Epidemics最新文献

{"title":"Short term forecast of new daily pandemic hospitalizations: A time series model for a single hospital","authors":"Lieke Fleur Heupink ,&nbsp;Espen Rostrup Nakstad ,&nbsp;Hilde Lurås ,&nbsp;Pål Wiik ,&nbsp;Kristine Lippestad ,&nbsp;Fred Espen Benth ,&nbsp;Jūratė Šaltytė Benth","doi":"10.1016/j.epidem.2026.100894","DOIUrl":"10.1016/j.epidem.2026.100894","url":null,"abstract":"<div><div>Reliable hospital admission can aid contingency planning during pandemics. While some studies have developed models for predicting new hospitalizations, most focus on data at the regional or national level. During health crises, a prediction model tailored to a single hospital is essential, as models based on regional data may fail to account for the heterogeneity within the local population in terms of demographics, morbidity, and other relevant characteristics. Our study addresses this gap by presenting an approach for short-term forecasting of daily pandemic admissions to a single hospital. We develop a time series model using COVID-19 admission data during the Alpha wave to one Norwegian hospital with a highly heterogeneous catchment area that includes both urban and rural areas. Previous hospitalizations are included as predictors, along with the local reproduction number (ℜ-number) to capture pandemic trends. To account for demographic differences in admission rates, we employ group-based modelling to divide the catchment area into sub-areas. Forecasts generated from sub-area models are then merged and compared with the forecasts from a model for the entire catchment area. The model’s forecasting ability is tested on the Delta wave. The merged model outperforms the total model on the Alpha wave, and both surpass the ARIMA benchmark. On the out of sample Delta wave, the total model performs better overall. While the model overpredicts admissions at the beginning of the Delta wave and the prediction intervals are somewhat conservative, it demonstrates potential for reliably forecasting new daily pandemic admissions. Continuous model adaption will however be necessary as the pandemic evolves.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100894"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173556","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":"Fast and trustworthy nowcasting of dengue fever: A case study using attention-based probabilistic neural networks in São Paulo, Brazil","authors":"Silas Koemen ,&nbsp;Nuno R. Faria ,&nbsp;Leonardo S. Bastos ,&nbsp;Oliver Ratmann ,&nbsp;André Victor Ribeiro Amaral ,&nbsp;on behalf of the Machine Learning & Global Health Network","doi":"10.1016/j.epidem.2025.100880","DOIUrl":"10.1016/j.epidem.2025.100880","url":null,"abstract":"<div><div>Nowcasting methods are crucial in infectious disease surveillance, as reporting delays often lead to underestimation of recent incidence and can impair timely public health decision-making. Accurate real-time estimates of case counts are essential for resource allocation, policy responses, and communication with the public. In this paper, we propose a novel probabilistic neural network (PNN) architecture, named NowcastPNN, to estimate occurred-but-not-yet-reported cases of infectious diseases, demonstrated here using dengue fever incidence in São Paulo, Brazil. The proposed model combines statistical modelling of the true number of cases, assuming a Negative Binomial (NB) distribution, with recent advances in machine learning and deep learning, such as the attention mechanism. Uncertainty intervals are obtained by sampling from the predicted NB distribution and using Monte Carlo (MC) Dropout. Using proper scoring rules for the prediction intervals, NowcastPNN achieves nearly a 30% reduction in losses compared to the second-best model among other state-of-the-art approaches. While our model requires a large training dataset (equivalent to two to four years of incidence counts) to outperform benchmarks, it is computationally cheap and outperforms alternative methods even with significantly fewer observations as input. These features make the NowcastPNN model a promising tool for nowcasting in epidemiological surveillance of arboviral threats and other domains involving right-truncated data.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100880"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760662","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 influenza transmission parameters: Comparing two study designs, 2023–2024","authors":"Jessica E. Biddle ,&nbsp;Stacey House ,&nbsp;Jennie H. Kwon ,&nbsp;Rachel M. Presti ,&nbsp;Stephanie A. Fritz ,&nbsp;Tara Curley ,&nbsp;Son H. McLaren ,&nbsp;Melissa S. Stockwell ,&nbsp;Jonathan Schmitz ,&nbsp;H. Keipp Talbot ,&nbsp;Carlos G. Grijalva ,&nbsp;Elie A. Saade ,&nbsp;Zainab Albar ,&nbsp;Vel Murugan ,&nbsp;Rick A. Cruz ,&nbsp;Emily T. Martin ,&nbsp;Ivana A. Vaughn ,&nbsp;Karen J. Wernli ,&nbsp;Brianna M. Wickersham ,&nbsp;Richard K. Zimmerman ,&nbsp;Olivia L. Williams","doi":"10.1016/j.epidem.2026.100888","DOIUrl":"10.1016/j.epidem.2026.100888","url":null,"abstract":"<div><div>Household studies play a critical role in estimating influenza transmission parameters, which are essential for real-time modeling of epidemic and pandemic dynamics to inform influenza control strategies. We compared two approaches for estimating household influenza transmission parameters from multisite studies conducted in the United States during the 2023–2024 influenza season: interviewing index cases about illnesses among household contacts (n = 1537 contacts) and prospective enrollment of index cases and their household contacts with systematic, daily symptom assessment and testing (n = 857 contacts). We compared the detection of symptomatic illness, influenza-like illness (ILI; fever and either cough or sore throat), influenza virus infection, and estimated serial illness onset intervals among household contacts across studies. Symptomatic illness episodes among household contacts were identified in 40 % of contacts by index case interview compared to 59 % of contacts from individual daily follow-up. Reports of ILI were more comparable between platforms (20 % vs. 26 % respectively). Index case interviews identified 12 % of household contacts with positive influenza tests while systematic, daily testing in the individual daily follow-up platform identified influenza infection among 44 % of household contacts. Both platforms yielded a median serial interval of 4 days. While index case interviews offer rapid, resource-efficient data collection and can inform epidemiological outcomes such as age-related risks and serial intervals, they substantially underestimate laboratory-confirmed influenza cases compared to systematic daily follow-up. These findings highlight the importance of study design in accurately capturing transmission dynamics and underscore the need for systematic laboratory testing to inform public health responses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100888"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978271","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":"Impact of COVID-19 on the transmission dynamics of HFMD associated enterovirus serotypes in Japan: A modelling study of surveillance data","authors":"Xuemei Yan,&nbsp;Nicholas C. Grassly ,&nbsp;Margarita Pons-Salort","doi":"10.1016/j.epidem.2026.100899","DOIUrl":"10.1016/j.epidem.2026.100899","url":null,"abstract":"<div><div>A notable decline in incidence of hand, foot, and mouth disease (HFMD) was observed globally since the beginning of the COVID-19 pandemic, indicating a change in the transmission dynamics of enteroviruses (EVs) causing HFMD. This study estimates the impact of COVID-19 non-pharmaceutical interventions (NPIs) on the transmission of EVs in Japan. Japan has a well-established sentinel surveillance system for enteroviruses and HFMD. A susceptible-infected-recovered stochastic transmission model was fitted to the time-series of weekly reported number of HFMD enterovirus A71 (EV-A71), coxsackievirus A16 (CVA16), and CVA6 cases from January 2005 to December 2024, to estimate the impact of government containment interventions during the COVID-19 pandemic. Compared to pre-pandemic levels, the transmission rate decreased by 7.86 % (95 % Credible Interval (CrI): 6.31 %, 10.3 %) for EV-A71, 12.0 % (10.3 %, 14.1 %) for CVA16, and 20.1 % (16.3 %, 24.0 %) for CVA6 on average from January 2020 to December 2022. The reduction in transmission rate during this period correlated with the COVID-19 Stringency Index, such that 49.3 %, 19.9 % and 25.5 % of the reduction for each serotype respectively was explained by the index, with the biggest reductions occurring during “State of Emergency” periods. Enterovirus serotypes with higher transmissibility (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) and therefore, a younger mean age at infection, were estimated to experience a bigger reduction in transmission that was more strongly associated with COVID-19 non-pharmaceutical interventions captured by the Stringency Index. This has implications for the impact of NPIs on other viral pathogens.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100899"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147285874","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":"Enhancing Influenza-Like Illness forecasting: An ensemble approach combining mathematical and deep learning models amidst the COVID-19 pandemic","authors":"Ganghyun Yoon ,&nbsp;Amanda Bleichrodt ,&nbsp;Gerardo Chowell ,&nbsp;Sunmi Lee","doi":"10.1016/j.epidem.2026.100901","DOIUrl":"10.1016/j.epidem.2026.100901","url":null,"abstract":"<div><h3>Background:</h3><div>Timely and accurate short-term forecasting of Influenza-Like Illness (ILI) is crucial for guiding outbreak response, optimizing healthcare resource allocation, and informing public health interventions. The COVID-19 pandemic, which disrupted seasonal ILI dynamics due to widespread nonpharmaceutical interventions (NPI), underscored the urgent need for adaptive and reliable forecasting frameworks.</div></div><div><h3>Method:</h3><div>In this study, we present a novel ensemble modeling approach that combines a mechanistic n-subepidemic model with a Monte Carlo Dropout Long Short-Term Memory (LSTM) neural network to improve age-specific ILI forecasting performance in South Korea. By capturing both the structured dynamics of disease spread and nonlinear temporal dependencies, our ensemble method adapts to pandemic-altered transmission patterns while offering robust uncertainty quantification. Age-stratified forecasting allows the framework to capture heterogeneity in vulnerability and transmission across demographic groups, providing more targeted insights for policy and planning.</div></div><div><h3>Result:</h3><div>We evaluated forecasting performance across four epidemic waves using the Weighted Interval Score (WIS), Mean Absolute Error (MAE), consistently finding that the ensemble models outperformed individual approaches.