Epidemics最新文献

筛选
英文 中文
Projecting the population-level impact of norovirus vaccines 预测诺如病毒疫苗对人群的影响
IF 3 3区 医学
Epidemics Pub Date : 2025-07-18 DOI: 10.1016/j.epidem.2025.100842
Katia Koelle , Brooke Lappe , Benjamin A. Lopman , Max S.Y. Lau , Emma Viscidi , Katherine B. Carlson
{"title":"Projecting the population-level impact of norovirus vaccines","authors":"Katia Koelle ,&nbsp;Brooke Lappe ,&nbsp;Benjamin A. Lopman ,&nbsp;Max S.Y. Lau ,&nbsp;Emma Viscidi ,&nbsp;Katherine B. Carlson","doi":"10.1016/j.epidem.2025.100842","DOIUrl":"10.1016/j.epidem.2025.100842","url":null,"abstract":"<div><div>Norovirus diversity has major implications for vaccine design. The number of circulating genogroups and genotypes, and the way this viral diversity interacts at the population level, will factor into how many and which genotypes should be included in an effective vaccine. Here, we develop an age-stratified, multi-strain model for norovirus to project potential population-level impacts of different vaccine formulations on genotype-specific and overall annual attack rates. Our model assumes that vaccination impacts susceptibility to infection but not infectiousness or the risk of developing disease. We parameterize the baseline model (without vaccination) based on literature estimates and the ability to recover observed epidemiological patterns. We then simulate this model under seven different potential vaccine formulations, initially assuming only pediatric vaccination. While we find that increases in coverage result in declines in annual norovirus attack rates for all formulations considered, we also find that vaccine formulations that include genotype GII.4 would be most effective at lowering overall norovirus attack rates. Inclusion of additional genotypes in a vaccine would further lower attack rates but more incrementally, with the addition of GI.3, GII.2, GII.3, and GII.6 together having a similar impact to that of GII.4 alone on reducing overall norovirus incidence. We further find that transient dynamics are expected for 10-20 years following roll-out with any pediatric vaccine. During this time, there may be unanticipated changes in genotype circulation patterns, although long-term increases in non-vaccine genotype attack rates above baseline levels are not expected. Finally, we anticipate that annual vaccination of older-aged individuals with a GII.4-containing vaccine can, under certain conditions but not others, provide appreciable direct benefits to individuals in this age group beyond what pediatric vaccination affords. Together, our results indicate that there is a clear population-level benefit of primary pediatric vaccination with a GII.4-inclusive norovirus vaccine plus incremental value of other genotypes, with additional direct benefits of annual vaccination to older adults provided that vaccination results in a considerable (multi-month) duration of broadly protective immunity to infection. More empirical studies are needed to validate the structure of the model and refine its parameterization, both of which affect projections of vaccine impact.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100842"},"PeriodicalIF":3.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672569","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}
引用次数: 0
Robust phylodynamic inference and model specification for HIV transmission dynamics HIV传播动力学的鲁棒系统动力学推断和模型规范
IF 3 3区 医学
Epidemics Pub Date : 2025-07-16 DOI: 10.1016/j.epidem.2025.100846
Fabrícia F. Nascimento , Sanjay R. Mehta , Susan J. Little , Erik M. Volz
{"title":"Robust phylodynamic inference and model specification for HIV transmission dynamics","authors":"Fabrícia F. Nascimento ,&nbsp;Sanjay R. Mehta ,&nbsp;Susan J. Little ,&nbsp;Erik M. Volz","doi":"10.1016/j.epidem.2025.100846","DOIUrl":"10.1016/j.epidem.2025.100846","url":null,"abstract":"<div><div>The robustness and statistical efficiency of phylodynamic models have been tested by many investigators. However, little attention has been given to model specification and inductive bias that can occur if the model is misspecified or provides an overly simplistic representation of the evolutionary process. Here, we carried out a study involving the simulation of HIV epidemics using a complex model and calibrated to men who have sex with men from San Diego, USA. We then used this epidemic trajectory to simulate genealogies, sequence alignments equivalent to HIV partial <em>pol</em> gene and the complete genome. We proceeded to estimate migration rates using a simplistic representation of the epidemiological model by testing model-based phylodynamics and phylogeographic methods. We observed that even though there were some biases on the estimates using a simplistic representation of the epidemiological model, we were still able to estimate the migration rates depending on the method and sample size used in the analyses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100846"},"PeriodicalIF":3.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655353","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}
