Epidemics最新文献

{"title":"Insights from a Ventilation-Aware Pandemic and Outbreak Risk model (VAPOR)","authors":"Natalie J. Wilson ,&nbsp;Callandra Moore ,&nbsp;Clara Eunyoung Lee ,&nbsp;Ashleigh R. Tuite ,&nbsp;David N. Fisman","doi":"10.1016/j.epidem.2025.100878","DOIUrl":"10.1016/j.epidem.2025.100878","url":null,"abstract":"<div><div>Transmission of airborne pathogens in indoor spaces is strongly modulated by heterogeneity in ventilation. Understanding the role indoor air plays in pandemic risk is limited in part due to differing modeling approaches used in engineering and epidemiology. Here we present the VAPOR (Ventilation-Aware Pandemic and Outbreak Risk) model, a hybrid transmission framework that integrates Reed-Frost close-contact dynamics with Wells-Riley aerosol-mediated risk. Using a meta-population structure to simulate multi-patch environments (e.g., separate workplaces or schools), we explore how ventilation disparities shape epidemic potential. A fixed minority of individuals are modeled as “aerosolizers,” consistent with overdispersed real-world transmission patterns (e.g., SARS-CoV-2). Simulations reveal that both improving ventilation in high-risk patches and raising baseline ventilation across environments independently reduces risk. Parameter sweeps across air changes per hour (ACH, 2–12) demonstrate non-linear benefits with early saturation. These findings emphasize the need for targeted ventilation strategies and show how small-world effects amplify heterogeneity-driven transmission. VAPOR offers a framework for linking ventilation equity to epidemic control.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100878"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based short-term forecasting of COVID-19 hospital admissions using routine hospital patient data","authors":"Martin S. Wohlfender ,&nbsp;Judith A. Bouman ,&nbsp;Olga Endrich ,&nbsp;Alban Ramette ,&nbsp;Alexander B. Leichtle ,&nbsp;Guido Beldi ,&nbsp;Christian L. Althaus ,&nbsp;Julien Riou","doi":"10.1016/j.epidem.2025.100877","DOIUrl":"10.1016/j.epidem.2025.100877","url":null,"abstract":"<div><div>During the COVID-19 pandemic, the field of infectious disease modeling advanced rapidly, with forecasting tools developed to track trends in transmission dynamics and anticipate potential shortages of critical resources such as hospital capacity. In this study, we compared short-term forecasting approaches for COVID-19 hospital admissions that generate forecasts one to five weeks ahead, using retrospective electronic health records. We extracted different features (e.g., daily emergency department visits) from an individual-level patient dataset covering six hospitals located in the region of Bern, Switzerland, from February 2020 to June 2023. We then applied five methods – last-observation carried forward (baseline), linear regression, XGBoost and two types of neural networks – to time series using a leave-future-out training scheme with multiple cutting points and optimized hyperparameters. Performance was evaluated using the root mean square error between forecasts and observations. Generally, we found that XGBoost outperformed the other methods in predicting future hospital admissions. Our results also show that adding features such as the number of hospital admissions with fever and augmenting hospital data with measurements of viral concentration in wastewater improves forecast accuracy. This study offers a thorough and systematic comparison of methods applicable to routine hospital data for real-time epidemic forecasting. With the increasing availability and volume of electronic health records, improved forecasting methods will contribute to more precise and timely information during epidemic waves of COVID-19 and other respiratory viruses, thereby strengthening evidence-based public health decision-making.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100877"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using wastewater surveillance to improve infectious disease control in correctional facilities and congregate living settings: A modeling perspective","authors":"Daniel de-la-Rosa-Martinez ,&nbsp;Kara L. Nelson ,&nbsp;Joyce Lee ,&nbsp;Rose S. Kantor ,&nbsp;Ashley Hazel ,&nbsp;Seth Blumberg","doi":"10.1016/j.epidem.2026.100898","DOIUrl":"10.1016/j.epidem.2026.100898","url":null,"abstract":"<div><div>Wastewater surveillance is a valuable tool for monitoring infectious disease dynamics. However, its integration into outbreak control strategies in congregate settings requires further exploration. As observed during the SARS-CoV-2 pandemic, these high-risk environments can facilitate large outbreaks, further exacerbated by residents’ heightened vulnerability. Congregate settings exhibit distinct epidemiological dynamics that influence wastewater surveillance. For instance, their semi-closed populations and reduced mobility can lower environmental noise in wastewater signals, but small population sizes also increase stochastic fluctuations, complicating the interpretation of disease trends. In this context, mathematical modeling helps translate wastewater signals into actionable insights for outbreak response. This work synthesizes key benefits and challenges in applying wastewater surveillance in congregate settings and identifies modeling approaches that have potential to improve outbreak detection, enhance monitoring of transmission dynamics, and optimize infection control strategies. This provides a conceptual framework for expanding wastewater surveillance to strengthen infectious disease control in these high-risk populations.