</div></div><div><h3>Conclusion:</h3><div>These findings underscore the power of hybrid forecasting approaches to improve epidemic preparedness and response, providing a flexible data-driven framework that can evolve with changing transmission dynamics and extend to other emerging infectious threats.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100901"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147348617","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":"Maternal immunity drives age-related patterns of RSV disease","authors":"Clara Brigitta ,&nbsp;David Hodgson ,&nbsp;Graham F. Medley ,&nbsp;Rosalind M. Eggo","doi":"10.1016/j.epidem.2026.100903","DOIUrl":"10.1016/j.epidem.2026.100903","url":null,"abstract":"<div><div>Respiratory Syncytial Virus (RSV) is a leading cause of respiratory illness in young children. While maternal antibodies offer temporary protection in early infancy, their interaction with age-dependent disease risk remains poorly quantified. The COVID-19 pandemic, which disrupted RSV transmission, provides a unique opportunity to explore these dynamics and the potential impact of maternal vaccination. We developed a compartmental model of childhood RSV disease incorporating maternal infection history, maternally-derived immunity, waning protection in infants, and age-dependent disease risk. Calibrated to Scottish surveillance data (2016–2024), the model estimated non-linear functions for maternal immunity and RSV risk by age, and projected burden from 2024 to 2028 under vaccination and no-vaccination scenarios. Following pandemic-related disruption, RSV burden shifted to older children due to delayed primary exposure. Infants who missed their typical first RSV season in 2020 experienced higher disease rates at later ages, in subsequent seasons. Maternal immunity conferred protection only when infection occurred in late pregnancy, with infant protection waning to negligible levels by six months of age. Maternal vaccination at current coverage rates was projected to reduce RSV disease cases in infants ≤ 6 months by 15.3% (95% CI: 11.9–18.5%) in 2024–25 and 18.4% (95% CI: 12.8–23.6%) in 2025–26. Our findings highlight the role of the timing and immunological mechanisms of maternally-derived immunity in shaping RSV dynamics in young children and demonstrate how disruptions—whether through pandemic-related changes or maternal vaccination—can alter age-specific disease patterns.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100903"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373234","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":"Spatiotemporal transmission of influenza in the US during the 2022/23 season","authors":"Alexia Couture ,&nbsp;Matthew Biggerstaff ,&nbsp;Michael Sheppard ,&nbsp;Alicia Budd ,&nbsp;Aaron Kite-Powell ,&nbsp;Sinead E. Morris","doi":"10.1016/j.epidem.2026.100896","DOIUrl":"10.1016/j.epidem.2026.100896","url":null,"abstract":"<div><div>Understanding the spatiotemporal dynamics of seasonal influenza spread across the United States (US) is crucial for informed public health planning. We explored patterns of influenza transmission during the 2022/23 season in the US and used a mathematical model to infer potential drivers and underlying mechanisms. Leveraging emergency department visit data, we first estimated the timing of influenza onset for the 2022/23 season at the Health Service Area (HSA) level. We then combined the estimated onset times in a gravity-based mechanistic model with covariates that could be associated with influenza spread, including demographics, climate, mobility, and school opening information. We compared multiple models to find the best fit to the onset times, infer factors driving transmission, and identify potential geographic hubs that were most influential in generating early chains of transmission. From the estimated onset times, we found a spatiotemporal pattern that was characterized by early transmission in southern and southeastern HSAs, followed by localized spread to other regions. The best-fit model included absolute humidity and local transmission modulated by school opening times, with four out of five potential hubs located in the southern US (two in Georgia and one each in North Carolina and Texas) and one in the Northwest (Washington). In conclusion, we found a regional pattern for the spatiotemporal spread of 2022/23 seasonal influenza and identified potential key drivers of this pattern. These findings, and similar studies from other influenza seasons, may improve our understanding of the spatiotemporal spread of influenza in the US.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100896"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146259889","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":"Combining an agent-based model with Gaussian process emulation to model the emergence of the SARS-CoV-2 Omicron variant in Norway","authors":"Alfonso Diz-Lois Palomares ,&nbsp;Birgitte Freiesleben de Blasio ,&nbsp;Louis Yat Hin Chan ,&nbsp;Francesco Di Ruscio ,&nbsp;Jørgen E. Midtbø","doi":"10.1016/j.epidem.2026.100895","DOIUrl":"10.1016/j.epidem.2026.100895","url":null,"abstract":"<div><div>Agent-based models (ABMs) are powerful for simulating detailed infectious disease dynamics, but they are computationally intensive, limiting parameter inference. We present a Bayesian history matching approach with Gaussian process emulation to efficiently calibrate an ABM of SARS-CoV-2 transmission in Norway, enabling inference of numerous parameters and reducing computational costs.