Epidemics最新文献

{"title":"Wastewater surveillance: From COVID-19 emergency response to a pillar of continuous public health.","authors":"David A Larsen, Kathy Leung","doi":"10.1016/j.epidem.2026.100912","DOIUrl":"https://doi.org/10.1016/j.epidem.2026.100912","url":null,"abstract":"","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":" ","pages":"100912"},"PeriodicalIF":2.4,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147624635","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 methodological variability in wastewater surveillance: A wavelet decomposition approach","authors":"Maria L. Daza–Torres ,&nbsp;J. Cricelio Montesinos-López ,&nbsp;Rachel Olson ,&nbsp;C. Winston Bess ,&nbsp;Colleen C. Naughton ,&nbsp;Heather N. Bischel ,&nbsp;Miriam Nuño","doi":"10.1016/j.epidem.2026.100897","DOIUrl":"10.1016/j.epidem.2026.100897","url":null,"abstract":"<div><div>Wastewater surveillance has emerged as a critical public health tool, enabling early detection of infectious disease outbreaks and providing timely, population-level insights into community health trends. However, variability in sample collection and processing, for example, between wastewater influent and settled solids, can introduce methodological noise that differentially impacts true epidemiological signals and limits cross-site comparability.</div><div>To address this challenge, we aimed to discern underlying disease trends from methodological variability in SARS-CoV-2 wastewater data using the discrete wavelet transform (DWT), with a focus on comparing influent and solids samples from the same geographic locations.</div><div>We applied DWT to longitudinal SARS-CoV-2 RNA concentrations in wastewater from five cities in California: Los Banos, Turlock, Woodland, Winters, and Esparto—each with paired influent and solids samples. DWT decomposes each signal into two components: (1) approximation coefficients that capture smoothed long-term trends, and (2) detail coefficients that isolate high-frequency fluctuations and transient variations in the signal. We reconstructed signals by progressively removing the high-frequency components (detail coefficients) and assessed similarity between sample types using hierarchical clustering.</div><div>Clustering of raw signals did not yield city-specific groupings, indicating that methodological noise obscured the underlying epidemiological signal. Intermediate reconstructions that retained some high-frequency components continued to show mixed groupings. In contrast, reconstructions based solely on low-frequency approximation coefficients revealed clear, city-specific clustering, with influent and solids samples from the same city aligning closely.</div><div>These findings support our hypothesis that high-frequency components are primarily driven by sample processing and laboratory noise, while low-frequency components reflect shared epidemiological trends. Our findings underscore the importance of denoising in wastewater data preprocessing and offer a scalable approach for enhancing signal comparability across regions and sample types.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100897"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147327781","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":"Whose knowledge counts? Equity, epistemic justice, and reforming infectious disease research culture","authors":"Hanna-Tina Fischer ,&nbsp;Augustina Koduah","doi":"10.1016/j.epidem.2025.100883","DOIUrl":"10.1016/j.epidem.2025.100883","url":null,"abstract":"<div><div>Infectious disease epidemiology is shaped by engrained research cultures that privilege biomedical and quantitative knowledge systems, systematically marginalizing qualitative, contextual, and locally informed approaches. These hierarchies reflect deeper inequities in who leads, who participates, and whose knowledge counts—disparities often patterned along geography, gender, language, and disciplinary background. This perspectives paper examines how funding priorities, academic training, and publishing norms sustain epistemic and structural exclusion, particularly for researchers based in the Global South. Drawing on Ghana’s COVID-19 response, we show how reliance on externally developed epidemiological models mirrored broader marginalization in research authorship, agenda-setting, and decision-making. We argue that equity-focused reforms in funding, training, and publishing—grounded in epistemic and distributive justice—are necessary to transform infectious disease research culture. A more just and inclusive research culture is not only an ethical imperative but essential to the effectiveness and legitimacy of epidemic responses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100883"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913642","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":"Changing contact patterns in Newfoundland and Labrador, Canada in response to public health measures during the COVID-19 pandemic","authors":"Renny Doig ,&nbsp;Amy Hurford ,&nbsp;Suzette Spurrell ,&nbsp;Andrea Morrissey ,&nbsp;Liangliang Wang ,&nbsp;Caroline Colijn","doi":"10.1016/j.epidem.2026.100892","DOIUrl":"10.1016/j.epidem.2026.100892","url":null,"abstract":"<div><div>The