Epidemics最新文献

{"title":"Estimating the role of heterogeneities in the first-wave dynamics of COVID-19 in Nigeria","authors":"Christabel James,&nbsp;Jason Matthiopoulos,&nbsp;Daniel T. Haydon","doi":"10.1016/j.epidem.2026.100944","DOIUrl":"10.1016/j.epidem.2026.100944","url":null,"abstract":"<div><div>Heterogeneity, defined as variation in individual-specific traits such as susceptibility or connectivity that are not subject to short-term change, can strongly influence epidemiological dynamics. In particular, it can reduce the effective reproduction number and the herd immunity threshold, thereby increasing the potential to control infectious disease outbreaks. However, heterogeneities at the epidemic level are extremely challenging to observe directly and difficult to estimate indirectly from epidemiological data. This study develops a methodology to estimate heterogeneities in COVID-19 transmission from under-reported incidence data. We estimate the parameters of a modified Susceptible Infected Recovered model using Bayesian Markov Chain Monte Carlo techniques, first testing the proposed method on simulated data to determine its effectiveness before applying it to incompletely reported time-series data from ten Nigerian states. We estimate the average basic reproduction number to be 1.360 (95% credible intervals (CrI): 1.356–1.366), the heterogeneity parameter representing the coefficient of variation in susceptibility to be 2.561 (95% CrI 2.247–2.884), and the reporting probability to be 0.005 (95% CrI 0.004–0.006). Information criteria indicated that the model with heterogeneity was better supported than a homogenous model. The herd immunity threshold with this estimated level of heterogeneity for the best model drops from 0.26 to 0.04 compared to the homogenous model and the percentage of individuals infected in the first wave from over 20% to about 8%. Underestimating the impact of heterogeneities would lead to overestimating the extent of interventions required to bring the effective reproduction number below 1, important when considering the design of control programs that combine different, partially effective interventions.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"56 ","pages":"Article 100944"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898010","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":"Population-level associations in the spread of co-circulating respiratory viruses: A multi-method statistical investigation using incidence data","authors":"Nadine Barth,&nbsp;Gesa Carstens,&nbsp;Eva Kozanli,&nbsp;Wanda Han,&nbsp;Lisa Hermans,&nbsp;Daniela Paolotti,&nbsp;Steven Abrams,&nbsp;Geert Molenberghs,&nbsp;Niel Hens,&nbsp;Christel Faes,&nbsp;Dirk Eggink,&nbsp;Albert Jan Van Hoek,&nbsp;Andrea Torneri","doi":"10.1016/j.epidem.2026.100925","DOIUrl":"10.1016/j.epidem.2026.100925","url":null,"abstract":"<div><div>Respiratory infections remain a major global health burden, causing substantial morbidity and mortality worldwide. The responsible viruses circulate concurrently, potentially affecting each other’s dynamics, yet the extent and direction of such interactions remain poorly understood. Characterising these cross-pathogen effects at the population level is essential for elucidating transmission dynamics and guiding mitigation strategies. Using incidence data from a participatory syndromic surveillance system with multiplex PCR (polymerase chain reaction) confirmation of specific pathogens, we applied complementary statistical approaches, including multivariate regression, endemic–epidemic, and distributed-lag models, to characterise immediate and delayed associations among seven major respiratory diseases. We show that these pathogens form a connected system of temporal associations in which some pairs, such as SARS-CoV-2 and human seasonal coronaviruses, exhibit positive associations in their temporal incidence patterns, primarily from SARS-CoV-2 to human seasonal coronaviruses, whereas others, such as influenza and rhinovirus or parainfluenza virus show negative associations in circulation dynamics. Associations were often directional rather than reciprocal: for instance, rhinovirus was negatively associated with subsequent human seasonal coronaviruses, whereas the reverse pattern was not observed, while positive bidirectional associations between human metapneumovirus and parainfluenza virus were observed in several models. Temporal association patterns were largely consistent across analytical frameworks, suggesting persistent co-circulation dynamics among the studied respiratory viruses. By integrating multiple analytic frameworks, our study provides a comprehensive, data-driven view of patterns of co-circulation and statistical association among respiratory viruses, offering crucial insights for improved epidemic forecasting and mitigation strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"56 ","pages":"Article 100925"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867455","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":"The impact of ambiguously reported epidemiological parameters for infectious disease modelling and recommended best practices.","authors":"Carmen Tamayo Cuartero, Joshua W Lambert, Gina Cuomo-Dannenburg, Julia Fitzner, Patricia Ndumbi, Chloe Rice, Dina Saulo, Lisa Waddell, James M Azam, Patrick Doohan, Joseph T Hicks, Kelly McCain, Christian Morgenstern, Tristan M Naidoo, Shazia Ruybul-Pesántez, Thomas Rawson, Cosmo