AIMC Topic: Epidemiological Models
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A novel framework for inferring dynamic infectious disease transmission with graph attention: a COVID-19 case study in Korea.
BMC public health
May 22, 2025
INTRODUCTION: Epidemic modeling is crucial for understanding and predicting infectious disease spread. To capture the complexity of real-world transmission, dynamic interactions between individuals with spatial heterogeneity must be considered. This ...
Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study.
Journal of theoretical biology
Mar 20, 2025
Cholera, an acute diarrheal disease, is a serious concern in developing and underdeveloped areas. A qualitative understanding of cholera epidemics aims to foresee transmission patterns based on reported data and mechanistic models. The mechanistic mo...
Artificial intelligence for modelling infectious disease epidemics.
Nature
Feb 19, 2025
Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social sci...
Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.
PLoS computational biology
Jan 9, 2025
Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human...
Deep neural networks for endemic measles dynamics: Comparative analysis and integration with mechanistic models.
PLoS computational biology
Nov 21, 2024
Measles is an important infectious disease system both for its burden on public health and as an opportunity for studying nonlinear spatio-temporal disease dynamics. Traditional mechanistic models often struggle to fully capture the complex nonlinear...
A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis.
Journal of theoretical biology
Nov 13, 2024
Tuberculosis (TB), the second leading infectious killer globally, claimed the lives of 1.3 million individuals in 2022, after COVID-19, surpassing the toll of HIV and AIDS. With an estimated 10.6 million new TB cases worldwide in 2022, the gravity of...
Managing spatio-temporal heterogeneity of susceptibles by embedding it into an homogeneous model: A mechanistic and deep learning study.
PLoS computational biology
Sep 30, 2024
Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in the system poses a challenge. In this study, we propose a novel modelling framework integrating the...
A Physics-Informed Neural Network approach for compartmental epidemiological models.
PLoS computational biology
Sep 5, 2024
Compartmental models provide simple and efficient tools to analyze the relevant transmission processes during an outbreak, to produce short-term forecasts or transmission scenarios, and to assess the impact of vaccination campaigns. However, their ca...
Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling.
Epidemics
Jun 24, 2024
Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions that facilitate more informed decision-making. There are three major types of models: mechanistic model...
Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach.
Computers in biology and medicine
Jun 4, 2024
This article introduces a novel mathematical model analyzing the dynamics of Dengue in the recent past, specifically focusing on the 2023 outbreak of this disease. The model explores the patterns and behaviors of dengue fever in Bangladesh. Incorpora...
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