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100898"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147318762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis","authors":"Thomas Bayley ,&nbsp;Tony Ward ,&nbsp;Fabian Sturman ,&nbsp;Akashaditya Das ,&nbsp;Luca Imeneo ,&nbsp;Cliff Kerr ,&nbsp;Christophe Fraser ,&nbsp;Simon Maskell ,&nbsp;Jasmina Panovska-Griffiths","doi":"10.1016/j.epidem.2025.100881","DOIUrl":"10.1016/j.epidem.2025.100881","url":null,"abstract":"<div><div>Mathematical modelling with agent-based models (ABMs) has gained popularity during the COVID-19 pandemic, but their complexity makes efficient and robust calibration to data challenging, particularly when applying Bayesian methods to quantify parameter uncertainty. We propose a method for calibrating ABMs that combines a Machine-Learning step with Approximate Bayesian Computation (ML-ABC). We showcase ML-ABC application with a proof-of-principle case study, in which we calibrate the Covasim -a stochastic ABM that has been used to model the English COVID-19 epidemic and inform policy at important junctions. Benchmarking against traditional Rejection-ABC (R-ABC), we illustrate the advantage of ML-ABC application in calibrating Covasim to data on hospitalisations and deaths from COVID-19 during the first and the second COVID-19 epidemic waves of 2020 and early 2021. Across scenarios, we demonstrate that using an ML screening step allows us to derive identical posterior distributions of the calibrated Covasim parameters as with the traditional R-ABC method, but faster. Specifically, we derive posterior distributions for input parameters around 52% faster when calibrating to the first epidemic wave and around 33% faster when calibrating parameters for the second epidemic wave, compared to the traditional R-ABC. Policy modelling requires calibration which is both efficient to adapt to fast-changing pandemic environments and robust to ensure confidence in policy decisions. However, existing ABM calibration often relies on myopic non-exhaustive searches in order to remain tractable, resulting in point parameter estimates. In this preliminary study, ML-ABC strictly improves upon existing ABC calibration approaches in all tested scenarios, indicating its potential to make ABC competitive with point-estimate calibration approaches. This novel approach offers a pathway to effectively calibrate ABMs in a way which is both efficient and quantifies parameter uncertainty, crucial for realising the potential of ABMs for timely and responsively modelling during an emerging epidemic.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100881"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From wastewater to infection estimates: Incident COVID-19 infections during Omicron in the U.S.","authors":"Rachel Lobay ,&nbsp;Ajitesh Srivastava ,&nbsp;Daniel J. McDonald","doi":"10.1016/j.epidem.2026.100889","DOIUrl":"10.1016/j.epidem.2026.100889","url":null,"abstract":"<div><div>Reconstructing the course of the COVID-19 pandemic through estimating incident infections is important for assessing disease burden and characterizing transmission dynamics. While wastewater concentration data have been used to estimate infections in localized pre-Omicron studies, a scalable approach that estimates variant-specific shedding rates and that accounts for underreporting remains underdeveloped. To this end, we develop a multi-source approach to retrospectively estimate daily COVID-19 infections in U.S. states during the Omicron era. Our approach integrates wastewater and seroprevalence surveillance data to improve infection estimates during the Delta-Omicron transition period. These refined estimates, along with wastewater concentration data adjusted for limited coverage, are used to calculate variant-specific shedding rates, which inform daily infection estimates going forward. While case-based estimates tend to exhibit striking volatility, these infection estimates show more stable and interpretable patterns that closely align with Omicron subvariant transitions. Moreover, we directly quantify the degree of underreporting, showing the extent that reported cases significantly underestimate disease burden in a sample of seven U.S. states. In these states, case reports capture less than a quarter of total infections, leaving the vast majority unaccounted for in official reports. Finally, we estimate time-varying effective reproduction numbers and growth rates to provide a more accurate and timely picture of transmission dynamics over the Omicron era in U.S. states.