Public Health

[Progress in application of machine learning in epidemiology].

Population based health data collection and analysis are important in epidemiological research. In recent years, with the rapid development of big data, Internet and cloud computing, artificial intelligence has gradually attracted attention of epidemiological researchers. More and more researchers are trying to use artificial intelligence algorithms for genome sequencing and medical image data min...

Bayesian estimation of transmission networks for infectious diseases

Reconstructing transmission networks is essential for identifying key factors like superspreaders and high-risk locations, which are critical for developing effective pandemic prevention strategies. In this study, we developed a Bayesian framework that integrates genomic and temporal data to reconstruct transmission networks for infectious diseases. The Bayesian transmission model accounts for t...

Analyzing factors of daily travel distances in Japan during the COVID-19 pandemic.

The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. C...

Ontologies related to livestock for the Global Burden of Animal Diseases programme: a review.

The Global Burden of Animal Diseases (GBADs) programme aims to assess the impact of animal health on agricultural animals, livestock production system...

Flusion: Integrating multiple data sources for accurate influenza predictions

Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the mo...

SecureVAX: A Blockchain-Enabled Secure Vaccine Passport System

A vaccine passport serves as documentary proof, providing passport holders with greater freedom while roaming around during pandemics. It confirms v...

Response to the letter to the editor regarding "Assisting the Infection Preventionist: Use of Artificial Intelligence for Healthcare-Associated Infection Surveillance".

Use of artificial intelligence for health care-associated infection surveillance: Correspondence.

Automated cooling tower detection through deep learning for Legionnaires' disease outbreak investigations: a model development and validation study.

BACKGROUND: Cooling towers containing Legionella spp are a high-risk source of Legionnaires' disease outbreaks. Manually locating cooling towers from ...

ECG-based Daily Activity Recognition Using 1D Convolutional Neural Networks.

This study presents an approach to human activity recognition (HAR) using electrocardiogram (ECG) signals. We explore the application of ECG for not o...

Towards Fluorescent-Tag-Less Viral Titration: Automated Estimation of Cell-Size Distribution and Infection Level from Phase-Contrast Microscopy Using Deep Learning and Transfer Learning.

Automated detection of infected insect cells is one of the crucial tasks in the field of recombinant protein production and vaccine development. The m...

Predicting Diabetes in Canadian Adults Using Machine Learning.

Rising diabetes rates have led to increased healthcare costs and health complications. An estimated half of diabetes cases remain undiagnosed. Early a...

Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of Automatic Data Annotation Approach

In recent years, the United States has witnessed a significant surge in the popularity of vaping or e-cigarette use, leading to a notable rise in ca...

Streamlining social media information retrieval for public health research with deep learning.

OBJECTIVE: Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-mat...

Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination

The COVID-19 pandemic exposed significant weaknesses in the healthcare information system. The overwhelming volume of misinformation on social media...

Harnessing causal forests for epidemiologic research: key considerations.

Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inferenc...

Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases.

COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbre...

A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models

Antibodies are crucial proteins produced by the immune system to eliminate harmful foreign substances and have become pivotal therapeutic agents for...

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences abo...

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen p...