Epidemics最新文献

{"title":"Assessing the effectiveness of travel control measures in preventing imported COVID-19 cases reveals the critical role of travel volume","authors":"Mingwei Li ,&nbsp;Karen A. Grépin ,&nbsp;Ru Zhang ,&nbsp;Benjamin J. Cowling ,&nbsp;Bingyi Yang","doi":"10.1016/j.epidem.2025.100837","DOIUrl":"10.1016/j.epidem.2025.100837","url":null,"abstract":"<div><h3>Background</h3><div>Although travel control measures have played a key role in mitigating COVID-19 spread in certain regions, few empirical observational studies have specifically quantified their effectiveness in preventing the importation of infectious cases into communities. In Hong Kong, layered policies (e.g., mandatory quarantine, staggered testing protocols, and phased travel volume restriction) provided a natural experiment to disentangle these components. Our study evaluates the contributions of each measure to preventing imported infectious cases releasing to community.</div></div><div><h3>Methods</h3><div>We retrospectively assessed these measures' effectiveness in Hong Kong, utilizing data from eight countries during 2020–2021. Data on imported COVID-19 cases, including departure origins and time from arrival to report, was compiled. To estimate the SARS-CoV-2 prevalence among inbound travelers, we used a Bayesian framework that accounted for the disease history and testing sensitivity and fitted to cases detected on arrival and travel volumes. We compared the number of prevented infections under the implemented measures to a scenario where no measures were taken. We also conducted counterfactual analysis to examine the independent and marginal effects of individual measures.</div></div><div><h3>Results</h3><div>Stringent travel measures prevented 9821 (9065 – 10,564) importations from entering Hong Kong. Travel volume reductions had the greatest impact (93.0 % reduction, 95 % confidence interval, CI: 91.9 %-93.9 %), followed by mandatory quarantine (80.8 % reduction, 95 % CI: 75.7 % - 87.1 %). In-quarantine COVID-19 testing showed no substantial additional effectiveness in preventing infectious COVID-19 cases into community (81.8 % reduction, 95 % CI:74.8 %-87.1 %) beyond mandatory quarantine alone.</div></div><div><h3>Conclusions</h3><div>Our findings demonstrate that while stringent post-arrival measures effectively reduced community transmission of imported COVID-19 cases, travel volume reduction played a critical and independent role in limiting viral importation, regardless of post-arrival interventions.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100837"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090387","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":"Accounting for the geometry of the respiratory tract in viral infections","authors":"Thomas Williams ,&nbsp;James M. McCaw ,&nbsp;James M. Osborne","doi":"10.1016/j.epidem.2025.100829","DOIUrl":"10.1016/j.epidem.2025.100829","url":null,"abstract":"<div><div>Increasingly, experimentalists and modellers alike have come to recognise the important role of spatial structure in infection dynamics. Almost invariably, spatial computational models of viral infections — as with <em>in vitro</em> experimental systems — represent the tissue as wide and flat, which is often assumed to be representative of the entire affected tissue within the host. However, this assumption fails to take into account the distinctive geometry of the respiratory tract in the context of viral infections. The respiratory tract is characterised by a tubular, branching structure, and moreover is spatially heterogeneous: deeper regions of the lung are composed of far narrower airways and are associated with more severe infection. Here, we extend a typical multicellular model of viral dynamics to account for two essential features of the geometry of the respiratory tract: the tubular structure of airways, and the branching process between airway generations. We show that, with this more realistic tissue geometry, the dynamics of infection are substantially changed compared to standard computational and experimental approaches, and that the resulting model is equipped to tackle important biological phenomena that do not arise in a flat host tissue, including viral lineage dynamics, and heterogeneity in immune responses to infection in different regions of the respiratory tree. Our findings suggest aspects of viral dynamics which current <em>in vitro</em> systems may be insufficient to describe, and points to several features of respiratory infections which can be experimentally assessed.