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A study published in JACC shows that waist circumference and waist-to-hip ratio are better predictors of heart disease risk than BMI alone, indicating that relying solely on BMI may misclassify cardiovascular risk.
This paper presents EpiFlow, a framework for improving wastewater-based disease forecasting by processing wastewater signals, analyzing their relationship with hospital burden indicators, and incorporating them into time-varying forecasting models. The authors show that including wastewater viral load improves COVID-19 hospital admission forecasts, especially during low-prevalence periods.
This paper introduces GeoID-PINN, a physics-informed neural network for regional epidemic inference that incorporates geographic coupling via a spatial source-composition matrix. It demonstrates that accurate trajectory forecasts do not guarantee correct recovery of regional dependence structures, using simulations and COVID-19 data from Louisiana counties.
Introduces EpiNarrate, an agentic framework that separates structured numerical reasoning from natural-language generation to produce grounded epidemiological narratives from ensemble projections.
Researchers at the University of Edinburgh published a catalog of all 239 human-infective RNA viruses, analyzing their transmissibility and pandemic potential. The study highlights that viruses already capable of human-to-human spread pose the greatest threat, while most zoonotic viruses like rabies remain non-transmissible between people.
This article explains hazard ratios in health studies, why they cannot be directly converted to life expectancy changes without considering risk distribution over time, and clarifies the difference between hazard ratios and relative risks.
This paper presents a transferable learned temporal prior for outbreak transmission reconstruction, demonstrating improved performance on a real Andes virus benchmark and highlighting the importance of quantifying uncertainty in transmission labels.
This article demonstrates how to model the COVID-19 outbreak using the SEIRS model implemented in the J programming language, explaining the states and variables involved.
This paper proposes a bilayer coupled SIR/SIRS framework to model synthetic data contamination and model collapse in AI ecosystems, showing that cross-contamination between models and data corpora leads to supercritical dynamics and identifying detection-based filtering as a key intervention.
This paper introduces CAST, a polynomial-time approximation algorithm for strategically allocating HIV treatment resources to virally unsuppressed individuals in a transmission network to minimize new infections, outperforming existing baselines on real-world networks.
Google DeepMind introduces a computational discovery prototype that uses AlphaEvolve and Empirical Research Assistance to develop and score thousands of code variations in parallel, enabling faster testing of modelling approaches for epidemiology.
HantaWatch provides a real-time tracker for the MV Hondius hantavirus outbreak, detailing confirmed cases, international health responses, and expert analysis on transmission risks.