Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

130
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
130

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clarifying the relationship between biomedical and health informatics and digital health: expert perspectives.

BMJ health & care informatics·2026
Same author

An evidence gap map of digital health interventions for enhancing patient engagement in healthcare.

NPJ digital medicine·2026
Same author

AI-Integrated EEG Decision Support for Neurocritical Care: A Conceptual and Feasibility Framework.

Studies in health technology and informatics·2026
Same author

Automatic Speech Recognition and Large Language Models for Multilingual Pathology Report Generation: Proof-of-Concept Study.

JMIR formative research·2026
Same author

Publisher Correction: Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

Obesity surgery·2026
Same author

Mediation of Pre-Pregnancy Body Mass Index and Dietary Patterns with Relation to Vitamin D and Erythropoiesis-Related Micronutrients in Pregnant Women.

International journal of medical sciences·2026

Related Experiment Video

Updated: Jul 5, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.5K

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dina Nur Anggraini Ningrum1, Yu-Chuan Jack Li2,3,4, Chien-Yeh Hsu5,6

  • 1Public Health Department, Universitas Negeri Semarang, Semarang City, Indonesia.

Studies in Health Technology and Informatics
|January 25, 2024
PubMed
Summary

Artificial intelligence (AI) models with spatiotemporal data accurately predict Dengue outbreaks and cases one week in advance. This AI approach enhances early warning systems for effective community-based vector control.

Keywords:
artificial intelligencedengue incidence cases predictiondengue outbreak predictionearly warning system

More Related Videos

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
06:00

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Published on: January 26, 2024

1.3K
A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
04:23

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

Published on: April 28, 2019

6.6K

Related Experiment Videos

Last Updated: Jul 5, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.5K
Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
06:00

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Published on: January 26, 2024

1.3K
A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
04:23

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

Published on: April 28, 2019

6.6K

Area of Science:

  • Public Health
  • Infectious Disease Epidemiology
  • Artificial Intelligence in Medicine

Background:

  • Dengue fever is a significant global health burden, causing millions of infections and thousands of deaths annually, primarily in children.
  • Effective vector control remains crucial for dengue prevention, as vaccines have limitations.
  • Early detection and medical access can drastically reduce dengue mortality rates.

Purpose of the Study:

  • To develop an artificial intelligence (AI) model with a spatiotemporal approach for predicting Dengue outbreaks and incidence cases.
  • To create a predictive model ready for implementation in an early warning system application.
  • To improve Dengue surveillance and inform community-based vector control strategies in endemic areas like Semarang City, Indonesia.

Main Methods:

  • Utilized spatiotemporal, meteorological, climatological, and dengue surveillance data from Semarang City, Indonesia (January 2014 - December 2021).
  • Employed machine learning and Long Short-Term Memory (LSTM) networks for predictive modeling, dividing data into 80% training and 20% testing sets.
  • Evaluated outbreak prediction using accuracy, AUROC, precision, recall, and F1 score; incidence prediction was assessed using MSE, MAE, RMSE, and R-squared.

Main Results:

  • The Extra Trees Classifier model achieved high performance in Dengue outbreak prediction (Accuracy: 0.8925, AUROC: 0.9529, F1 Score: 0.7238).
  • The CatBoost Regressor model demonstrated superior performance in Dengue incidence case prediction (R-squared: 0.5621, RMSE: 1.0891).
  • AI models incorporating spatiotemporal data significantly enhance prediction accuracy for both Dengue outbreaks and incidence cases.

Conclusions:

  • Artificial intelligence, particularly with a spatiotemporal approach, offers a powerful tool for predicting Dengue outbreaks and incidence cases.
  • The developed AI models are suitable for integration into early warning systems, enabling timely interventions.
  • Implementing AI-driven early warning systems can enhance policy-maker and community engagement in targeted, community-based vector control efforts.