Latest AI and machine learning research in public health & policy for healthcare professionals.
When designing control strategies for an infectious disease it is critical to identify the key pathways of transmission. Data on infected hosts - when they were born, where they lived and with whom they interacted - can help infer sources of infection and transmission clusters. However such data are generally not powerful enough to identify infector-infectee pairs with any certainty. Whole-genom...
Physics-informed neural networks (PINNs) are increasingly used in mathematical epidemiology to bridge the gap between noisy clinical data and compartmental models, such as the susceptible-exposed-infected-removed (SEIR) model. However, training these hybrid networks is often unstable due to competing optimization objectives. As established in recent literature on ``gradient pathology," the gradien...
Effective public health planning and intervention strategies necessitate an understanding of the temporal and geographic distribution of disease incid...
Despite decades of work, surveillance still struggles to find specific targets across long, multi-camera video. Prior methods -- tracking pipelines, C...
Wastewater-based epidemiology provides a scalable, noninvasive framework for population-level infectious disease monitoring, but traditional assays li...
In recent years, progress in medical informatics and machine learning has been accelerated by the availability of openly accessible benchmark datasets...
Despite improvements in access to clean water and sanitation, typhoid fever outbreaks continue to cause substantial morbidity and mortality worldwide....
Background: Cardiovascular disease (CVD) prevention is limited by the major challenge of low long-term adherence to effective lifestyle regimens. Arte...
Ancestral state reconstruction is a classical problem of broad relevance in phylogenetics. Likelihood-based methods for reconstructing ancestral state...
Fraud in the health landscape is an aggravating issue, with far-reaching consequences burdening the financial stability of the health industry and thr...
Motivation: The emergence of novel viral pathogens poses critical threats to global health, yet current computational approaches for viral risk assess...
Scrub typhus remains a persistent public health concern with strong spatial and temporal variability. This study analyses the spatio-temporal distribu...
Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large languag...
When working with real-world insurance data, practitioners often encounter challenges during the data preparation stage that can undermine the statist...
Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to exp...
Antimicrobial resistance (AMR) threatens antibiotic effectiveness, but quantitatively evaluating stewardship strategies under partial observability an...
Capturing the structured mixing within a population is key to the reliable projection of infectious disease dynamics and hence informed control. Both ...
Self-supervised learning (SSL) has revolutionized representation learning, with Joint-Embedding Architectures (JEAs) emerging as an effective approach...
Background Timely detection of seasonal influenza outbreaks is critical for healthcare system preparedness and public health response. Although numero...
Background Placental dysfunction remains a leading cause of stillbirth and neonatal morbidity, yet current monitoring tools provide only indirect and ...