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100889"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian spatio-temporal modelling for infectious disease outbreak detection","authors":"Matthew Adeoye ,&nbsp;Xavier Didelot ,&nbsp;Simon E.F. Spencer","doi":"10.1016/j.epidem.2025.100879","DOIUrl":"10.1016/j.epidem.2025.100879","url":null,"abstract":"<div><div>The Bayesian analysis of infectious disease surveillance data from multiple locations typically involves building and fitting a spatio-temporal model of how the disease spreads in the structured population. Here we present new generally applicable methodology to perform this task. We introduce a parsimonious representation of seasonality and a biologically informed specification of the outbreak component to avoid parameter identifiability issues. We develop a computationally efficient Bayesian inference methodology for the proposed models, including techniques to detect outbreaks by computing marginal posterior probabilities at each spatial location and time point. We show that it is possible to efficiently integrate out the discrete parameters associated with outbreak states, enabling the use of dynamic Hamiltonian Monte Carlo (HMC) as a complementary alternative to a hybrid Markov chain Monte Carlo (MCMC) algorithm. Furthermore, we introduce a robust Bayesian model comparison framework based on importance sampling to approximate model evidence in high-dimensional space. The performance of our methodology is validated through systematic simulation studies, where simulated outbreaks were successfully detected, and our model comparison strategy demonstrates strong reliability. We also apply our new methodology to monthly incidence data on invasive meningococcal disease from 28 European countries. The results highlight outbreaks across multiple countries and months, with model comparison analysis showing that the new specification outperforms previous approaches. The accompanying software is freely available as a R package at <span><span>https://github.com/Matthewadeoye/DetectOutbreaks</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100879"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmenting community-driven vector surveillance with automated image classification: Lessons from the Artificial Intelligence Mosquito Alert (AIMA) system","authors":"Monika Falk ,&nbsp;Joan Garriga ,&nbsp;Roger Eritja ,&nbsp;Isis Sanpera-Calbet ,&nbsp;Enric Pou ,&nbsp;Alex Richter-Boix ,&nbsp;John R.B. Palmer ,&nbsp;Frederic Bartumeus","doi":"10.1016/j.epidem.2025.100863","DOIUrl":"10.1016/j.epidem.2025.100863","url":null,"abstract":"<div><div>The Mosquito Alert (MA) platform leverages artificial intelligence to enhance community-driven mosquito surveillance by automatically identifying mosquito species from geolocated images submitted via a mobile app. This empowers the public to report both native and invasive mosquitoes of public health relevance, contributing to early detection and monitoring efforts. The Artificial Intelligence Mosquito Alert (AIMA) system integrates machine learning image classification within an automated backend pipeline to enable real-time triaging of submissions: critical reports are flagged for expert review, routine cases are classified automatically, and contributors receive immediate feedback fostering participant engagement. By automating routine identifications, the system reduces the burden on experts, allowing them to focus on complex or ambiguous cases that require taxonomic expertise. This study focuses on two AIMA operational periods in 2023 and 2024. We evaluate model updates and performance across these years, highlighting both progress achieved and remaining limitations under real-world citizen science conditions. The most reliably classified species across both models were <em>Aedes albopictus</em> and <em>Culex</em> sp., whereas <em>Aedes aegypti</em> remained difficult to identify. Despite its limitations, AIMA remains central to enabling scalable, responsive, and intelligent mosquito vector surveillance, substantially reducing the time experts must devote to routine identifications. Functioning as an Early Warning System (EWS), MA produces real-time distribution maps of invasive species and rapidly delivers actionable information to public health authorities, facilitating timely responses and intervention.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100863"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning approach for enhancing pandemic prediction: A retrospective evaluation of transformer neural networks and multi-source data fusion for infectious disease forecasting","authors":"Jiande Wu ,&nbsp;Shakhawat Tanim ,&nbsp;MinJae Woo ,&nbsp;Tanvir Ahammed ,&nbsp;Amanda Marie Bleichrodt ,&nbsp;Lior Rennert","doi":"10.1016/j.epidem.2025.100865","DOIUrl":"10.1016/j.epidem.2025.100865","url":null,"abstract":"<div><div>This paper introduces a deep learning model for county-level Covid-19 forecasting, presenting it as a retrospective case study. We utilize a transformer neural network with multi-source data fusion, incorporating historical case data, death data, and social media sentiment to capture complex temporal and spatial dynamics. Additionally, we develop multi-level and multi-scale attention mechanisms for adaptive time-frequency analysis. In a retrospective evaluation across three Omicron variant waves (December 2021 through February 2023), the model demonstrated strong performance in predicting county-level Covid-19 cases and deaths, with median county agreement accuracy ranging from 74.0 % to 82.6 % for one-week case forecasts and 68.7–79.6 % for 