Pub Date : 2025-10-10DOI: 10.1016/j.epidem.2025.100861
Gargi Deshpande , Bijay Rimal , Kristen Shelton , Jason Vogel , Bradley Stevenson , Katrin Gaardbo Kuhn
Upper respiratory infections caused by viruses such as respiratory syncytial virus (RSV) and influenza are major health concerns globally. Traditional surveillance methods of these viruses rely on clinical data, which can miss mild or asymptomatic cases, leading to gaps in understanding of their epidemiology. Wastewater-based surveillance (WBS) offers an alternative monitoring approach, providing real-time, population-representative data infection levels. This study aimed to evaluate the value of WBS for monitoring influenza A and B and RSV in Oklahoma from August 2022 to May 2024. Wastewater samples were collected weekly from 18 treatment plants statewide, and viral RNA was quantified using RT-qPCR. We compared wastewater data with reported influenza hospitalizations and RSV test positivity. We found significant seasonality in clinical outcomes as well as wastewater concentrations for influenza A and RSV. Our results also showed comparatively high wastewater concentrations during times when influenza hospitalizations and RSV test positivity were at their seasonal highs. Our study demonstrates the potential for WBS to offer timely insights into respiratory virus trends, particularly for underserved communities. This method complements traditional surveillance, offering a broader understanding of viral transmission and supporting public health interventions.
{"title":"Wastewater-based surveillance for influenza and respiratory syncytial virus: Insights from a 21-month study in Oklahoma","authors":"Gargi Deshpande , Bijay Rimal , Kristen Shelton , Jason Vogel , Bradley Stevenson , Katrin Gaardbo Kuhn","doi":"10.1016/j.epidem.2025.100861","DOIUrl":"10.1016/j.epidem.2025.100861","url":null,"abstract":"<div><div>Upper respiratory infections caused by viruses such as respiratory syncytial virus (RSV) and influenza are major health concerns globally. Traditional surveillance methods of these viruses rely on clinical data, which can miss mild or asymptomatic cases, leading to gaps in understanding of their epidemiology. Wastewater-based surveillance (WBS) offers an alternative monitoring approach, providing real-time, population-representative data infection levels. This study aimed to evaluate the value of WBS for monitoring influenza A and B and RSV in Oklahoma from August 2022 to May 2024. Wastewater samples were collected weekly from 18 treatment plants statewide, and viral RNA was quantified using RT-qPCR. We compared wastewater data with reported influenza hospitalizations and RSV test positivity. We found significant seasonality in clinical outcomes as well as wastewater concentrations for influenza A and RSV. Our results also showed comparatively high wastewater concentrations during times when influenza hospitalizations and RSV test positivity were at their seasonal highs. Our study demonstrates the potential for WBS to offer timely insights into respiratory virus trends, particularly for underserved communities. This method complements traditional surveillance, offering a broader understanding of viral transmission and supporting public health interventions.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100861"},"PeriodicalIF":2.4,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309655","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}
Pub Date : 2025-10-08DOI: 10.1016/j.epidem.2025.100859
Soren L. Larsen , Junke Yang , Huibin Lv , Yang Wei Huan , Qi Wen Teo , Tossapol Pholcharee , Ruipeng Lei , Akshita B. Gopal , Evan K. Shao , Logan Talmage , Chris K.P. Mok , Saki Takahashi , Alicia N.M. Kraay , Nicholas C. Wu , Pamela P. Martinez
Despite the increased availability of serological data, understanding serodynamics remains challenging. Serocatalytic models, which describe the rate of seroconversion (gain of antibodies) and seroreversion (loss of antibodies) within a population, have traditionally been fit to cross-sectional serological data to capture long-term transmission dynamics. However, a key limitation is their binary assumption on serological status, ignoring heterogeneity in optical density levels, antibody titers, and/or exposure history. Here, we implemented Gaussian mixture models - an established statistical tool - to cross-sectional data in order to characterize serological diversity of seasonal human coronaviruses (sHCoVs) across a wide range of age groups. These methods consistently identified multiple distinct seropositive levels, suggesting that among seropositive individuals, the number of prior exposures or response to infection may vary. We fit adapted, multi-compartment serocatalytic models with different assumptions on exposure history and waning of antibodies. The best fit model for each sHCoV was always one that accounted for host variation in the scale of serological response to infection. These models allowed us to estimate the strength and frequency of serological responses, finding that the time for a seronegative individual to become seropositive ranges from 2.40 to 7.03 years across sHCoVs, and most individuals mount a strong antibody response reflected in high optical density values, skipping lower levels of seropositivity. We find that despite frequent infection and strong serological responses, for all sHCoVs except 229E, individuals are likely to become seronegative again at some point after their first infection. Nonetheless, our results also indicate that by age 22, for each sHCoV the probability of having seroconverted at least once is over 95%. Crucially, our reimagined serocatalytic methods can be flexibly adapted across pathogens, having the potential to be broadly applied beyond this work.