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100829"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907492","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":"Integrative modeling of the spread of serious infectious diseases and corresponding wastewater dynamics","authors":"Nina Schmid ,&nbsp;Julia Bicker ,&nbsp;Andreas F. Hofmann ,&nbsp;Karina Wallrafen-Sam ,&nbsp;David Kerkmann ,&nbsp;Andreas Wieser ,&nbsp;Martin J. Kühn ,&nbsp;Jan Hasenauer","doi":"10.1016/j.epidem.2025.100836","DOIUrl":"10.1016/j.epidem.2025.100836","url":null,"abstract":"<div><div>The COVID-19 pandemic has emphasized the critical need for accurate disease modeling to inform public health interventions. Traditional reliance on confirmed infection data is often hindered by reporting delays and under-reporting, while antigen or antibody testing of a full cohort can be costly and impractical. Wastewater-based surveillance offers a promising alternative by detecting viral concentrations from fecal shedding, potentially providing a more accurate estimate of true infection prevalence. However, challenges remain in optimizing sampling protocols, locations, and normalization strategies, particularly in accounting for environmental factors like precipitation.</div><div>We present an integrative model that simulates the spread of serious infectious diseases by linking detailed infection dynamics with wastewater processes through viral shedding curves. Through comprehensive simulations, we examine how virus characteristics, precipitation events, measurement protocols, and normalization strategies affect the relationship between infection dynamics and wastewater measurements. Our findings reveal a complex relationship between disease prevalence and corresponding wastewater concentrations, with key variability sources including upstream sampling locations, continuous rainfall, and rapid viral decay. Notably, we find that flow rate normalization can be unreliable when rainwater infiltrates sewer systems. Despite these challenges, our study demonstrates that wastewater-based surveillance data can serve as a leading indicator of disease prevalence, predicting outbreak peaks before they occur. The proposed integrative model can thus be used to optimize wastewater-based surveillance, enhancing its utility for public health monitoring.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100836"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220809","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":"Estimating social contact rates for the COVID-19 pandemic using Google mobility and pre-pandemic contact surveys","authors":"Em Prestige ,&nbsp;Pietro Coletti ,&nbsp;Jantien Backer ,&nbsp;Nicholas G. Davies ,&nbsp;W. John Edmunds ,&nbsp;Christopher I. Jarvis","doi":"10.1016/j.epidem.2025.100830","DOIUrl":"10.1016/j.epidem.2025.100830","url":null,"abstract":"<div><div>During the COVID-19 pandemic, aggregated mobility data was frequently used to estimate changing social contact rates. By taking pre-pandemic contact matrices, and transforming these using pandemic-era mobility data, infectious disease modellers attempted to predict the effect of large-scale behavioural changes on contact rates. This study explores the most accurate method for this transformation, using pandemic-era contact surveys as ground truth. We compared four methods for scaling synthetic contact matrices: two using fitted regression models and two using “naïve” mobility or mobility squared models. The regression models were fitted using the CoMix contact survey and Google mobility data from the UK over March 2020 – March 2021. The four models were then used to scale synthetic contact matrices—a representation of pre-pandemic behaviour—using mobility data from the UK, Belgium and the Netherlands to predict the number of contacts expected in “work” and “other” settings for a given mobility level. We then compared partial reproduction numbers estimated from the four models with those calculated directly from CoMix contact matrices across the three countries. The accuracy of each model was assessed using root mean squared error. The fitted regression models had substantially more accurate predictions than the naïve models, even when models were applied to out-of-sample data from the UK, Belgium and the Netherlands. Across all countries investigated, the linear fitted regression model was the most accurate and the naïve model using mobility alone was the least accurate. When attempting to estimate social contact rates during a pandemic without the resources available to conduct contact surveys, using a model fitted to data from another pandemic context is likely to be an improvement over using a “naïve” model based on mobility data alone. If a naïve model is to be used, mobility squared may be a better predictor of contact rates than mobility per se.