Santoni, H Juliette T Unwin, Adam Kucharski, Anne Cori, Sangeeta Bhatia, Ruth McCabe","doi":"10.1016/j.epidem.2026.100942","DOIUrl":"https://doi.org/10.1016/j.epidem.2026.100942","url":null,"abstract":"<p><p>Epidemiological parameters characterise the natural history, transmission and severity of a pathogen and are necessary to understand the spread of infectious diseases. These parameters underpin our ability to quantify and respond to disease outbreaks. Parameters can be estimated from observations using a range of methods and are often reported in varied ways throughout the literature. These parameter estimates constitute essential inputs to infectious disease models used to quantify and project disease spread and burden, and assess intervention impact. Hence, any incompleteness or ambiguity in reported parameter estimates can have downstream consequences on the inferences drawn from the models that use these estimates. We summarise common issues with incomplete or ambiguous reporting of epidemiological parameter estimates and illustrate the impact through five case studies. Specifically, we show that in many instances, misinterpreting parameters reported in the literature can lead to biased conclusions that mislead subsequent public health responses. Additionally, we provide recommendations on how to clearly communicate common epidemiological parameter estimates consistently and reproducibly, to maximise their secondary use and comparison, in turn minimising erroneous extraction from the literature and application in epidemiological analysis.</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":" ","pages":"100942"},"PeriodicalIF":2.4,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148762773","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 serological cross-reactivity to disentangle the dynamics of West Nile and Usutu viruses in an emerging area","authors":"Jonathan Bastard ,&nbsp;Camille Migné ,&nbsp;Teheipuaura Helle ,&nbsp;Estelle Agneray ,&nbsp;Clément Bigeard ,&nbsp;Yasmine Boudjadi ,&nbsp;Manon Chevrier ,&nbsp;Marine Dumarest ,&nbsp;Mathilde Gondard ,&nbsp;Sandra Martin-Latil ,&nbsp;Laure Mathews-Martin ,&nbsp;Thierry Petit ,&nbsp;Thomas Charpentier ,&nbsp;Hanae Pouillevet ,&nbsp;Benoit Durand ,&nbsp;Raphaëlle Métras ,&nbsp;Gaëlle Gonzalez","doi":"10.1016/j.epidem.2026.100923","DOIUrl":"10.1016/j.epidem.2026.100923","url":null,"abstract":"<div><div>Zoos may serve as sentinel sites for zoonotic vector-borne diseases. West Nile virus (WNV) and Usutu virus (USUV) are closely related orthoflaviviruses transmitted between <em>Culex</em> mosquitoes and a bird reservoir. Both viruses can also infect mammals, including humans, where they may cause symptoms and, more rarely, hospitalization and death. However, serological cross-reactivity between WNV and USUV complicates their differential diagnosis. Here, we aimed to reconstruct the dynamics of emergence of WNV in a zoo located in a newly affected area in Europe, using ELISA and Virus Neutralization Test (VNT) serological analysis of 1707 animal sera collected between 2015 and 2024. Combining this data in a model accounting for cross-reactivity with USUV, we estimated yearly forces of infection (FOI) by both viruses, and thus found that WNV likely circulated in the area one year prior to the first cases reported to the passive surveillance system. Our results also showed that, in the zoo, mammals and reptiles had a lower risk of infection than birds (relative risk of 0.14 [0.05; 0.28]), and that the exposure of birds to water (aquatic lifestyle or proximity to stagnant water) affected the risk. Finally, we estimated diagnosis parameters, including the sensitivity of the VNT (80.4% [76.5%; 84.3%]), the expected VNT titer value, and the level of serological cross-reactivity between viruses during the VNT. To conclude, our modelling framework allowed to disentangle the co-circulation of two closely related viruses, a crucial point in ensuring the reliable sentinel surveillance of these vector-borne zoonotic pathogens.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"55 ","pages":"Article 100923"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167676","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":"Wastewater surveillance: From COVID-19 emergency response to a pillar of continuous public health","authors":"David A. Larsen,&nbsp;Kathy Leung","doi":"10.1016/j.epidem.2026.100912","DOIUrl":"10.1016/j.epidem.2026.100912","url":null,"abstract":"","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"55 ","pages":"Article 100912"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","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":"A stochastic meta-population model of Ebola virus disease transmission for informing public health decisions","authors":"Thibaut Jombart ,&nbsp;Sarah Kada ,&nbsp;Debapriyo Chakraborty ,&nbsp;David W. Redding ,&nbsp;Jessica Abbate","doi":"10.1016/j.epidem.2026.100922","DOIUrl":"10.1016/j.epidem.2026.100922","url":null,"abstract":"<div><div>Ebola virus disease (EVD) remains a constant international public health threat. Developing models that integrate the complex transmission dynamics of EVD is essential for informing evidence-based strategies for outbreak preparedness and response. Here, we introduce a stochastic, meta-population, compartmental model of EVD epidemics which accounts for key stages of the disease transmission including ecologically-driven zoonotic introductions, person-to-person