Epidemics最新文献

{"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":"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":"2025-05-24","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":"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":"2025-05-15","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":"Ceasing sampling at wastewater treatment plants where viral dynamics are most predictable","authors":"Mo Liu,&nbsp;Devan G. Becker","doi":"10.1016/j.epidem.2025.100834","DOIUrl":"10.1016/j.epidem.2025.100834","url":null,"abstract":"<div><div>Wastewater sampling has been shown to be an effective tool for monitoring the dynamics of an infectious disease. During the COVID-19 pandemic, many sampling sites were opened in order to capture as much information as possible. However, with the pandemic waning, not all sampling sites need to continue operating.</div><div>In this work, we investigate a method for evaluating sampling sites for which sampling can stop. We apply machine learning methods to predict the mutation frequencies from wastewater sites on the next day in one location based on the frequencies on previous days in other locations, then record the prediction error. The sites with the lowest prediction error are the ones that contain the least amount of unique information, and sampling can cease at those locations. We demonstrate a systematic approach to evaluating prediction errors and several interpretations of the error. We demonstrate this method on five locations in Switzerland, finding two locations that could be removed with minimal information loss.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100834"},"PeriodicalIF":3.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the transmission dynamics of African swine fever virus within commercial swine barns: Quantifying the contribution of multiple transmission pathways","authors":"Aniruddha Deka ,&nbsp;Jason A. Galvis ,&nbsp;Christian Fleming ,&nbsp;Maryam Safari ,&nbsp;Chi-An Yeh ,&nbsp;Gustavo Machado","doi":"10.1016/j.epidem.2025.100828","DOIUrl":"10.1016/j.epidem.2025.100828","url":null,"abstract":"<div><div>The transmission of African swine fever virus (ASFV) within swine barns occurs through direct and indirect pathways. Identifying and quantifying the roles of ASFV dissemination within barns is crucial for developing disease control strategies. We created a stochastic transmission model to examine the ASFV dissemination dynamics through transmission routes within commercial swine barns. We consider seven transmission routes at three disease dynamics levels: within-pens, between-pens, and within-room transmission, along with the transfer of pigs between pens within rooms. We simulated ASFV spread within barns of various sizes and layouts from rooms with a median of 32 pens (IQR: 28-40), where each pen housed a median of 34 pigs (IQR: 29-36). Our model enables tracking the viral load in each pen and monitoring the disease status at the pen level. Results show that between-pen transmission pathways exhibited the highest contribution in spread, accounting for 66.76%, whereas within-pen and within-room pathways account for 26.12% and 7.12%, respectively. Nose-to-nose contact between pens was the primary dissemination route, comprising an average of 46.04%. On the other hand, aerosol transmission within pens had the lowest contribution, accounting for less than 1%. Furthermore, we show that the daily transfer of pigs between pens did not impact the spread of ASFV. On average, at the room level, the combined approach of passive daily surveillance and mortality-focused surveillance enabled ASFV detection within 18 (IQR: 16-19) days. The model allows us to monitor the viral load variation across the room over time, revealing that most of the viral load accumulates in pens closer to the exhaust fans after a month. This work significantly deepens our understanding of ASFV spread within commercial swine production farms in the U.S. and highlights the main transmission pathways that should be prioritized when implementing ASFV countermeasure actions at the room level.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100828"},"PeriodicalIF":3.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878659","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":"Modelling the interplay between responsive individual vaccination decisions and the spread of SARS-CoV-2","authors":"Karina Wallrafen-Sam ,&nbsp;Maria Garcia Quesada ,&nbsp;Benjamin A. Lopman ,&nbsp;Samuel M. Jenness","doi":"10.1016/j.epidem.2025.100831","DOIUrl":"10.1016/j.epidem.2025.100831","url":null,"abstract":"<div><h3>Background</h3><div>COVID-19 vaccine hesitancy proved to be a major barrier to higher uptake, but it is unclear whether interventions targeting hesitancy could result in substantial prevention benefits. Epidemic models that represent vaccine decision-making psychology can provide insight into the potential impact of vaccine promotion interventions in the context of the COVID-19 pandemic and future epidemics of vaccine-preventable diseases.</div></div><div><h3>Methods</h3><div>We coupled a network- and agent-based model of SARS-CoV-2 transmission with a social-psychological vaccination decision-making model in which vaccine side effects and breakthrough infections could “nudge” individuals towards vaccine resistance while spikes in COVID-19 hospitalizations could nudge them towards vaccine willingness. This model was parameterized and calibrated to represent the COVID-19 epidemic in Georgia, USA from January 2021 to August 2022. We modelled counterfactual scenarios in which increases to resistant-to-willing nudges were combined with decreases to willing-to-resistant nudges. We compared cumulative vaccine doses administered, SARS-CoV-2 incidence, and COVID-related deaths across scenarios.</div></div><div><h3>Results</h3><div>Increasing the probability of hospitalization-prompted resistant-to-willing nudges increased vaccine uptake by as much as 5.4 % and decreased SARS-CoV-2 incidence by as much as 4.0 %. In contrast, decreasing the probability of breakthrough infection-related willing-to-resistant nudges had a negligible impact on further vaccination and disease outcomes.</div></div><div><h3>Conclusions</h3><div>Vaccine promotion interventions that address community-level factors influencing decision-making may have a greater ability to avert SARS-CoV-2 infections than those targeted to individual vaccination and infection history. Additionally, reactive vaccine promotion interventions may have only limited prevention benefits in the short term, suggesting that attention should be paid to formulating interventions that accurately anticipate the case curve.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100831"},"PeriodicalIF":3.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895672","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":"2025-04-23","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":"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":"2025-04-23","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":"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":"2025-04-12","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":"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":"2025-04-09","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}