Population-level respiratory virus-virus interactions, Puerto Rico, 2013-2023.

Background: Understanding virus-virus interactions is important for evaluating disease transmission and severity. Positive interactions suggest concurrent circulation, while negative interactions indicate reduced transmission of one virus when another is prevalent. This study examines interactions among seven respiratory viruses using a Bayesian approach that accounts for seasonality and long-term trends.

Machine learning for predicting severe dengue in Puerto Rico.

Background: Distinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. World Health Organization (WHO)-recommended warning signs offer a practical approach for clinicians but have limited sensitivity and specificity. This study aims to evaluate machine learning (ML) model performance compared to WHO-recommended warning signs in predicting severe dengue among laboratory-confirmed cases in Puerto Rico.