COVID-19 epidemiological studies

Últimos artículos

• Hybrid epidemic simulation framework coupling equation-based and individual-based models

  Jaeyoung Kwak, Michael H. Lees, Chin Chun Ooi, Wentong Cai · 29 de septiembre de 2026

  Mass gathering events like concerts, sports matches, and festivals bring many people into close contact within a short period, creating localized bursts of infection that can shape epidemic outcomes across an entire city. To evaluate how these transient transmission events translate into broader urb…

• Driving Epidemic Models with AI Agents: the Epydemix Agent Framework

  Nicol\`o Gozzi, Ciro Cattuto, Alessandro Vespignani · 25 de septiembre de 2026

  Artificial Intelligence agents based on large language models provide convenient natural language interfaces to scientific software, but reliability is not automatic. Here we introduce the Epydemix Agent Framework, an additive layer over Epydemix, an open-source Python library for stochastic compart…

• TERN: A Delta-rule Memory with a Seasonal Reference and Online Adaptation for Epidemic Forecasting

  Shunya Nagashima, Yuta Funayama · 17 de septiembre de 2026

  Weekly influenza surveillance counts guide vaccine distribution and public-health alerts, yet they are hard to forecast. Each region offers only a few seasons, waves shift in timing and height every year, and information that helps while a wave grows misleads after its peak, whereas last season's sh…

• Mitigating Disease Spread by Design in Refugee and IDP Camps

  Giulia Zarpellon, Joseph Aylett-Bullock, Frank Krauss, Miguel Luengo-Oroz · 7 de septiembre de 2026

  Disease spread represents an increasing challenge in refugee and internally displaced person (IDP) settlements. The movement and interaction of people within camps is influenced by their layout, which therefore has the potential to significantly affect disease spread. This work aims at creating a me…

• EpiFlow: A framework for improving the utility of wastewater signals for disease forecasting

  Aniruddha Adiga, Jingyuan Chou, Gursharn Kaur, Andrew Warren, Srinivasan Venkatramanan, Baltazar Espinoza, Bryan Lewis, Justin Crow, Alexandra Lorentz, Rekha Singh, Madhav Marathe · 10 de agosto de 2026

  Wastewater-based surveillance is an effective tool for disease monitoring and can provide early warning of outbreaks. Although wastewater viral loads (WVL) correlate with disease burden, their utility for improving real-time forecasting remains under investigation. During the early phases of an epid…

• GeoID-PINN: Identifiability-Aware Regional Epidemic Inference with Geographic Coupling

  Weixiong Hua, Fan Bu · 5 de agosto de 2026

  Regional surveillance data reflect local transmission, reporting, seeding, and external infection pressure, which are difficult to identify separately. We introduce GeoID-PINN, a physics-informed neural network (PINN) for susceptible-infectious-recovered-deceased (SIRD) dynamics. The model represent…

• Boundary Degree as a Node-level Feature for Epidemic Scenario Identification in Agent-based Cascade Simulations

  Amro Alabsi Aljundi, Galen Harrison, Jiangzhuo Chen, Abhijin Adiga, Anil Kumar Vullikanti, Madhav V. Marathe · 30 de junio de 2026

  Characterizing the scenario underlying an epidemic from its disease cascade is an important task in simulation analytics. We propose boundary degree, the count of an infected node's contacts in the underlying contact network that were not infected, as a per-node cascade feature for this task. Throug…

• Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC

  Alina Bazarova, Johann Fredrik Jadebeck, Henrik Zunker, Carolina J. Klett-Tammen, Torben Heinsohn, Wolfgang Wiechert, Katharina Noeh, Stefan Kesselheim · 26 de junio de 2026

  Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making. Bayesian calibration of such models is commonly performed using Markov chain Monte Carlo (MCMC), which can become computationally expensive for high-dimensional nonlinear s…

• When Networks Substitute for Outcome Surveillance? A Substitution-Complementarity Framework for Behavioral Signals in Predictive Monitoring

  Sepehr Ilami, Qingtao Cao, Babak Heydari · 25 de junio de 2026

  Monitoring systems increasingly fuse dynamic behavioral data with outcome-based surveillance, raising a basic question: when does behavioral data carry predictive information that outcome history lacks? We study this using epidemic forecasting on mobility networks, asking whether mobility networks p…

• Leveraging Social Media Data for COVID-19 Studies

  Nur Hafieza Ismail, Nur Shazwani Kamarudin, Nurol Husna Che Rose · 10 de junio de 2026

  Nowadays, social media networks have become widely preferred sources of information. Especially during the time of the Coronavirus disease 2019 COVID 19 pandemic, social media has been one of the most used platforms to get the latest news and information related to COVID 19. Social media are popular…

• Is Telehealth Better Used to Treat Patients or Help Other Physicians Treat Patients? An Agent-Based Modeling Study of Healthcare Provision

  Michael Chary · 9 de junio de 2026

  Telehealth, the delivery of medical care remotely, is hoped to increase access to specialty services or decrease health care utilization. Physicians can provide telehealth to each other or to patients. Specialists often treat complex patients who can be adequately cared for only in academic hospital…

• What Causes COVID-19 Fear? General Drivers of Fear During a Health Crisis

  Daniele Baccega, Paolo Castagno, Antonio Fern\'andez Anta, Juan Marcos Ramirez, Matteo Sereno · 8 de junio de 2026