</div><div>We apply this technique to model the emergence and subsequent winter wave of the Omicron BA.1/BA.2 variants in Norway from September 2021 to April 2022. By fitting age-specific hospitalisation data, we infer 15 key parameters, including age-specific susceptibility, variant transmissibility, and the infection-hospitalisation ratio. The calibrated model accurately reproduces epidemic curves and provides insights into Omicron’s spread.</div><div>We estimate that around 60% of the Norwegian population was infected with Omicron BA.1/BA.2 during the 2021/2022 winter wave. The model suggests the variant circulated in Norway before its detection in South Africa in late November 2021. Our findings indicate that Omicron’s transmission advantage over Delta was primarily due to immune evasion rather than a substantial increase in intrinsic transmissibility. Counterfactual scenarios reveal the critical role of the November 2021 booster vaccine rollout in mitigating the ensuing winter wave, indicating near-optimal timing for balancing immunisation and waning. Our estimates also suggest that the December 2021 interventions had limited impact beyond altering general mobility.</div><div>This study demonstrates the power of combining ABMs with advanced calibration to derive detailed epidemiological insights from limited data. Efficiently fitting parameters enables comprehensive epidemic modelling. Emulator-based calibration greatly enhances ABMs’ utility for retrospective analysis and real-time decision support during outbreaks, providing a valuable tool for public health planning and response.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100895"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147372681","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 robust compartmental modeling framework for infectious disease monitoring and analysis via fractional differential equations","authors":"Farrukh A. Chishtie ,&nbsp;John Drozd ,&nbsp;X. Li ,&nbsp;A. Benterki ,&nbsp;Sree R. Valluri","doi":"10.1016/j.epidem.2026.100887","DOIUrl":"10.1016/j.epidem.2026.100887","url":null,"abstract":"<div><div>This study presents a comprehensive framework for infectious disease monitoring using fractional differential equations, specifically developing the SEIQRDP (Susceptible, Exposed, Infected, Quarantined, Recovered, Deceased, Protected) model. Traditional compartmental models are extended by incorporating fractional calculus, with orders <span><math><mrow><mi>α</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>2</mn><mo>]</mo></mrow></mrow></math></span>, which provides enhanced flexibility in capturing memory effects and non-local behaviors inherent in disease transmission dynamics. The framework demonstrates improved accuracy when fitted to Canadian COVID-19 data compared to classical integer-order models, with Wave 1 achieving 22.1% improvement (95% CI: 17.4–26.8%) and Wave 2 achieving 6.2% improvement (95% CI: 3.1–9.3%) in predictive accuracy (average <span><math><mo>∼</mo></math></span>14%). Fractional orders both below and above unity yield superior fits to empirical data depending on epidemic phase, successfully capturing multi-wave dynamics across different pandemic phases. The model incorporates time-dependent parameters to account for varying intervention strategies. Rigorous mathematical analysis including existence, uniqueness, and stability of solutions is provided alongside comprehensive sensitivity analysis. Out-of-sample validation using rolling-origin cross-validation demonstrates robust forecasting performance across 7-, 14-, and 21-day horizons. This research provides public health authorities with an evidence-based tool for epidemic modeling, with proposed extensions for AI-enhanced surveillance, interoperability standards, and Long COVID monitoring discussed as future research directions.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100887"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020351","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":"UnMuted: Defining SARS-CoV-2 lineages according to temporally consistent mutation clusters in wastewater samples","authors":"Devan Becker","doi":"10.1016/j.epidem.2025.100876","DOIUrl":"10.1016/j.epidem.2025.100876","url":null,"abstract":"<div><div>SARS-CoV-2 lineages are defined according to placement in a phylogenetic tree, but approximated by a list of mutations based on sequences collected from clinical sampling. Wastewater lineage abundance is generally found under the assumption that the mutation frequency is approximately equal to the sum of the abundances of the lineages to which it belongs. By leveraging numerous samples collected over time, I am able to estimate the temporal trends of the abundance of lineages as well as the definitions of those lineages. This is accomplished by assuming that collections of mutations that appear together over time can be used to define lineages.</div><div>Three main models are considered: One that does not imposes a temporal structure, one that includes an explicit temporal component but allows for missing lineages, and one with an explicit temporal component that attempts to estimate all lineages. It is found that the temporal trend of estimated lineage definitions approximately corresponds to the trend of lineage definitions determined by clinical samples, despite having no information from clinical samples.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100876"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791945","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}