引用次数: 0
Improving policy-oriented agent-based modeling with history matching: A case study 使用历史匹配改进面向策略的基于代理的建模:一个案例研究
IF 3 3区 医学
Epidemics Pub Date : 2025-07-16 DOI: 10.1016/j.epidem.2025.100845
David O’Gara , Cliff C. Kerr , Daniel J. Klein , Mickaël Binois , Roman Garnett , Ross A. Hammond
{"title":"Improving policy-oriented agent-based modeling with history matching: A case study","authors":"David O’Gara ,&nbsp;Cliff C. Kerr ,&nbsp;Daniel J. Klein ,&nbsp;Mickaël Binois ,&nbsp;Roman Garnett ,&nbsp;Ross A. Hammond","doi":"10.1016/j.epidem.2025.100845","DOIUrl":"10.1016/j.epidem.2025.100845","url":null,"abstract":"<div><div>Advances in computing power and data availability have led to growing sophistication in mechanistic mathematical models of social dynamics. Increasingly these models are used to inform real-world policy decision-making, often with significant time sensitivity. One such modeling approach is agent-based modeling, which offers particular strengths for capturing spatial and behavioral realism, and for <em>in-silico</em> experiments (varying input parameters and assumptions to explore their downstream impact on key outcomes). To be useful in the real-world, these models must be able to qualitatively or quantitatively capture observed empirical phenomena, forming the starting point for subsequent experimentation. One recent example is the COVID-19 pandemic, where epidemiological agent-based models informed policy and response planning worldwide. Throughout, modeling teams often had to spend valuable time and effort aligning their models to data, also known as calibration. Since many agent-based models are computationally intensive, the calibration process constrains the questions and scenarios policymakers may explore in time-sensitive situations. In this paper, we combine history matching, heteroskedastic Gaussian process modeling, and approximate Bayesian computation to address this bottleneck, substantially increasing efficiency and thus widening the range of utility for policy models. We illustrate our approach with a case study using a previously published and widely used epidemiological model, the Covasim model.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100845"},"PeriodicalIF":3.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672567","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}
引用次数: 0
Learning associations of COVID-19 hospitalizations with wastewater viral signals by Markov modulated models 利用马尔可夫调制模型学习废水病毒信号与COVID-19住院的关联
IF 3 3区 医学
Epidemics Pub Date : 2025-07-14 DOI: 10.1016/j.epidem.2025.100840
K. Ken Peng , Charmaine B. Dean , X. Joan Hu , Robert Delatolla
{"title":"Learning associations of COVID-19 hospitalizations with wastewater viral signals by Markov modulated models","authors":"K. Ken Peng ,&nbsp;Charmaine B. Dean ,&nbsp;X. Joan Hu ,&nbsp;Robert Delatolla","doi":"10.1016/j.epidem.2025.100840","DOIUrl":"10.1016/j.epidem.2025.100840","url":null,"abstract":"<div><div>Recent research highlights a strong correlation between COVID-19 hospitalizations and wastewater viral signals. Increases in wastewater viral signals may be early warnings of increases in hospital admissions. That indicates a promising opportunity to assess and predict the burden of infectious diseases and has driven the widespread adoption and development of wastewater monitoring tools by public health organizations. Previous studies utilize distributed lag models to explore associations of COVID-19 hospitalizations with lagged SARS-CoV-2 wastewater viral signals. However, the conventional distributed lag models assume the duration time of the lag to be fixed, which is not always plausible. This paper presents Markov-modulated models with distributed lasting time, treating the duration of the lag as a random variable defined by a hidden process. We evaluate exposure effects over the duration time and estimate the distribution of the lasting time using the wastewater data and COVID-19 hospitalization records from Ottawa, Canada during June 2020 to November 2022. The different COVID-19 pandemic waves are accommodated in the statistical learning. Moreover, two strategies for comparing the associations over different time intervals are exemplified using the Ottawa data. Of note, the proposed Markov modulated models, an extension of distributed lag models, are potentially applicable to many different problems where the lag time is not fixed.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100840"},"PeriodicalIF":3.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666047","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}
引用次数: 0
Modeling transmission dynamics and socio-economic determinants of scarlet fever in Chengdu, China: An integrated SEIAR and machine learning approach 中国成都猩红热传播动态和社会经济决定因素建模:综合SEIAR和机器学习方法
IF 3 3区 医学
Epidemics Pub Date : 2025-07-09 DOI: 10.1016/j.epidem.2025.100844
Tianlong Yang , Xunbo Du , Junfan Li , Tin Zhang , Yao Wang , Liang Wang
