAPE) of 2.492.
The integration of TimeGAN, SGMD, SE, BiTCN, and BiLSTM significantly improved prediction accuracy.

Conclusions:

The developed hybrid model offers a robust approach for predicting dengue fever epidemics.
Accurate forecasting of dengue trends is vital for effective public health interventions and resource allocation.
This methodology highlights the potential of advanced machine learning techniques in analyzing complex environmental and epidemiological data.