{"title":"Reimagining the serocatalytic model for infectious diseases: A case study of common coronaviruses","authors":"Soren L. Larsen , Junke Yang , Huibin Lv , Yang Wei Huan , Qi Wen Teo , Tossapol Pholcharee , Ruipeng Lei , Akshita B. Gopal , Evan K. Shao , Logan Talmage , Chris K.P. Mok , Saki Takahashi , Alicia N.M. Kraay , Nicholas C. Wu , Pamela P. Martinez","doi":"10.1016/j.epidem.2025.100859","DOIUrl":"10.1016/j.epidem.2025.100859","url":null,"abstract":"<div><div>Despite the increased availability of serological data, understanding serodynamics remains challenging. Serocatalytic models, which describe the rate of seroconversion (gain of antibodies) and seroreversion (loss of antibodies) within a population, have traditionally been fit to cross-sectional serological data to capture long-term transmission dynamics. However, a key limitation is their binary assumption on serological status, ignoring heterogeneity in optical density levels, antibody titers, and/or exposure history. Here, we implemented Gaussian mixture models - an established statistical tool - to cross-sectional data in order to characterize serological diversity of seasonal human coronaviruses (sHCoVs) across a wide range of age groups. These methods consistently identified multiple distinct seropositive levels, suggesting that among seropositive individuals, the number of prior exposures or response to infection may vary. We fit adapted, multi-compartment serocatalytic models with different assumptions on exposure history and waning of antibodies. The best fit model for each sHCoV was always one that accounted for host variation in the scale of serological response to infection. These models allowed us to estimate the strength and frequency of serological responses, finding that the time for a seronegative individual to become seropositive ranges from 2.40 to 7.03 years across sHCoVs, and most individuals mount a strong antibody response reflected in high optical density values, skipping lower levels of seropositivity. We find that despite frequent infection and strong serological responses, for all sHCoVs except 229E, individuals are likely to become seronegative again at some point after their first infection. Nonetheless, our results also indicate that by age 22, for each sHCoV the probability of having seroconverted at least once is over 95%. Crucially, our reimagined serocatalytic methods can be flexibly adapted across pathogens, having the potential to be broadly applied beyond this work.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100859"},"PeriodicalIF":2.4,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294198","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}
Pub Date : 2025-09-19DOI: 10.1016/j.epidem.2025.100855
Xiahui Li, Fergus Chadwick, Ben Swallow
Bayesian inference methods are useful in infectious diseases modeling due to their capability to propagate uncertainty, manage sparse data, incorporate latent structures, and address high-dimensional parameter spaces. However, parameter inference through assimilation of observational data in these models remains challenging. While asymptotically exact Bayesian methods offer theoretical guarantees for accurate inference, they can be computationally demanding and impractical for real-time outbreak analysis. This review synthesizes recent advances in approximate Bayesian inference methods that aim to balance inferential accuracy with scalability. We focus on four prominent families: Approximate Bayesian Computation, Bayesian Synthetic Likelihood, Integrated Nested Laplace Approximation, and Variational Inference. For each method, we evaluate its relevance to epidemiological applications, emphasizing innovations that improve both computational efficiency and inference accuracy. We also offer practical guidance on method selection across a range of modeling scenarios. Finally, we identify hybrid exact approximate inference as a promising frontier that combines methodological rigor with the scalability needed for the response to outbreaks. This review provides epidemiologists with a conceptual framework to navigate the trade-off between statistical accuracy and computational feasibility in contemporary disease modeling.
{"title":"Advances in approximate Bayesian inference for models in epidemiology","authors":"Xiahui Li, Fergus Chadwick, Ben Swallow","doi":"10.1016/j.epidem.2025.100855","DOIUrl":"10.1016/j.epidem.2025.100855","url":null,"abstract":"<div><div>Bayesian inference methods are useful in infectious diseases modeling due to their capability to propagate uncertainty, manage sparse data, incorporate latent structures, and address high-dimensional parameter spaces. However, parameter inference through assimilation of observational data in these models remains challenging. While asymptotically exact Bayesian methods offer theoretical guarantees for accurate inference, they can be computationally demanding and impractical for real-time outbreak analysis. This review synthesizes recent advances in approximate Bayesian inference methods that aim to balance inferential accuracy with scalability. We focus on four prominent families: Approximate Bayesian Computation, Bayesian Synthetic Likelihood, Integrated Nested Laplace Approximation, and Variational Inference. For each method, we evaluate its relevance to epidemiological applications, emphasizing innovations that improve both computational efficiency and inference accuracy. We also offer practical guidance on method selection across a range of modeling scenarios. Finally, we identify hybrid exact approximate inference as a promising frontier that combines methodological rigor with the scalability needed for the response to outbreaks. This review provides epidemiologists with a conceptual framework to navigate the trade-off between statistical accuracy and computational feasibility in contemporary disease modeling.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100855"},"PeriodicalIF":2.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119932","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}
Pub Date : 2025-09-16DOI: 10.1016/j.epidem.2025.100857
Pierre Nouvellet
Most current methods to estimate the time-varying reproduction number (Rt), such as EpiEstim, rely on branching processes and the renewal equation. They also require subjective choices to set the level of temporal and spatial heterogeneity assumed. We propose a novel framework to estimate Rt based on Generalized Linear and Additive Models (GLM/GAM). By integrating the renewal equation model within GLM/GAM, the proposed framework, “Rtglm”, allows smooth estimation of Rt variations over time and space without relying on arbitrary scaling parameters. The performance of Rtglm was evaluated using historical datasets and simulated outbreaks. It demonstrated improved overall performance and accuracy compared to EpiEstim, as measured by the CRPS scores and Mean Square Errors respectively. However, when case incidence was low and Rt estimation relied on a smoothing term, Rtglm was marginally overconfident in its estimates. The method offers substantial improvement for the real-time estimation of spatio-temporal trends in Rt, with improved performance and lower reliance on arbitrarily set parameters. The open-source and user-friendly R package developed will also simplify user experience. Finally, the framework bridges gaps between epidemic monitoring methodologies and sets the stage for future extensions to enhance statistical inference and integrate additional epidemiological complexities, including the evaluation of intervention strategies.