  The COVID-19 pandemic triggered not only a global health crisis but also an infodemic, where exposure to heterogeneous information sources influenced public emotional responses. In this work, we investigate the determinants of self-reported fear of infection using data from the Delphi US CTIS survey…

• An Infectious Disease Spread Simulation Based on Large Language Model Decision Making

  Yonchanok Khaokaew, Ruochen Kong, Andreas Zufle, Hao Xue, Taylor Anderson, Chandini Raina MacIntyre, Matthew Scotch, Flora D. Salim, David J Heslop · 5 de junio de 2026

  Modelling individual decision-making during infectious disease outbreaks is crucial for understanding behavioural dynamics and informing effective public health interventions. Prior work has shown that large language models can simulate realistic human behaviour by generating agent decisions based o…

• EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts

  Yiming Lu, Sihang Zeng, Zhengxu Tang, Max Lau, Fei Liu, Wei Jin · 5 de junio de 2026

  Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming process in which labels arrive after predictions and disease regimes shift over time. We study this mismatch in weekly COVID-19 hospitalization trend foreca…

• Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models

  Janani Venugopalan, Gaurav Deshkar, Rishabh Gaur, Harshal Hayatnagarkar, Jayanta Kshirsagar · 4 de junio de 2026

  Purpose The WHO's COVID-19 non-pharmaceutical interventions (e.g., lockdowns, vaccinations) effectively curb transmission but impose heavy economic strains. Existing research often neglects individual behaviors and falsely assumes perfect infection tracking and flawless policy execution, failing to …

• The Epi-LLM Framework: probing LLM behavioral priors through epidemiological agent-based models

  Petra Ferenz, Ava Keeling, Tobias O'Keefe, Lorenzo Stigliano, Francesco Di Lauro, Andres Colubri, Jasmina Panovska-Griffiths · 3 de junio de 2026

  Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging. Here we introduce the Epi-LLM framework: a novel integration of agent-based modelling, real-life epigames, and large language models (LLMs) in which a synthetic society of agents re…

• Spatio-temporal stochastic graph-based learning for infectious disease forecasting

  Luz Stefani Sotomayor Valenzuela, Susanna Cramb, Darren Wraith · 1 de junio de 2026

  Spatio-temporal graph-based models have typically been used to forecast new cases of infectious diseases such as COVID-19 and chickenpox outbreaks. However, the use of stochastic modelling into their learning process has been surprisingly under-investigated and rarely considered entire data sets of …

• SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation

  Haochun Wang, Sendong Zhao, Jingbo Wang, Yanrui Du, Bing Qin, Ting Liu · 27 de mayo de 2026

  Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread, creating feedback loops that induce distribution shifts at policy intervention points. This renders data-driven models unreliable under distribution shift. We propose \textbf{SL-BiLEM} (Structu…

• Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search

  Sarah Martinson, Michael P. Brenner, Martyna Plomecka, Brian P. Williams, Nicholas G. Reich, Zahra Shamsi · 18 de mayo de 2026

  Probabilistic forecasting of infectious diseases is crucial for public health but relies on labor-intensive manual model curation by expert modeling teams. This bespoke development bottlenecks scalability to granular geographic resolutions or emerging pathogens. Here, we present an autonomous system…

• Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection

  Zhaowei She, Zilong Wang, Jagpreet Chhatwal, Turgay Ayer · 14 de mayo de 2026

  The COVID-19 pandemic has exerted a profound impact on the global economy and continues to exact a significant toll on human lives. The COVID-19 case growth rate stands as a key epidemiological parameter to estimate and monitor for effective detection and containment of the resurgence of outbreaks. …

• EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting

  Madhurima Panja, Danny D'Agostino, Huitao Li, Tanujit Chakraborty, Nan Liu · 13 de mayo de 2026

  The increasing adoption of data-driven decision-making in public health has established epidemic forecasting as a critical area of research. Recent advances in multivariate forecasting models better capture complex temporal dependencies than conventional univariate approaches, which model individual…

• Generative diffusion models for spatiotemporal influenza forecasting

  Joseph Lemaitre, Justin Lessler · 29 de abril de 2026

  Forecasting infectious disease incidence can provide important information to guide public health planning, yet is difficult because epidemic dynamics are complex. Current mechanistic and statistical approaches often struggle to capture multimodal uncertainty or emergent trends. Influpaint adapts de…

• LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals

  Joon Sung Park, Carolyn Q. Zou, Jonne Kamphorst, Niles Egan, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Percy Liang, Robb Willer, Michael S. Bernstein · 23 de abril de 2026

  Machine learning can predict human behavior well when substantial structured data and well-defined outcomes are available, but these models are typically limited to specific outcomes and cannot readily be applied to new domains. We test whether large language models (LLMs) can support a more general…

• Mantis: A Foundation Model for Mechanistic Disease Forecasting

  Carson Dudley, Reiden Magdaleno, Christopher Harding, Ananya Sharma, Emily Martin, Marisa Eisenberg · 15 de abril de 2026

  Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate data sets, bespoke training, and expert tuning, all of which can hinder rapid generation of forecasts for new settings. To help address these challenges, we developed Ma…

• Toward World Models for Epidemiology

  Zeeshan Memon, Yiqi Su, Christo Kurisummoottil Thomas, Walid Saad, Liang Zhao, Naren Ramakrishnan · 13 de abril de 2026

  World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertainty. In this paper, we argue that computational epidemiology is a natural and underdeveloped setting for world models. This is because epidemic deci…