{"title":"Modeling transmission dynamics and socio-economic determinants of scarlet fever in Chengdu, China: An integrated SEIAR and machine learning approach","authors":"Tianlong Yang ,&nbsp;Xunbo Du ,&nbsp;Junfan Li ,&nbsp;Tin Zhang ,&nbsp;Yao Wang ,&nbsp;Liang Wang","doi":"10.1016/j.epidem.2025.100844","DOIUrl":"10.1016/j.epidem.2025.100844","url":null,"abstract":"<div><h3>Background</h3><div>Scarlet fever (SF) is an acute infectious disease that poses a significant public health threat; however, its transmission dynamics, particularly the impact of asymptomatic carriers and socioeconomic determinants, remain unclear.</div></div><div><h3>Methods</h3><div>We developed a susceptible–exposed–infectious–asymptomatic–recovered (SEIAR) model that incorporates asymptomatic infections to estimate the time-varying reproduction number (<em>R</em><sub><em>t</em></sub>) for SF in Chengdu (2005–2019) using local epidemiological data. The model was evaluated using the coefficient of determination (<em>R</em>²), and sensitivity analysis confirmed its robustness. We further integrated Boruta, Extreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP) to systematically assess the influence of socioeconomic variables on <em>R</em><sub><em>t</em></sub>.</div></div><div><h3>Results</h3><div>Between 2005 and 2019, Chengdu reported 11,499 cases of SF, with an average incidence of 4.87 per 100,000. Two distinct seasonal peaks occurred in April–May and November–December, and incidence rates were notably lower during school holidays. The majority of cases affected children aged 3–7, with a male-to-female ratio of 1.59:1. In addition, core districts such as Wuhou and Xindu exhibited the highest incidence. The SEIAR model demonstrated strong predictive performance (overall <em>R</em>² = 0.831, <em>P</em> &lt; 0.001) and estimated a median <em>R</em><sub><em>t</em></sub> of 0.963; however, several regions exceeded this threshold, with <em>R</em><sub><em>t</em></sub> peaking approximately two months prior to incidence surges. Spatial analyses revealed significant clustering in central urban areas, and integrated socioeconomic analysis identified the one-child rate as the primary driver of <em>R</em><sub><em>t</em></sub>, followed by population density and healthcare facility density (<em>P</em> &lt; 0.01).</div></div><div><h3>Conclusion</h3><div>By integrating epidemiological data with socioeconomic factors, this study quantitatively elucidates the transmission characteristics of SF in Chengdu, providing data-driven support for monitoring and targeted intervention strategies in the absence of vaccination.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100844"},"PeriodicalIF":3.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597434","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}
引用次数: 0
Work absenteeism across economic activity sectors and its association with COVID-19-like illness prevalence in the Netherlands, 2020–2023 2020-2023年荷兰经济活动部门的缺勤情况及其与covid -19样疾病流行的关系
IF 3 3区 医学
Epidemics Pub Date : 2025-07-01 DOI: 10.1016/j.epidem.2025.100841
Hester Korthals Altes , Jan Van De Kassteele , Bram Wisse , Maria Xiridou , Albert Jan Van Hoek , Jacco Wallinga
{"title":"Work absenteeism across economic activity sectors and its association with COVID-19-like illness prevalence in the Netherlands, 2020–2023","authors":"Hester Korthals Altes ,&nbsp;Jan Van De Kassteele ,&nbsp;Bram Wisse ,&nbsp;Maria Xiridou ,&nbsp;Albert Jan Van Hoek ,&nbsp;Jacco Wallinga","doi":"10.1016/j.epidem.2025.100841","DOIUrl":"10.1016/j.epidem.2025.100841","url":null,"abstract":"<div><div>The monitoring of work absenteeism can inform pandemic decision making, besides the surveillance of disease end-points like mortality and intensive care bed occupancy. For instance, high disease prevalence accompanied by elevated levels of absenteeism in the healthcare sector will increase the strain on the health care system, and may necessitate adaptation of the control measures. This highlights the need to assess the association between COVID-19 disease prevalence and absenteeism in relevant economic sectors. We initiated the comprehensive monitoring and analysis of work absenteeism and developed an autoregressive time series model which combined COVID-19 prevalence as measured through syndromic surveillance, with absenteeism across various economic activity sectors in the Netherlands. The analysis was updated regularly and shared with policy makers. Overall, prevalence of COVID-19-like illnesses was the most important contributor to variation in absenteeism over the period November 2020-May 2023, with absenteeism rates varying markedly between activity sectors. Of the sectors well-covered by the absenteeism database, the Education and Logistics sectors showed the greatest contribution of a seasonal pattern independent of COVID-19 to absenteeism.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100841"},"PeriodicalIF":3.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572646","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}
引用次数: 0
Estimating the effective reproduction number from wastewater (Rt): A methods comparison 估计废水的有效再生数(Rt):一种方法的比较
