Epidemics最新文献

{"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":"2025-09-01","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}

{"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":"2025-09-01","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}

{"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_t) 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_t.</div></div><div><h3>Methods</h3><div>We review wastewater-based methods to estimate the R_t for SARS-CoV-2 based on similarity to the reference case-based R_t, 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_t models identified from the literature. Each model is compared to the R_t 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_t 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_t values than county R_t 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_t 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":"2025-09-01","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}

{"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":"2025-09-01","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}

{"title":"Machine learning approaches for real-time ZIP code and county-level estimation of state-wide infectious disease hospitalizations using local health system data","authors":"Tanvir Ahammed ,&nbsp;Md Sakhawat Hossain ,&nbsp;Christopher McMahan ,&nbsp;Lior Rennert","doi":"10.1016/j.epidem.2025.100823","DOIUrl":"10.1016/j.epidem.2025.100823","url":null,"abstract":"<div><div>The lack of conventional methods of estimating real-time infectious disease burden in granular regions inhibits timely and efficient public health response. Comprehensive data sources (e.g., state health department data) typically needed for such estimation are often limited due to 1) substantial delays in data reporting and 2) lack of geographic granularity in data provided to researchers. Leveraging real-time local health system data presents an opportunity to overcome these challenges. This study evaluates the effectiveness of machine learning and statistical approaches using local health system data to estimate current and previous COVID-19 hospitalizations in South Carolina. Random Forest models demonstrated consistently higher average median percent agreement accuracy compared to generalized linear mixed models for current weekly hospitalizations across 123 ZIP codes (72.29 %, IQR: 63.20–75.62 %) and 28 counties (76.43 %, IQR: 70.33–81.16 %) with sufficient health system coverage. To account for underrepresented populations in health systems, we combined Random Forest models with Classification and Regression Trees (CART) for imputation. The average median percent agreement was 61.02 % (IQR: 51.17–72.29 %) for all ZIP codes and 72.64 % (IQR: 66.13–77.69 %) for all counties. Median percent agreement for cumulative hospitalizations over the previous 6 months was 80.98 % (IQR: 68.99–89.66 %) for all ZIP codes and 81.17 % (IQR: 68.55–91.33 %) for all counties. These findings emphasize the effectiveness of utilizing real-time health system data to estimate infectious disease burden. Moreover, the methodologies developed in this study can be adapted to estimate hospitalizations for other diseases, offering a valuable tool for public health officials to respond swiftly and effectively to various health crises.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100823"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815115","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":"Optimizing influenza vaccine allocation by age using cost-effectiveness analysis: A comparison of 6720 vaccination program scenarios in children and adults in Belgium","authors":"Regina Manansala ,&nbsp;Joke Bilcke ,&nbsp;Lander Willem ,&nbsp;Niel Hens ,&nbsp;Philippe Beutels","doi":"10.1016/j.epidem.2025.100826","DOIUrl":"10.1016/j.epidem.2025.100826","url":null,"abstract":"<div><h3>Background</h3><div>Many European countries prioritize groups for annual influenza vaccination based on risk of severe disease and death. This has resulted in relatively high influenza vaccination coverage in older adults in Belgium. However, coverage is much lower in younger adults and negligible in children. Children and young adults are known to play a major role in the transmission dynamics of influenza. Thus, an important policy question is how influenza vaccines can be optimally allocated across age groups, taking indirect effects into account.</div></div><div><h3>Methods</h3><div>We adapted a dynamic transmission model to reproduce influenza seasonality in Belgium comparing 6720 mutually exclusive vaccination options, including current practice. Vaccination options were defined by different combinations of coverage level changes in nine age groups. We performed an economic evaluation comparing all options from a healthcare payer perspective. Quality-adjusted life-years (QALYs) were the primary health outcome. We expressed parametric uncertainty using the Incremental Net Monetary Benefits (INMB) approach.</div></div><div><h3>Results</h3><div>Of all the vaccination options considered, over 90 % dominated the current Belgian vaccination strategy in terms of cost-effectiveness. Children were estimated to contribute a substantial indirect protective effect to the overall population. The most cost-effective program increases vaccination coverage rates for children to 90 %, 50–64 years old to 48 %, and 65–74 years old to 75 %.</div></div><div><h3>Discussion</h3><div>Overall QALY gains can be maximized in seasonal influenza vaccination programs at acceptable costs by achieving high vaccination coverage in childhood age groups. Programmatic and ethical concerns towards such an implementation in the Belgian context need to be separately considered.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100826"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833472","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":"Detection of surges of SARS-Cov-2 using nonparametric Hawkes models","authors":"Sophie Phillips ,&nbsp;George Mohler ,&nbsp;Frederic Schoenberg","doi":"10.1016/j.epidem.2025.100824","DOIUrl":"10.1016/j.epidem.2025.100824","url":null,"abstract":"<div><div>Hawkes point process models have been shown to forecast the number of daily new cases of epidemic diseases, including SARS-CoV-2 (Covid-19), with high accuracy. Here, we explore how accurately Hawkes models forecast surges of Covid-19 in the United States. We use Hawkes models to estimate the effective reproduction rate <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> and transmission density parameters for Covid-19 case counts in each of the 50 United States, then forecast <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> in future weeks with simple exponential smoothing. A classifier based on <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>&gt;</mo><mi>x</mi></mrow></math></span> is applied to predict upcoming surges in cases each week from August 2020 to December 2021, using only data available up to that week. At false alarm rates below 5%, the forecasts based on <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> are correct more often than forecasts based on smoothing the raw case count data, achieving a maximum accuracy of 90% with <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>&gt;</mo><mn>1</mn><mo>.