provincial government of Newfoundland and Labrador, Canada implemented a contact tracing program as part of a containment strategy during the COVID-19 pandemic. A high proportion of cases were detected and contact traced, and our analysis provides insights into secondary case distributions and contact patterns in Newfoundland and Labrador. We used a heuristic approximation of secondary cases to account for ambiguities in who infected whom. These approximate values provide an empirical distribution of secondary cases. These distributions are compared against the stringency of public health measures. Additionally, we visualised age- and contact-based patterns and compared these patterns with respect to stringency. The maximum number of contacts traced per week was 4645 and the mean number of contacts traced per case was 12.5. Approximate 95 % CIs of the effective reproduction number under Alert levels 2–4 were (1.02,1.21), (0.99,1.39), (0.84,1.06), and (1.20,1.47). We find that this level of contact tracing was sufficient, in combination with other public health interventions, to contain pandemic SARS-CoV-2 spread in Newfoundland and Labrador prior to the establishment of the Omicron variant. Understanding age-based contact patterns is necessary to describe disease spread and the risk of severe outcomes. A successful containment strategy requires that contact tracing capacity is not exceeded, making it necessary to understand the behaviour of high-contact individuals.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100892"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146120756","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":"Social contact patterns derived from an epidemiological survey and GPS-based co-location data – A systematic comparison using parallel data collections during the COVID-19 pandemic in Germany","authors":"Huynh Thi Phuong ,&nbsp;Janik Suer ,&nbsp;Vitaly Belik ,&nbsp;Alejandra Rincón Hidalgo ,&nbsp;Andrzej K. Jarynowski ,&nbsp;Richard Pastor ,&nbsp;Steven Schulz ,&nbsp;Ashish Thampi ,&nbsp;Chao Xu ,&nbsp;Marlli Zambrano ,&nbsp;Rafael Mikolajczyk ,&nbsp;André Karch ,&nbsp;Veronika K. Jaeger ,&nbsp;on behalf of OptimAgent Consortium","doi":"10.1016/j.epidem.2026.100886","DOIUrl":"10.1016/j.epidem.2026.100886","url":null,"abstract":"<div><div>The parametrisation of contact behaviour is crucial for infectious disease transmission models. Contact information derived from self-reported surveys and from co-location in space and time (GPS-based) may reflect different dimensions of contact behaviour, which might be associated with distinct epidemiological risks depending on the contagion of interest. This study explores whether and how contacts measured using these distinct approaches exhibit similar or complementary contact patterns. We compare the mean number of contacts and the mean excess number of contacts (i.e. the ratio of mean squared contacts to mean contacts) from the COVIMOD survey and NETCHECK GPS co-location data between April 2020 and December 2021. While mean contacts measure contact intensity, mean excess contacts reflect dispersion, which is important for understanding superspreading behaviour. Mean contacts were considerably higher in co-location data (11.04; 95 %CI: 10.90–11.19) than in survey data (3.38; 95 % CI: 3.30–3.47); however, both data sources correlated well with each other. Mean excess contacts were similar during periods of strict non-pharmaceutical interventions (NPIs) but diverged when NPIs were lifted, with co-location data values rising more markedly. Setting-specific contact patterns also differed, potentially due to methodological differences in setting classification and data capture. Furthermore, regional variation was more pronounced in co-location data, with densely populated city-states showing higher contact numbers. Comparative insights from the two data sources demonstrate that GPS-based and survey-based contact data capture complementary and distinct aspects of human interaction. Combining both sources could provide a more comprehensive picture of human interactions relevant to infectious disease modelling.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100886"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978270","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":"Transmission lineage dynamics and the detection of viral importation in emerging epidemics","authors":"Joseph L.