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100830"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877350","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":"The effect of COVID-19 vaccination on change in contact and implications for transmission","authors":"Carol Y. Liu ,&nbsp;Aaron Siegler ,&nbsp;Patrick Sullivan ,&nbsp;Samuel M. Jenness ,&nbsp;Stefan Flasche ,&nbsp;Benjamin Lopman ,&nbsp;Kristin Nelson","doi":"10.1016/j.epidem.2025.100827","DOIUrl":"10.1016/j.epidem.2025.100827","url":null,"abstract":"<div><h3>Background</h3><div>Monitoring human behavior as epidemic intelligence can critically complement traditional surveillance systems during epidemics. Retrospective analysis of novel behavioral data streams initiated during the COVID-19 pandemic help illustrate their utility. During the pandemic, behavior changed rapidly and was increasingly influenced by individual choice in response to changes such as newly available vaccines. Vaccines provided substantial protection against severe disease and deaths; however, their effect on behavior is understudied and it is unclear if vaccine effects against infection fully offset relaxation of social distancing behaviors.</div></div><div><h3>Methods &amp; results</h3><div>We analyzed data from a longitudinal cohort sampled from U.S. households that measured contact rates, risk mitigation and COVID-19 vaccination status between August 2020-April 2022. Contact rates universally increased across survey rounds among all sociodemographic groups, but unvaccinated individuals had persistently higher contact rates. Using a multilevel generalized linear mixed effects model, we found that individuals who newly completed a primary vaccine series had an additional increase of 1.93 (95 % CI: 0.27–3.59) contacts compared to individuals who remained unvaccinated. Using observed contact rates to estimate transmission, we found that observed increases in contact rates were not fully offset by vaccine protection against infection, but transmission was still maintained below levels without distancing and vaccination despite clusters of individuals with high contact and no vaccination.</div></div><div><h3>Conclusion</h3><div>We estimated changes in contact rates following vaccination and inferred the joint effect of changes in vaccination and contacts on population-level transmission, finding that observed increases in contact rates were not fully offset by vaccine effects. Our work highlights the potential utility of ongoing longitudinal monitoring of contact patterns during epidemics.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100827"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880911","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":"Estimating nosocomial transmission of micro-organisms in hospital settings using patient records and culture data","authors":"Jaime Cascante Vega ,&nbsp;Rami Yaari ,&nbsp;Tal Robin ,&nbsp;Lingsheng Wen ,&nbsp;Jason Zucker ,&nbsp;Anne-Catrin Uhlemann ,&nbsp;Sen Pei ,&nbsp;Jeffrey Shaman","doi":"10.1016/j.epidem.2025.100817","DOIUrl":"10.1016/j.epidem.2025.100817","url":null,"abstract":"<div><div>Pathogenic bacteria are a major threat to patient health in hospitals. Here we leverage electronic health records from a major New York City hospital system collected during 2020–2021 to support simulation inference of nosocomial transmission and pathogenic bacteria detection using an agent-based model (ABM). The ABM uses these data to inform simulation of importation from the community, nosocomial transmission, and patient spontaneous decolonization of bacteria. We additionally use patient clinical culture results to inform an observational model of detection of the pathogenic bacteria. The model is coupled with a Bayesian inference algorithm, an iterated ensemble adjustment Kalman filter, to estimate the likelihood of detection upon testing and nosocomial transmission rates. We evaluate parameter identifiability for this model-inference system and find that the system is able to estimate modelled nosocomial transmission and effective sensitivity upon clinical culture testing. We apply the framework to estimate both quantities for seven prevalent bacterial pathogens: <em>Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus</em> (both sensitive, MSSA, and resistant, MRSA, phenotypes), <em>Enterococcus faecium</em> and <em>Enterococcus faecalis</em>. We estimate that nosocomial transmission for <em>E. coli</em> is negligible<em>.