IF 3 3区 医学
Epidemics Pub Date : 2025-06-18 DOI: 10.1016/j.epidem.2025.100839
Dustin T. Hill , Yifan Zhu , Christopher Dunham , E. Joe Moran , Yiquan Zhou , Mary B. Collins , Brittany L. Kmush , David A. Larsen
{"title":"Estimating the effective reproduction number from wastewater (Rt): A methods comparison","authors":"Dustin T. Hill ,&nbsp;Yifan Zhu ,&nbsp;Christopher Dunham ,&nbsp;E. Joe Moran ,&nbsp;Yiquan Zhou ,&nbsp;Mary B. Collins ,&nbsp;Brittany L. Kmush ,&nbsp;David A. Larsen","doi":"10.1016/j.epidem.2025.100839","DOIUrl":"10.1016/j.epidem.2025.100839","url":null,"abstract":"<div><h3>Background</h3><div>The effective reproduction number (R<sub>t</sub>) is a dynamic indicator of current disease spread risk. Wastewater measurements of viral concentrations are known to correlate with clinical measures of diseases and have been incorporated into methods for estimating the R<sub>t</sub>.</div></div><div><h3>Methods</h3><div>We review wastewater-based methods to estimate the R<sub>t</sub> for SARS-CoV-2 based on similarity to the reference case-based R<sub>t</sub>, ease of use, and computational requirements. Using wastewater data collected between August 1, 2022, and February 20, 2024, from 205 wastewater treatment plants across New York State, we fit eight wastewater R<sub>t</sub> models identified from the literature. Each model is compared to the R<sub>t</sub> estimated from case data for New York at the sewershed (wastewater treatment plant catchment area), county, and state levels.</div></div><div><h3>Results</h3><div>We find a high degree of similarity across all eight methods despite differences in model parameters and approach. Further, two methods based on the common measures of percent change and linear fit reproduced the R<sub>t</sub> from case data very well and a GLM accurately predicted case data. Model output varied between spatial scales with some models more closely estimating sewershed R<sub>t</sub> values than county R<sub>t</sub> values. Similarity to clinical models was also highly correlated with the proportion of the population served by sewer in the surveilled communities (r = 0.77).</div></div><div><h3>Conclusions</h3><div>While not all methods that estimate R<sub>t</sub> from wastewater produce the same results, they all provide a way to incorporate wastewater concentration data into epidemic modeling. Our results show that straightforward measures like the percent change can produce similar results of more complex models. Based on the results, researchers and public health officials can select the method that is best for their situation.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100839"},"PeriodicalIF":3.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338640","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}
引用次数: 0
Incident COVID-19 infections before Omicron in the U.S. 在美国欧米克隆之前发生的COVID-19感染事件
IF 3 3区 医学
Epidemics Pub Date : 2025-06-09 DOI: 10.1016/j.epidem.2025.100838
Rachel Lobay , Ajitesh Srivastava , Ryan J. Tibshirani , Daniel J. McDonald
{"title":"Incident COVID-19 infections before Omicron in the U.S.","authors":"Rachel Lobay ,&nbsp;Ajitesh Srivastava ,&nbsp;Ryan J. Tibshirani ,&nbsp;Daniel J. McDonald","doi":"10.1016/j.epidem.2025.100838","DOIUrl":"10.1016/j.epidem.2025.100838","url":null,"abstract":"<div><div>The timing and magnitude of COVID-19 infections are of interest to the public and to public health, but these are challenging to ascertain due to the volume of undetected asymptomatic cases and reporting delays. Accurate estimates of COVID-19 infections based on finalized data can improve understanding of the pandemic and provide more meaningful quantification of disease patterns and burden. Therefore, we retrospectively estimate daily incident infections for each U.S. state prior to Omicron. To this end, reported COVID-19 cases are deconvolved to their likely date of infection onset using delay distributions estimated from the CDC line list. Then, a novel serology-driven model is used to scale these deconvolved cases to account for the unreported infections. The resulting infection estimates incorporate variant-specific incubation periods, reinfections, and waning antigenic immunity. They clearly demonstrate that reported cases failed to reflect the full extent of disease burden in all states. Most notably, infections were severely underreported during the Delta wave, with an estimated reporting rate as low as 6.3% in New Jersey, 7.3% in Maryland, and 8.4% in Nevada. Moreover, in 44 states, fewer than 1/3 of infections eventually appeared as case reports, and there were sustained periods where surges in infections were virtually undetectable through reported cases. This pattern was clearly illustrated by North and South Dakota during the spring of 2021, as well as by several Northeastern states during the Delta wave of late summer that year. While reported cases offered a convenient proxy of disease burden, they failed to capture the full extent of infections and severely underestimated the true disease burden. Our retrospective analysis also estimates other important quantities for every state, including variant-specific deconvolved cases, time-varying case ascertainment ratios, as well as infection-hospitalization and infection-fatality ratios.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100838"},"PeriodicalIF":3.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289026","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}
