Data-Driven Disease Surveillance

Últimos artículos

• Routine Blood Tests Outperform CRP for Distinguishing Bacterial From Viral Infection in Children

  Mihaela Demireva, Zhecho Mitev, Djuna Chinareva-Klimentova, Svetoslav Ivanov, Georgi Nalbantov, Dimitar Mitev · 21 de septiembre de 2026

  Acute infectious diseases are among the leading causes of medical consultations and hospitalizations in children worldwide. These infections are predominantly caused by viruses or bacteria, yet differentiating between the two remains a common clinical challenge. As a result, pediatricians often defa…

• Geographically Weighted Surrogate Models for Rapid Small-Area Chronic Disease Estimation

  Aanya Gupta, Szandra P\'eter, Sara Von Hoene, Emma Von Hoene, Taylor Anderson · 3 de agosto de 2026

  Small-area estimation (SAE) enables researchers and policymakers to identify spatial disparities in health outcomes, but survey-based SAE products carry an inherent lag. Gold-standard estimates such as CDC PLACES are released roughly two years after the underlying survey data are collected, limiting…

• Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets

  Syed Roshaan Ali Shah, Kristof Van Tricht, Christina Butsko, Jeroen Degerickx, Zoltan Szantoi · 28 de julio de 2026

  High quality reference data remain a critical bottleneck for crop-type mapping at any spatial and temporal scale. Operational systems such as WorldCereal aggregate labels from heterogeneous sources such as parcel registers, national databases, field surveys, and map-derived products, each with their…

• Epidemic Informatics and Control: A Holistic Approach from System Informatics to Epidemic Response and Risk Management in Public Health

  Hui Yang, Siqi Zhang, Runsang Liu, Alexander Krall, Yidan Wang, Marta Ventura, Chris Deflitch · 16 de julio de 2026

  This paper presents a holistic systems informatics approach, i.e., Define, Measure, Analyze, Improve, and Control (DMAIC), for epidemic response and management through the intensive use of data, statistics and optimization. Despite the sustained successes of system informatics in a variety of establ…

• A Transferable Learned Temporal Prior for Transmission Reconstruction and Decision-Relevant Uncertainty in Real Outbreak Labels

  Md Ahsan Karim · 1 de julio de 2026

  Outbreak transmission reconstruction treats epidemiological timing and transmission labels as deterministic ground truth; neither has been systematically evaluated. We trained a logistic regression temporal prior on eleven disease families, locked all parameters before accessing any target outbreak …

• Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning

  Diana Guadalupe Gomez, Chenwei Wu, Zhiyi Wang, Liyue Shen, Alexander Rodr\'iguez · 23 de junio de 2026

  Epidemic forecasting models typically rely on surveillance data reported over administrative regions, treating them as atomic units, thereby obscuring sub-regional spatial structure that shapes disease dynamics. We introduce a spatially structured multimodal epidemic forecasting setting that integra…

• Generating Public Health Responses using Survey-Augmented Large Language Models

  Leonardo Marciaga, Thuyen Pham, Julia Rezvani, Alina Hyk, Chunyang Liao, Konstantinos Mitsopoulos, Raffaele Vardavas · 23 de junio de 2026

  Epidemiological models often rely on survey data to represent how individuals make health-related decisions, such as whether to vaccinate or adopt protective behaviors. However, repeated large-scale surveys are costly, time-consuming, and limited in the range of scenarios they can capture. In this w…

• Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting

  Alireza Jafari, Judy Fox, Geoffrey C. Fox, Madhav Marathe, Aniruddha Adiga · 19 de junio de 2026

  Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-health need. Reliable forecasts of epidemic time series can inform vaccination timing, hospital staffing, and resource all…

• Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring

  Yixuan Zhang (Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark), Yang Song (Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark), Hao Wang (Rutgers University, New Brunswick, NJ, USA), Samir Bhatt (Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark, MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom), Hengguan Huang (Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark, MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom) · 9 de junio de 2026

  Wastewater influenza surveillance can reveal community circulation before clinical reporting, but wastewater alone is not a fully identifiable proxy for human burden. Existing wastewater models assume a fixed evidence set, while generic evidence-acquisition methods treat official surveillance stream…

• Transferable Self-Harm Surveillance from Emergency Department Triage Notes Using an Evidence-Augmented Machine Learning Approach

  Liuliu Chen, Gowri Rajaram, Eleanor Bailey, Katrina Witt, Michelle Lamblin, Jo Robinson, Mike Conway, Vlada Rozova · 2 de junio de 2026

  Self-harm is a major public health concern, but current surveillance relying on hospital presentations is inadequate due to the low sensitivity of diagnostic codes. Emergency Department (ED) triage notes, recorded at the initial point of contact, provide a succinct summary of presentations and an op…

• Challenges in the calibration of tree-based models for imbalanced classification

  Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford · 2 de junio de 2026

  When using machine learning for imbalanced binary classification problems, it is common to subsample the majority class to create a (more) balanced training dataset. This biases the model's predictions because the model learns from data that is not fully representative of the underlying population o…

• IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem

  Aniruddha Adiga, Jingyuan Chou, Anshul Chiranth, Bryan Lewis, Ana I. Bento, Shaun Truelove, Geoffrey Fox, Madhav Marathe, Harry Hochheiser, Srini Venkatramanan · 21 de abril de 2026