</mo><mn>39</mn></mrow></math></span>. The optimal decision boundary uses a combination of <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> and observed data.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100824"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830099","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":"Onset of infectiousness explains differences in transmissibility across Mycobacterium tuberculosis lineages","authors":"Etthel M. Windels ,&nbsp;Cecilia Valenzuela Agüí ,&nbsp;Bouke C. de Jong ,&nbsp;Conor J. Meehan ,&nbsp;Chloé Loiseau ,&nbsp;Galo A. Goig ,&nbsp;Michaela Zwyer ,&nbsp;Sonia Borrell ,&nbsp;Daniela Brites ,&nbsp;Sebastien Gagneux ,&nbsp;Tanja Stadler","doi":"10.1016/j.epidem.2025.100821","DOIUrl":"10.1016/j.epidem.2025.100821","url":null,"abstract":"<div><div><em>Mycobacterium tuberculosis</em> complex (MTBC) lineages show substantial variability in virulence, but the epidemiological consequences of this variability have not been studied in detail. Here, we aimed for a lineage-specific epidemiological characterization by applying phylodynamic models to genomic data from different countries, representing the most abundant MTBC lineages. Our results suggest that all lineages are associated with similar durations and levels of infectiousness, resulting in similar reproductive numbers. However, L1 and L6 are associated with a delayed onset of infectiousness, leading to longer periods between subsequent transmission events. Together, our findings highlight the role of MTBC genetic diversity in tuberculosis disease progression and transmission.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100821"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674697","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":"A CFD-informed barn-level swine disease dissemination model and its use for ventilation optimization","authors":"Maryam Safari ,&nbsp;Christian Fleming ,&nbsp;Jason A. Galvis ,&nbsp;Aniruddha Deka ,&nbsp;Felipe Sanchez ,&nbsp;Gustavo Machado ,&nbsp;Chi-An Yeh","doi":"10.1016/j.epidem.2025.100835","DOIUrl":"10.1016/j.epidem.2025.100835","url":null,"abstract":"<div><div>The airborne spread of infectious livestock diseases plays a crucial role in the propagation of epidemics, particularly in populations confined to densely populated facilities, such as commercial swine barns. In this study, we present a framework to study airborne disease dissemination within commercial swine barns and facilitate the strategic design of control actions, including optimization of ventilation and placement of sick animals (sick pen). This framework is based on a susceptible–infected–recovered (SIR) model that accounts for the between-pen disease spread within swine barns. A pen-to-pen contact network is used to construct a transmission matrix according to the transport of airborne respiratory pathogens across pens in the barns, via our Reynolds-averaged Navier–Stokes computational fluid dynamics (CFD) solver. By employing this CFD-augmented SIR model, we demonstrated that the location of the sick pen and the barn ventilation configuration played crucial roles in modifying disease dissemination dynamics at the barn level. In addition, we examined the effect of natural ventilation through different curtain adjustments. We observed that curtain adjustments either suppress the disease spread by an average of 64.8% or exacerbate the outbreak potential by an average of 5.8%, compared to the scenario where side curtains are not raised. Furthermore, we optimize the ventilation configuration via the selection and placement of ventilation fans through the integration of the CFD-augmented framework with the genetic algorithm to minimize the dissemination of swine disease within barns. Compared to the original barn ventilation settings, our optimized ventilation system significantly reduced disease spread by an average of 20%. Our study demonstrates that the use of the proposed framework provides a detailed understanding of the flow physics and the transport of airborne pathogens, which facilitate the optimization of ventilation systems and strategic management of sick pens within the swine barns.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100835"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169818","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":"Does spatial information improve forecasting of influenza-like illness?","authors":"Gabrielle Thivierge ,&nbsp;Aaron Rumack ,&nbsp;F. William Townes","doi":"10.1016/j.epidem.2025.100820","DOIUrl":"10.1016/j.epidem.2025.100820","url":null,"abstract":"<div><div>Seasonal influenza forecasting is critical for public health and individual decision making. We investigate whether the inclusion of data about influenza activity in neighboring states can improve point predictions and distribution forecasting of influenza-like illness (ILI) in each US state using statistical regression models. Using CDC FluView ILI data from 2010–2019, we forecast weekly ILI in each US state with quantile, linear, and Poisson autoregressive models fit using different combinations of ILI data from the target state, neighboring states, and the US population-weighted average. Scoring with root mean squared error and weighted interval score indicated that the covariate sets including neighbors and/or the US weighted average ILI showed slightly higher accuracy than models fit only using lagged ILI in the target state, on average. Additionally, the improvement in performance when including neighbors was similar to the improvement when including the US average instead, suggesting the proximity of the neighboring states is not the driver of the slight increase in accuracy. There is also clear within-season and between-season variability in the effect of spatial information on prediction accuracy.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100820"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714961","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}