transmission, spatial spread, and potentially complex interventions. Our model can distinguish between different transmission modes (direct transmission from contact with infectious cases, funeral exposures, or sexual transmission from contact with convalescent individuals) as well as different intervention mechanisms (overall reduction of contacts, safe and dignified burials, and vaccination). We illustrate our approach by simulating EVD epidemics in an area at high risk of zoonotic introduction in the Democratic Republic of the Congo, and show how it can be used to identify potential future transmission hotspots and help assess the scaling of future responses. Our model is implemented in a computer-efficient, free, open-source software, and can be used for informing public health policies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"55 ","pages":"Article 100922"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148206756","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 age-dependent transmission and vaccination policy in Singapore’s SARS-CoV-2 epidemic: A computational modelling approach","authors":"Jingyan Huang ,&nbsp;Zhi Ling ,&nbsp;Mousumi Roy ,&nbsp;Shihui Jin ,&nbsp;Jeremy WeiQuan Chan ,&nbsp;Kelvin Bryan Tan ,&nbsp;Swapnil Mishra ,&nbsp;MLGH Network","doi":"10.1016/j.epidem.2026.100921","DOIUrl":"10.1016/j.epidem.2026.100921","url":null,"abstract":"<div><div>The SARS-CoV-2 pandemic in Singapore revealed pronounced age-specific differences in disease transmission and severity. To investigate these dynamics, we developed a semi-mechanistic Bayesian hierarchical model that jointly estimated SARS-CoV-2 transmission across seven age groups from 2021 to 2023. Our model integrates both case and hospitalization data, while accounting for vaccination rollouts and time-varying contact patterns between age groups. Using a non-parametric approach, we estimated changes in contact behaviour directly from the epidemiological data, avoiding assumptions about their functional form. The effective reproduction number (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi><mo>,</mo><mn>1</mn></mrow></msub></math></span>) for children aged 0–14 fluctuated around one before October 2021 followed by a fluctuation with a mean smaller than one thereafter. <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi><mo>,</mo><mi>a</mi></mrow></msub></math></span> values for <span><math><mrow><mn>2</mn><mo>≤</mo><mi>a</mi><mo>≤</mo><mn>6</mn></mrow></math></span> fluctuated around one while <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi><mo>,</mo><mn>7</mn></mrow></msub></math></span> fluctuated around one before September 2021 followed by a fluctuation with a mean of 1.5 thereafter. The model closely matched empirical trends, with posterior estimates and 95% credible intervals aligning well with observed case and hospitalization data across all age groups. These findings underscore the critical role of age-specific contact behaviour and highlight the importance of targeted interventions during evolving pandemic phases.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"55 ","pages":"Article 100921"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148057833","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":"Forecasting seasonal influenza epidemics with physics-informed neural networks","authors":"Martina Rama ,&nbsp;Gabriele Santin ,&nbsp;Giulia Cencetti ,&nbsp;Michele Tizzoni ,&nbsp;Bruno Lepri","doi":"10.1016/j.epidem.2026.100919","DOIUrl":"10.1016/j.epidem.2026.100919","url":null,"abstract":"<div><div>Accurate epidemic forecasting is critical for informing public health decisions and timely interventions. While physics-informed neural networks have shown promise in various scientific domains, their potential application to real-time epidemic forecasting remains underexplored. Here, we present SIR-INN, a hybrid forecasting framework that integrates the mechanistic structure of the classical Susceptible–Infectious–Recovered (SIR) model into a neural network architecture. Trained once on synthetic epidemic scenarios, the model is able to generalize across epidemic conditions without retraining. From limited and noisy observations, SIR-INN infers key transmission parameters via Markov chain Monte Carlo, generating probabilistic short- and long-term forecasts. We validate SIR-INN using national influenza data from the Italian National Institute of Health in the 2023–2024 and 2024–2025 seasons. The model performs competitively with current state-of-the-art approaches, particularly in terms of weighted interval score. It shows accurate predictive performance in nearly all phases of the outbreak, with improved accuracy observed for the 2024–2025 influenza season. Credible uncertainty intervals are consistently maintained, while coverage metrics highlight room for improvement in uncertainty calibration. SIR-INN offers a computationally efficient, transparent, and generalizable solution for epidemic forecasting, appropriately leveraging the framework’s hybrid design. Its ability to provide real-time predictions of epidemic dynamics, together with uncertainty quantification, makes it a promising tool for real-world epidemic forecasting.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"55 ","pages":"Article 100919"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167675","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":"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}