-H. Tsui ,&nbsp;Prathyush Sambaturu ,&nbsp;Rosario Evans Pena ,&nbsp;Linus Too ,&nbsp;Bernardo Gutierrez ,&nbsp;Rhys Inward ,&nbsp;Moritz U.G. Kraemer ,&nbsp;Louis du Plessis ,&nbsp;Oliver G. Pybus","doi":"10.1016/j.epidem.2026.100893","DOIUrl":"10.1016/j.epidem.2026.100893","url":null,"abstract":"<div><div>The accurate inference of pathogen movements among locations during an epidemic is crucial for measuring infectious disease spread and for informing effective control strategies. Phylogeographic methods can reconstruct historical patterns of disease dissemination by combining the evolutionary history of sampled pathogen genomes with geographic information. Despite a substantial expansion of pathogen genomics during and after the COVID-19 pandemic, only a small fraction of infections are typically sampled and sequenced, leading to underestimation of the true intensity of viral importation. Here, we seek to understand the sampling processes underlying this underestimation. We show that the coupling of viral importation and local transmission dynamics can result in local transmission lineages with different size distributions, influencing the probability that individual viral importation events will be detected. Using analytical and simulation approaches, we show that both the proportion of importation events detected and the temporal patterns of inferred importation are highly sensitive to importation dynamics and local transmission parameters, resulting in substantial biases, particularly under low-intensity sampling. Our findings highlight the importance of interpreting phylogeographic estimates in the context of outbreak conditions, particularly when comparing viral movements across time and among epidemic settings characterised by rapid spatial dissemination. These insights are critical for improving the reliability of genomic epidemiology approaches to the design of public health responses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100893"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147311734","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":"Predicting local COVID-19 emergences: A time-series classification approach and value of data from social media, search engines, and neighbouring regions","authors":"Erin E. Rees ,&nbsp;Mani Sotoodeh ,&nbsp;José Denis-Robichaud ,&nbsp;Hélène Carabin ,&nbsp;Simon de de Montigny","doi":"10.1016/j.epidem.2026.100891","DOIUrl":"10.1016/j.epidem.2026.100891","url":null,"abstract":"<div><h3>Background</h3><div>Early warning for known infectious disease threats use methods that focus on detection of outbreaks, often at large geographical scales. However, earlier warning, specifically at the onset of disease emergence (i.e., first case(s)) and at finer spatial scales could significantly improve timeliness and targeting of prevention and control efforts. As a proof-of-concept, we demonstrate that a early classification time-series approach can predict COVID-19 emergence at a local jurisdictional level with a 10-day lead time.</div></div><div><h3>Methods</h3><div>To predict emergence with a 10-day lead time in Canadian health regions (HRs) during January to November 2020, we developed three classification models. Predictor variables were restricted to information about COVID-19 and included daily metrics at the HR level for social media and traditional EBS data (i.e., news media), and at the provincial/territorial (P/T) level for search engine data. Predictor contributions from neighbouring areas additionally included reported case data (with the other predictors) from the nearest region, or weighted by distance and/or population size of all adjacent regions.</div></div><div><h3>Results</h3><div>Using the highest performing model, Deep Gated Recurrent Unit, the classification balanced accuracy was higher for <em>distance-</em> and <em>population-based</em> spatial weighting (0.78), than for nearest neighbour data only (0.64). It was also higher when open-access information was included with traditional EBS information (0.78), compared to excluding open-access information (0.63).</div></div><div><h3>Conclusions</h3><div>In a Canadian context for COVID-19, using a retrospective approach, study results demonstrate classification models can predict emergence with a 10-day lead time at the finest spatial scale of health governance (i.e., HRs) used by P/Ts. Furthermore, prediction accuracy improves with information from neighbouring regions and open-access data (social media, search engine). Implications for operationalizing our method in event-based surveillance systems are discussed.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100891"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078038","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":"Generating geographically detailed synthetic contact networks: A generalizable approach with applications to epidemic outcome disparities","authors":"Alexander Y. Tulchinsky ,&nbsp;Alisa Hamilton ,&nbsp;Fardad Haghpanah ,&nbsp;Nodar Kipshidze ,&nbsp;Eili Y. Klein","doi":"10.1016/j.epidem.2026.100900","DOIUrl":"10.1016/j.epidem.2026.100900","url":null,"abstract":"<div><div>Social contact networks based on synthetic populations are useful for studying the effects of population features and policy interventions on disease transmission. We present an adaptable and accessible method for generating geographically detailed synthetic populations and associated contact networks from public census data, and apply it to a selection of US metropolitan areas. We simulate a respiratory pathogen spreading in each population and find that network structure alone produces differences in infection risk among racial/ethnic subpopulations, as well as between geographic locations of differing socioeconomic status, particularly in urban centers. We then simulate a work and school closure policy intervention, and find an increase in geographic infection risk differences, and in some cities, in racial/ethnic risk differences as well. Different outcomes between cities are associated with demographic and geographic differences in household size, contact with school-age children, and employment industry. The results suggest that demography, socioeconomics, and policy interact in a context-dependent manner to shape epidemiological outcomes. We have made our methods available as open-source software that can be extended by other researchers.