Infectious Disease

From Sequences to Strategies: Early Detection of New SARS-CoV-2 Variants via Genetic Distance to Reduce Hospitalizations

The COVID-19 pandemic highlighted the critical need for robust methods to monitor viral evolution and detect emerging variants of concern (VOCs). Traditional genomic surveillance often lacks predictive power. This study expanded an unsupervised machine learning clustering algorithm, based on SARS-CoV-2 Spike protein Levenshtein distance, to track and predict variant predominance across six Europea...

Temporal Learning with Dynamic Range (TLDR) for Modeling Recurrent Exposure and Treatment Outcomes

The temporal sequence of clinical events is crucial in outcomes research, yet standard machine learning (ML) approaches often overlook this aspect in electronic health records (EHRs), limiting predictive accuracy. We introduce Temporal Learning with Dynamic Range (TLDR), a time-sensitive ML framework, to identify risk factors for post-acute sequelae of SARS-CoV-2 infection (PASC). Using longitudin...

Candidate Correlates of Protection in the HVTN505 HIV-1 Vaccine Efficacy Trial Identified by Positive-Unlabeled Learning

With a goal of unveiling mechanisms by which vaccines can provide protection against HIV-1 acquisition, several studies have explored correlates of ri...

Bayesian hybrid statistical and machine learning models for dengue forecasting in Bangladesh: Temporal and spatial analysis for an early warning system

Dengue remains a major public health concern in Bangladesh, yet reliable forecasting models that integrate climatic and demographic drivers are limite...

Predictive Modelling’s role in Improving Pre-exposure Prophylaxis (PrEP) Uptake in High-Risk HIV Groups in Africa: An Integrative Scoping Review

This scoping review explores how predictive modelling can strengthen pre-exposure prophylaxis (PrEP) uptake among high-risk populations in Africa, whe...

Rainfall, Mosquito Indices, and Dengue Outbreaks in Southern Taiwan: Reassessing Predictive Modeling with Machine Learning Approaches

Dengue remains a major public health challenge in southern Taiwan, where recurrent outbreaks are shaped by complex environmental and entomological dri...

Harnessing Machine Learning for Antimicrobial Resistance Surveillance in Zimbabwe

Antimicrobial resistance (AMR) poses a significant public health challenge, particularly in resource-limited settings such as Zimbabwe, where surveill...

Development of a machine learning model to predict short duration HCV treatment response

Standard durations of direct acting antivirals (DAAs; 8–12 weeks) can be a barrier to HCV treatment initiation and completion among marginalised popul...

Machine Learning-Driven Identification of Serotype-Independent Pneumococcal Vaccine Candidates using samples from Human Infection Challenge Studies

Identifying conserved, immunogenic proteins that confer protection against Streptococcus pneumoniae colonisation could enable development of serotype-...

Combining blood transcriptomic signatures improves the prediction of progression to tuberculosis among household contacts in Brazil

Tuberculosis remains a major health threat, infecting nearly a third of the world’s population. Of those infected, 5-10% progress from latent infectio...

A machine learning model for prediction of early-onset neonatal sepsis in low- and middle-income countries: Development and validation study

Early-onset sepsis (EOS), which occurs within the first 72 hours of life, can often be fatal for neonates. Machine learning (ML) models demonstrate pr...

ViraLite: An Ultracompact HIV Viral Load Self-Testing System with Internal Quality Control

The availability of effective antiretroviral therapy has made HIV manageable, provided patients have consistent access to routine viral load (VL) test...

Machine Learning based Point-of-Care Disease Diagnostics using Dried patterns formed by E. coli bacteria-laden Sessile Urine Droplets

Urinary Tract Infection (UTI), primarily caused by E. coli bacteria, is a rising global health concern, affecting women and the elderly at a dispropor...

Neural networks for dengue forecasting: a systematic review

Early forecasts of dengue are an important tool for disease mitigation. Neural networks are powerful predictive models that have made contributions to...

Machine Learning and Probabilistic Approaches for Forecasting Infectious Disease Transmission and Cases

Forecasting the effective reproductive number (Rt) and infection case counts is critical for guiding public health responses. We developed a machine l...

First-in-Human Study of a First-in-Class AI-Designed Monoclonal Antibody (GB-0669) Against the Conserved SARS-CoV-2 Spike S2 Stem Helix

Antibodies against the SARS-CoV-2 spike receptor-binding domain provided effective COVID-19 treatment until resistant variants emerged. GB-0669 is a h...

Single time-point multi-dimensional biomarker models can predict acute organ injury trajectory

Forecasting acute organ injury trajectory remains a critical clinical challenge. Current approaches rely on serial measurements, delaying decision-mak...

Demographic Drivers of Epidemic Outcomes: Sensitivity Analysis of Multidimensional Parameters in the Covasim Model

Sensitivity analysis is a key tool for identifying which model inputs most strongly influence model outputs thereby informing data collection prioriti...

From claims to care: Machine learning algorithm to classify urinary tract infection cases using Swiss health insurance data

To evaluate whether machine learning (ML) applied to comprehensive claims data without diagnostic codes can distinguish a high proportion of antibioti...

Systems Vaccinology Reveals Distinct Immune Signatures of Inhaled and Intramuscular SARS-CoV-2 Vaccination in Humans

Mucosal vaccines may reduce both infection and transmission by engaging local immunity, yet the immunological pathways they activate in humans remain ...