5-week case forecasts. While these historical results are promising, prospective validation is needed to assess the model's utility under live, evolving data conditions. Median county agreement accuracy for deaths ranged from 83.2 % to 86.3 % for one-week forecasts and 84.3–87.2 % for five-week forecasts. Incorporating social media data yielded mild to moderate improvement in forecasting accuracy. Overall, the proposed model yielded substantial improvements compared to a baseline persistence model utilizing the last observation carried forward. By integrating real-time data and capturing complex pandemic dynamics, this approach surpasses traditional methods. The results demonstrate the model's strong performance in a retrospective setting, highlighting the utility of multi-source data fusion and attention mechanisms for fine-grained epidemiological forecasting. This work serves as a case study on the application of advanced deep learning techniques to local-level pandemic data, offering a methodological framework for future research.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100865"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145460189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the spatio-temporal spread of cholera in France in 1892","authors":"Charlotte Perlant ,&nbsp;François-Xavier Weill ,&nbsp;Juliette Paireau ,&nbsp;Mirabelle Scipioni ,&nbsp;Paolo Bosetti ,&nbsp;Simon Cauchemez","doi":"10.1016/j.epidem.2025.100872","DOIUrl":"10.1016/j.epidem.2025.100872","url":null,"abstract":"<div><div>From an historical perspective, it is important to understand how past epidemics spread; but such a task is complicated by limited data availability. Here, using unique digitized historical data, we characterized the patterns and drivers of spread of the last major French cholera epidemic in 1892. We found that epidemic dynamics are well captured by a standard gravity model, highlighting the key contribution of human mobility to cholera spread. Our findings also underscore the crucial role of major commercial ports that acted both as points of introduction from external sources (multiple introductions were estimated) and as local transmission hubs (transmission rates increased by a factor of 10 around ports). We also estimated a 2.5-fold increase in transmission rates in mid-August, compensated by a reduction in the duration of infectivity of municipalities, highlighting both seasonality in transmission and the effectiveness of control measures implemented in 1892. Applying modern analytical techniques to historical outbreaks enhances our understanding of past pandemics.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100872"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145649825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environmental drivers of Ixodes ricinus tick population dynamics: Mechanistic modelling using longitudinal field surveys and climate data","authors":"Younjung Kim ,&nbsp;Benoît Jaulhac ,&nbsp;Juan F. Vesga ,&nbsp;Laurence Zilliox ,&nbsp;Nathalie Boulanger ,&nbsp;W.John Edmunds ,&nbsp;Raphaëlle Métras","doi":"10.1016/j.epidem.2025.100854","DOIUrl":"10.1016/j.epidem.2025.100854","url":null,"abstract":"<div><div><em>Ixodes ricinus</em> is the primary vector for Lyme disease and tick-borne encephalitis across Europe. However, the environmental drivers of the tick's complex life cycle have not been quantified with real-world data, making it challenging to incorporate tick demography into tick-borne disease transmission models. To address this gap, we fitted a mechanistic model to a detailed 10-year longitudinal dataset from four sites in Northern France, where <em>I. ricinus</em> is abundant and Lyme disease and tick-borne encephalitis have been reported for decades. By incorporating key demographic processes and the influence of environmental conditions on these processes, our model estimated oviposition, hatching, and moulting rates across a range of temperature or saturation deficit, as well as questing and vertebrate host contact rates. In the studied tick population, moulting peaked at 14.2 °C (95 %HDI: 12.5–16.1 °C), substantially lower than commonly suggested by laboratory-based studies, whereas oviposition and hatching peaked at 24.4 °C (95 %HDI: 10.9–27.2 °C) and 24.7 °C (95 %HDI: 17.8–27.2 °C), respectively. Furthermore, the parameter scaling the empirical baseline vertebrate host contact rates was found to vary significantly between the four study sites, with one site presenting up to 2.90 (95 %HDI: 2.15–3.86) times higher contact rates than the other three sites. Additionally, for ticks overwintering through diapause, moulting in spring more accurately matched the predominantly unimodal questing patterns observed, compared to moulting in summer. Finally, model projections under pessimistic climate change scenarios indicated decreasing tick abundance trends over the next two decades, while no significant decrease was predicted under moderate scenarios. This study provides a foundation for models of <em>I. ricinus</em>-borne pathogen transmission and can be adapted to other <em>Ixodidae</em> populations of public health significance.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100854"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}