</em> While bacterial pathogens have different importation rates, nosocomial transmission rates were similar among organisms, except <em>E. coli</em>. We also find that estimated likelihoods of detection are similar for all pathogens. This work highlights how fine-scale patient data can support inference of the epidemiological properties of micro-organisms and how hospital traffic and patient contact determine epidemiological features. Evaluation of the transmission potential for different pathogens could ultimately support the development of hospital control measures, as well as the design of surveillance strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100817"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transmission models of respiratory infections in carceral settings: A systematic review","authors":"Sara N. Levintow ,&nbsp;Molly Remch ,&nbsp;Emily P. Jones ,&nbsp;Justin Lessler ,&nbsp;Jessie K. Edwards ,&nbsp;Lauren Brinkley-Rubinstein ,&nbsp;Dana K. Rice ,&nbsp;David L. Rosen ,&nbsp;Kimberly A. Powers","doi":"10.1016/j.epidem.2024.100809","DOIUrl":"10.1016/j.epidem.2024.100809","url":null,"abstract":"<div><h3>Background</h3><div>The prevention and control of infectious disease outbreaks in carceral settings face unique challenges. Transmission modeling is a powerful tool for understanding and addressing these challenges, but reviews of modeling work in this context pre-date the proliferation of outbreaks in jails and prisons during the SARS-CoV-2 pandemic. We conducted a systematic review of studies using transmission models of respiratory infections in carceral settings before and during the pandemic.</div></div><div><h3>Methods</h3><div>We searched PubMed, Embase, Scopus, CINAHL, and PsycInfo to identify studies published between 1970 and 2024 that modeled transmission of respiratory infectious diseases in carceral settings. We extracted information on the diseases, populations, and settings modeled; approaches used for parameterizing models and simulating transmission; outcomes of interest and techniques for model calibration, validation, and sensitivity analyses; and types, impacts, and ethical aspects of modeled interventions.</div></div><div><h3>Results</h3><div>Forty-six studies met eligibility criteria, with transmission dynamics of tuberculosis modeled in 24 (52 %), SARS-CoV-2 in 20 (43 %), influenza in one (2 %), and varicella-zoster virus in one (2 %). Carceral facilities in the United States were the most common focus (15, 33 %), followed by Brazil (8, 17 %). Most studies (36, 80 %) used compartmental models (vs. individual- or agent-based). Tuberculosis studies typically modeled transmission within a single facility, while most SARS-CoV-2 studies simulated transmission in multiple places, including between carceral and community settings. Half of studies fit models to epidemiological data; three validated model predictions. Models were used to estimate past or potential future intervention impacts in 32 (70 %) studies, forecast the status quo (without changing conditions) in six (13 %), and examine only theoretical aspects of transmission in eight (17 %). Interventions commonly involved testing and treatment, quarantine and isolation, and/or facility ventilation. Modeled interventions substantially reduced transmission, but some were not well-defined or did not consider ethical issues.</div></div><div><h3>Conclusion</h3><div>The pandemic prompted urgent attention to transmission dynamics in jails and prisons, but there has been little modeling of respiratory infections other than SARS-CoV-2 and tuberculosis. Increased attention to calibration, validation, and the practical and ethical aspects of intervention implementation could improve translation of model estimates into tangible benefits for the highly vulnerable populations in carceral settings.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100809"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced testing can substantially improve defense against several types of respiratory virus pandemic","authors":"James Petrie ,&nbsp;James A. Hay ,&nbsp;Oraya Srimokla ,&nbsp;Jasmina Panovska-Griffiths ,&nbsp;Charles Whittaker ,&nbsp;Joanna Masel","doi":"10.1016/j.epidem.2024.100812","DOIUrl":"10.1016/j.epidem.2024.100812","url":null,"abstract":"<div><div>Mass testing to identify and isolate infected individuals is a promising approach for reducing harm from the next acute respiratory virus pandemic. It offers the prospect of averting hospitalizations and deaths whilst avoiding the need for indiscriminate social distancing measures. To understand scenarios where mass testing might or might not be a viable intervention, here we modeled how effectiveness depends both on characteristics of the pathogen (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, time to peak viral load) and on the testing strategy (limit of detection, testing frequency, test turnaround time, adherence). We base time-dependent test sensitivity and time-dependent infectiousness on an underlying viral load trajectory model. We show that given moderately high public adherence, frequent testing can prevent as many transmissions as more costly interventions such as school or business closures. With very high adherence and fast, frequent, and sensitive testing, we show that most respiratory virus pandemics could be controlled with mass testing alone.