  Epidemic forecasting has become an integral part of real-time infectious disease outbreak response. While collaborative ensembles composed of statistical and machine learning models have become the norm for real-time forecasting, standardized benchmark datasets for evaluating such methods are lackin…

• Technical Case Study of Privacy-Enhancing Technologies (PETs) for Public Health

  Avinash Laddha, Danil Mikhailov, Uyi Stewart · 17 de marzo de 2026

  We present a technical case study on the Privacy-Enhancing Technologies (PETs) for Public Health Challenge, a collaborative effort to safely leverage sensitive private sector data for social impact, specifically pandemic management. The project utilized Differential Privacy (DP) to create realistic,…

• CERES: A Probabilistic Early Warning System for Acute Food Insecurity

  Tom Danny S. Pedersen · 11 de marzo de 2026

  We present CERES (Calibrated Early-warning and Risk Estimation System), an automated probabilistic forecasting system for acute food insecurity. CERES generates 90-day ahead probability estimates of IPC Phase 3+ (Crisis), Phase 4+ (Emergency), and Phase 5 (Famine) conditions for 43 high-risk countri…

• Graph Neural Network Surrogates to leverage Mechanistic Expert Knowledge towards Reliable and Immediate Pandemic Response

  Agatha Schmidt, Henrik Zunker, Alexander Heinlein, Martin J. K\"uhn · 15 de enero de 2026

  During the COVID-19 crisis, mechanistic models have guided evidence-based decision making. However, time-critical decisions in a dynamical environment limit the time available to gather supporting evidence. We address this bottleneck by developing a graph neural network (GNN) surrogate of an age-str…

• Modeling ICD-10 Morbidity and Multidimensional Poverty as a Spatial Network: Evidence from Thailand

  Pratana Kukieattikool, Kittiya Ku-kiattikun, Anukool Noymai, Navaporn Surasvadi, Jantakarn Makma, Pubodin Pornratchpum, Watcharakon Noothong, Chainarong Amornbunchornvej · 7 de enero de 2026

  Health and poverty in Thailand exhibit pronounced geographic structuring, yet the extent to which they operate as interconnected regional systems remains insufficiently understood. This study analyzes ICD-10 chapter-level morbidity and multidimensional poverty as outcomes embedded in a spatial inter…

• ARIES: A Scalable Multi-Agent Orchestration Framework for Real-Time Epidemiological Surveillance and Outbreak Monitoring

  Aniket Wattamwar, Sampson Akwafuo · 6 de enero de 2026

  Global health surveillance is currently facing a challenge of Knowledge Gaps. While general-purpose AI has proliferated, it remains fundamentally unsuited for the high-stakes epidemiological domain due to chronic hallucinations and an inability to navigate specialized data silos. This paper introduc…

• Harmonizing Community Science Datasets to Model Highly Pathogenic Avian Influenza (HPAI) in Birds in the Subantarctic

  Richard Littauer, Kris Bubendorfer · 10 de diciembre de 2025

  Community science observational datasets are useful in epidemiology and ecology for modeling species distributions, but the heterogeneous nature of the data presents significant challenges for standardization, data quality assurance and control, and workflow management. In this paper, we present a d…

• GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents

  Xinyu Zhang, Yixin Wu, Boyang Zhang, Chenhao Lin, Chao Shen, Michael Backes, Yang Zhang · 1 de diciembre de 2025

  Images shared on social media often expose geographic cues. While early geolocation methods required expert effort and lacked generalization, the rise of Large Vision Language Models (LVLMs) now enables accurate geolocation even for ordinary users. However, existing approaches are not optimized for …

• MAT-MPNN: A Mobility-Aware Transformer-MPNN Model for Dynamic Spatiotemporal Prediction of HIV Diagnoses in California, Florida, and New England

  Zhaoxuan Wang, Weichen Kang, Yutian Han, Lingyuan Zhao, Bo Li · 19 de noviembre de 2025

  Human Immunodeficiency Virus (HIV) has posed a major global health challenge for decades, and forecasting HIV diagnoses continues to be a critical area of research. However, capturing the complex spatial and temporal dependencies of HIV transmission remains challenging. Conventional Message Passing …

• Combining digital data streams and epidemic networks for real time outbreak detection

  Ruiqi Lyu, Alistair Turcan, Bryan Wilder · 11 de noviembre de 2025

  Responding to disease outbreaks requires close surveillance of their trajectories, but outbreak detection is hindered by the high noise in epidemic time series. Aggregating information across data sources has shown great denoising ability in other fields, but remains underexplored in epidemiology. H…

• Leveraging Compact Satellite Embeddings and Graph Neural Networks for Large-Scale Poverty Mapping

  Markus B. Pettersson, Adel Daoud · 4 de noviembre de 2025

  Accurate, fine-grained poverty maps remain scarce across much of the Global South. While Demographic and Health Surveys (DHS) provide high-quality socioeconomic data, their spatial coverage is limited and reported coordinates are randomly displaced for privacy, further reducing their quality. We pro…

• Challenges learning from imbalanced data using tree-based models: Prevalence estimates systematically depend on hyperparameters and can be upwardly biased

  Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford · 3 de noviembre de 2025

  When using machine learning for imbalanced binary classification problems, it is common to subsample the majority class to create a (more) balanced training dataset. This biases the model's predictions because the model learns from data whose data generating process differs from new data. One way of…