引用次数: 0
Verifying infectious disease scenario planning for geographically diverse populations 核实地理上不同人群的传染病情景规划
IF 3 3区 医学
Epidemics Pub Date : 2025-06-06 DOI: 10.1016/j.epidem.2025.100833
Jessica R. Conrad , Paul W. Fenimore , Kelly R. Moran , Marisa C. Eisenberg
{"title":"Verifying infectious disease scenario planning for geographically diverse populations","authors":"Jessica R. Conrad ,&nbsp;Paul W. Fenimore ,&nbsp;Kelly R. Moran ,&nbsp;Marisa C. Eisenberg","doi":"10.1016/j.epidem.2025.100833","DOIUrl":"10.1016/j.epidem.2025.100833","url":null,"abstract":"<div><div>In the face of the COVID-19 pandemic, the literature saw a spike in publications for epidemic models, and a renewed interest in capturing contact networks and geographic movement of populations. There remains a general lack of consensus in the modeling community around best practices for spatiotemporal epi-modeling, specifically as it pertains to the infection rate formulation and the underlying contact or mixing model.</div><div>We mathematically verify several common modeling assumptions in the literature, to prove when certain choices can provide consistent results across different geographic resolutions, population densities and patterns, and mixing assumptions. The most common infection rate formulation, a computationally low cost <em>per capita</em> infection rate assumption, fails the consistency tests for heterogeneous populations and gravity-weighting assumptions. Future modeling efforts in spatiotemporal disease modeling should be wary of this limitation, particularly when working with more heterogeneous or sparse populations.</div><div>Our results provide guidance for testing that a model preserves desirable properties even when model inputs mask potential problems due to symmetry or homogeneity. We also provide a recipe for performing this type of verification, strengthening decision support tools.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100833"},"PeriodicalIF":3.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501024","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}
引用次数: 0
A Cluster-Aggregate-Pool (CAP) ensemble algorithm for improved forecast performance of influenza-like illness 提高流感样疾病预测性能的聚类-聚合-池(CAP)集成算法
IF 3 3区 医学
Epidemics Pub Date : 2025-06-03 DOI: 10.1016/j.epidem.2025.100832
Ningxi Wei , Xinze Zhou , Wei-Min Huang , Thomas McAndrew
{"title":"A Cluster-Aggregate-Pool (CAP) ensemble algorithm for improved forecast performance of influenza-like illness","authors":"Ningxi Wei ,&nbsp;Xinze Zhou ,&nbsp;Wei-Min Huang ,&nbsp;Thomas McAndrew","doi":"10.1016/j.epidem.2025.100832","DOIUrl":"10.1016/j.epidem.2025.100832","url":null,"abstract":"<div><div>Seasonal influenza causes on average 425,000 hospitalizations and 32,000 deaths per year in the United States. Forecasts of influenza-like illness (ILI) — a surrogate for the proportion of patients infected with influenza — support public health decision making. The goal of an ensemble forecast of ILI is to increase accuracy and calibration compared to individual forecasts and to provide a single, cohesive prediction of future influenza. However, an ensemble may be composed of models that produce similar forecasts, causing issues with ensemble forecast performance and non-identifiability. To improve upon the above issues we propose a novel Cluster-Aggregate-Pool or ‘CAP’ ensemble algorithm that first groups together individual forecasts into clusters, aggregates forecasts that belong to the same cluster into a single forecast (called a cluster forecast), and then pools together cluster forecasts via a linear pool. We evaluated this algorithm on a benchmark dataset of 7 seasons of ILI plus forecasts generated by 27 individual models as part of the FluSight project. When compared to a non-CAP approach, we find that a CAP ensemble improves calibration by approximately 10% while maintaining similar accuracy to non-CAP alternatives. In addition, our CAP algorithm (i) generalizes past ensemble work associated with influenza forecasting and introduces a framework for future ensemble work, (ii) automatically accounts for missing forecasts from individual models, (iii) allows public health officials to participate in the ensemble by assigning individual models to clusters, and (iv) provide an additional signal about when peak influenza may be near.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100832"},"PeriodicalIF":3.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297581","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}
引用次数: 0
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信