AIMC Topic: COVID-19

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A data-driven machine learning framework to predict side effects of AstraZeneca and sinopharm COVID-19 vaccines.

Scientific reports
Due to the widespread COVID-19 vaccinations, we are focusing more on side effects to immunizations that might affect people's perceptions, and ultimately vaccine hesitancy. Machine learning (ML)-based predictive models using individual-level data ser...

Using Machine Learning Methods to Examine Turnover Rates in State Health Agencies.

Journal of public health management and practice : JPHMP
CONTEXT: High turnover rates in the public health workforce pose ongoing challenges to maintain essential services and institutional knowledge. Recent studies indicate that job dissatisfaction, burnout, and structural barriers have intensified follow...

Impact of COVID-19 isolation measures on ICU microbial resistance dynamics: simulation-based statistical modeling analysis.

Antimicrobial resistance and infection control
BACKGROUND: The transmission of antibiotic-resistant bacteria in intensive care units (ICUs) poses a significant challenge to infection control and patient safety. While direct patient-to-patient transmission is well documented, the relative contribu...

scMFF: a machine learning framework with multiple feature fusion strategies for cell type identification.

BMC bioinformatics
Accurate cell type classification is critical for downstream analysis in single-cell RNA sequencing (scRNA-seq). Most existing methods rely on a single type of feature representation-such as statistical, information theory, matrix factorization, or d...

A novel adaptive sigma KNN model for depression and anxiety detection following the COVID 19 pandemic.

Scientific reports
Mental health disorders, such as depression and anxiety, are increasing, and thus, there is a necessity for accurate and effective detection. K-Nearest Neighbors (KNN) and extensions have been extensively used in disease detection. In this work, Adap...

Applications of Artificial Intelligence in the Control of Infectious Diseases in the Post-COVID Era: Scoping Review.

JMIR nursing
BACKGROUND: The COVID-19 pandemic exposed systemic vulnerabilities in public health infrastructure, underscoring the urgency for innovation in disease surveillance and emergency response. Artificial intelligence (AI) has emerged as a promising tool t...

Predictive surveillance and diagnosis of COVID-19: An integrative machine learning and wastewater multi-omics approach.

Water research
COVID-19 has had major global impacts, highlighting the importance of robust predictive surveillance and diagnostic systems to ensure effective public health responses. Traditional surveillance methods based on passive case counting and diagnostic te...

Innovations and challenges in vaccine development: Lessons from the SARS-CoV-2 pandemic and prospects.

Biochemical and biophysical research communications
Vaccination stands as one of the most significant achievements in public health, dramatically reducing the incidence of infectious diseases worldwide. The COVID-19 pandemic has catalyzed revolutionary advancements in vaccinology, particularly through...

ResNet-EfficientNet powered framework for high-precision cough-based classification of infectious diseases.

Scientific reports
COVID-19 is a extremely contagious disease triggered by the SARS-CoV-2 virus which mostly affects the human breathing system. Furthermore, the COVID-19 was emerged in late 2019 and escalated rapidly into a global pandemic which impacted health and ec...

Deep generative models design mRNA sequences with enhanced translational capacity and stability.

Science (New York, N.Y.)
Despite the success of messenger RNA (mRNA) COVID-19 vaccines, extending this modality to more diseases necessitates substantial enhancements. We present GEMORNA, a generative RNA model that uses transformer architectures tailored for mRNA coding seq...