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100900"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147318764","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 epidemiology of pathogens with pandemic potential: A review of key parameters and clustering analysis","authors":"Jack Ward ,&nbsp;Oswaldo Gressani ,&nbsp;Sol Kim ,&nbsp;Niel Hens ,&nbsp;W. John Edmunds","doi":"10.1016/j.epidem.2025.100882","DOIUrl":"10.1016/j.epidem.2025.100882","url":null,"abstract":"<div><h3>Introduction</h3><div>In the light of the COVID-19 pandemic many countries are trying to widen their pandemic planning from its traditional focus on influenza. However, it is impossible to draw up detailed plans for every pathogen with epidemic potential. We set out to try to simplify this process by reviewing the epidemiology of a range of pathogens with pandemic potential and seeing whether they fall into groups with shared epidemiological traits.</div></div><div><h3>Methods</h3><div>We reviewed the epidemiological characteristics of 19 different pathogens with pandemic potential (those on the WHO priority list of pathogens, different strains of influenza and Mpox). We extracted data on key parameters (reproduction number serial interval, proportion of presymptomatic transmission, case fatality risk and transmission route) and applied an unsupervised learning algorithm. This combined Monte Carlo sampling with ensemble clustering to classify pathogens into distinct epidemiological archetypes based on their shared characteristics.</div></div><div><h3>Results</h3><div>From 154 articles we extracted 302 epidemiological parameter estimates. The clustering algorithms categorise these pathogens into six archetypes (1) highly transmissible Coronaviruses, (2) moderately transmissible Coronaviruses, (3) high-severity contact and zoonotic pathogens, (4) Influenza viruses (5) MERS-CoV-like and (6) MPV-like.</div></div><div><h3>Conclusion</h3><div>Unsupervised learning on epidemiological data can be used to define distinct pathogen archetypes. This method offers a valuable framework to allocate emerging and novel pathogens into defined groups to evaluate common approaches for their control.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100882"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760587","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":"Evaluating mobility restrictions through spatiotemporal effective reproduction number analysis in a multi-patch model with complex mobility data","authors":"Byul Nim Kim ,&nbsp;Minchan Choi ,&nbsp;Hyosun Lee,&nbsp;Sunmi Lee","doi":"10.1016/j.epidem.2025.100884","DOIUrl":"10.1016/j.epidem.2025.100884","url":null,"abstract":"<div><div>Understanding the spatial and temporal dynamics of infectious disease transmission is critical for effective epidemic preparedness and response. COVID-19 transmission is influenced by mobility patterns, regional connectivity, and evolving public health interventions, making it challenging to quantify region-specific transmission risks. Our study integrates intervention-driven analysis, real-world data, and high-resolution modeling to establish a robust computational framework for assessing interregional transmission dynamics. We employ a multi-patch model to estimate the time-dependent regional effective reproduction number and systematically quantify interregional infection contributions. By integrating high-resolution mobility and COVID-19 incidence data from South Korea, we identify key transmission hubs and assess the impact of mobility-driven transmission across different epidemic phases. Our results highlight Seoul and Gyeonggi as dominant sources of interregional spread, with their influence varying across phases of the pandemic. By distinguishing locally transmitted infections from mobility-induced cases, we introduce a data-driven approach to evaluate the effectiveness of movement restrictions and targeted interventions. Findings from the Pre-Delta phase demonstrate that mobility controls in transmission hubs significantly reduced the spread of infections. Our results underscore that densely connected regions disproportionately drive nationwide transmission, emphasizing the need for adaptive, phase-dependent intervention strategies rather than uniform nationwide policies. This study advances computational epidemiology by providing a scalable framework for integrating real-world mobility data with epidemic modeling to inform targeted, data-driven public health responses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100884"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913652","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}