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Updated: Dec 1, 2025

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

Comparing machine learning with case-control models to identify confirmed dengue cases.

Tzong-Shiann Ho^1,2, Ting-Chia Weng^3,4, Jung-Der Wang^3,5,6

¹Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan, Republic of China.

Plos Neglected Tropical Diseases

|November 10, 2020

Summary

This summary is machine-generated.

Machine learning models accurately identify dengue cases using basic patient data, aiding early detection and control in resource-limited settings. These models offer high sensitivity, especially before peak outbreaks, facilitating timely public health interventions.

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Last Updated: Dec 1, 2025

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

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Published on: April 28, 2019

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Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

Area of Science:

Epidemiology
Machine Learning
Public Health

Background:

Global dengue incidence has risen, necessitating improved surveillance.
Effective surveillance aids early outbreak detection, trend monitoring, and control.

Purpose of the Study:

To apply machine learning (ML) for identifying laboratory-confirmed dengue cases.
To evaluate the performance of ML models using limited clinical variables.

Main Methods:

Utilized data from 4,894 emergency department patients with dengue-like illness.
Applied deep neural network (DNN), decision tree (DT), and logistic regression (LR) models.
Input variables included age, body temperature, white blood cell counts (WBCs), and platelets.

Main Results:

Dengue confirmed in 60.11% of patients.
Models achieved Area Under the Curve (AUC) for Receiver Operating Characteristic (ROC) curves ranging from 83.75% to 85.87%.
High pre-peak sensitivities (92.6%-93.1%) were observed, particularly for low WBCs, fever, low platelets, and elderly patients.

Conclusions:

ML models offer a viable, convenient screening tool for dengue in resource-poor settings.
These models can support real-time syndromic surveillance and early warning systems.
Integration with other surveillance data can enhance dengue prevention and control efforts.