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100812"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative forecasting of influenza-like illness in Italy: The Influcast experience","authors":"Stefania Fiandrino ,&nbsp;Andrea Bizzotto ,&nbsp;Giorgio Guzzetta ,&nbsp;Stefano Merler ,&nbsp;Federico Baldo ,&nbsp;Eugenio Valdano ,&nbsp;Alberto Mateo Urdiales ,&nbsp;Antonino Bella ,&nbsp;Francesco Celino ,&nbsp;Lorenzo Zino ,&nbsp;Alessandro Rizzo ,&nbsp;Yuhan Li ,&nbsp;Nicola Perra ,&nbsp;Corrado Gioannini ,&nbsp;Paolo Milano ,&nbsp;Daniela Paolotti ,&nbsp;Marco Quaggiotto ,&nbsp;Luca Rossi ,&nbsp;Ivan Vismara ,&nbsp;Alessandro Vespignani ,&nbsp;Nicolò Gozzi","doi":"10.1016/j.epidem.2025.100819","DOIUrl":"10.1016/j.epidem.2025.100819","url":null,"abstract":"<div><div>Collaborative hubs that integrate multiple teams to generate ensemble projections and forecasts for shared targets are now regarded as state-of-the-art in epidemic predictive modeling. In this paper, we introduce Influcast, Italy’s first epidemic forecasting hub for influenza-like illness. During the 2023/2024 winter season, Influcast provided 20 rounds of forecasts, involving five teams and eight models to predict influenza-like illness incidence up to four weeks in advance at the national and regional administrative level. The individual forecasts were synthesized into an ensemble and benchmarked against a baseline model. Across all models, the ensemble most frequently ranks among the top performers at the national level considering different metrics and forecasting rounds. Additionally, the ensemble outperforms the baseline and most individual models across all regions. Despite a decline in absolute performance over longer horizons, the ensemble model outperformed the baseline in all considered horizons. These findings show the importance of multimodel forecasting hubs in producing reliable short-term influenza-like illnesses forecasts that can inform public health preparedness and mitigation strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100819"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flusion: Integrating multiple data sources for accurate influenza predictions","authors":"Evan L. Ray ,&nbsp;Yijin Wang ,&nbsp;Russell D. Wolfinger ,&nbsp;Nicholas G. Reich","doi":"10.1016/j.epidem.2024.100810","DOIUrl":"10.1016/j.epidem.2024.100810","url":null,"abstract":"<div><div>Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the motivation that accurate probabilistic forecasts could improve situational awareness and yield more effective public health actions. Starting with the 2021/22 influenza season, the forecasting targets for this challenge have been based on hospital admissions reported in the CDC’s National Healthcare Safety Network (NHSN) surveillance system. Reporting of influenza hospital admissions through NHSN began within the last few years, and as such only a limited amount of historical data are available for this target signal. To produce forecasts in the presence of limited data for the target surveillance system, we augmented these data with two signals that have a longer historical record: 1) ILI+, which estimates the proportion of outpatient doctor visits where the patient has influenza; and 2) rates of laboratory-confirmed influenza hospitalizations at a selected set of healthcare facilities. Our model, Flusion, is an ensemble model that combines two machine learning models using gradient boosting for quantile regression based on different feature sets with a Bayesian autoregressive model. The gradient boosting models were trained on all three data signals, while the autoregressive model was trained on only data for the target surveillance signal, NHSN admissions; all three models were trained jointly on data for multiple locations. In each week of the influenza season, these models produced quantiles of a predictive distribution of influenza hospital admissions in each state for the current week and the following three weeks; the ensemble prediction was computed by averaging these quantile predictions. Flusion emerged as the top-performing model in the CDC’s influenza prediction challenge for the 2023/24 season. In this article we investigate the factors contributing to Flusion’s success, and we find that its strong performance was primarily driven by the use of a gradient boosting model that was trained jointly on data from multiple surveillance signals and multiple locations. These results indicate the value of sharing information across multiple locations and surveillance signals, especially when doing so adds to the pool of available training data.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100810"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}