{"title":"Rtglm: Unifying estimation of the time-varying reproduction number, Rt, under the Generalised Linear and Additive Models","authors":"Pierre Nouvellet","doi":"10.1016/j.epidem.2025.100857","DOIUrl":"10.1016/j.epidem.2025.100857","url":null,"abstract":"<div><div>Most current methods to estimate the time-varying reproduction number (<em>R<sub>t</sub></em>), such as <em>EpiEstim</em>, rely on branching processes and the renewal equation. They also require subjective choices to set the level of temporal and spatial heterogeneity assumed. We propose a novel framework to estimate <em>R<sub>t</sub></em> based on Generalized Linear and Additive Models (GLM/GAM). By integrating the renewal equation model within GLM/GAM, the proposed framework, “<em>Rtglm</em>”, allows smooth estimation of <em>R<sub>t</sub></em> variations over time and space without relying on arbitrary scaling parameters. The performance of <em>Rtglm</em> was evaluated using historical datasets and simulated outbreaks. It demonstrated improved overall performance and accuracy compared to <em>EpiEstim</em>, as measured by the CRPS scores and Mean Square Errors respectively. However, when case incidence was low and <em>R<sub>t</sub></em> estimation relied on a smoothing term, <em>Rtglm</em> was marginally overconfident in its estimates. The method offers substantial improvement for the real-time estimation of spatio-temporal trends in <em>R<sub>t</sub></em>, with improved performance and lower reliance on arbitrarily set parameters. The open-source and user-friendly R package developed will also simplify user experience. Finally, the framework bridges gaps between epidemic monitoring methodologies and sets the stage for future extensions to enhance statistical inference and integrate additional epidemiological complexities, including the evaluation of intervention strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100857"},"PeriodicalIF":2.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151631","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}
Pub Date : 2025-09-11DOI: 10.1016/j.epidem.2025.100856
Haowei Wang , Kin On Kwok , Ruiyun Li , Steven Riley
Background
The COVID-19 pandemic caused substantial pressure on healthcare, with many systems needing to prepare for and mitigate the consequences of surges in demand caused by multiple overlapping waves of infections. Therefore, public health agencies and health system managers also benefitted from short-term forecasts for respiratory infections that allowed them to manage services. While quantitative forecasts treating hospital admissions as continuous variables existed, many health managers prefer discrete levels of demand, similar to approaches used in weather and flooding. However, effective tools for generating precise sub-national forecasts remained limited.
Methods
We forecast regional COVID-19 hospitalisations in England, using the period from March 2020 to December 2021 for training and evaluating predictions using data from January to December 2022. We transform regional admission counts into an ordinal variable using n-tile and n-uniform methods. We further developed a method based on XGBoost, and used previously for influenza, to enable it to exploit the ordering information in ordinal hospital admission levels. We incorporated different types of data as predictors: epidemiological data including weekly region COVID-19 cases and hospital admissions, weather conditions and mobility data for multiple categories of locations. The impact of different discretisation methods and the number of ordinal levels was also considered.
Results
We found that mobility data brings about a more substantial improvement in predictive performance than relying only on epidemiological data and the inclusion of weather data. When both weather and mobility data are used in addition to epidemiological data, the results are very similar to models with only epidemiological data and mobility data. These results are robust in terms of the number of levels chosen for the forecast target.
Conclusion
Accurate ordinal forecasts of COVID-19 hospitalisations were obtained using XGBoost and mobility data. While uniform ordinal levels showed higher apparent accuracy, we recommend n-tile ordinal levels which contain far richer information.
{"title":"Forecasting regional COVID-19 hospitalisation in England using ordinal machine learning method","authors":"Haowei Wang , Kin On Kwok , Ruiyun Li , Steven Riley","doi":"10.1016/j.epidem.2025.100856","DOIUrl":"10.1016/j.epidem.2025.100856","url":null,"abstract":"<div><h3>Background</h3><div>The COVID-19 pandemic caused substantial pressure on healthcare, with many systems needing to prepare for and mitigate the consequences of surges in demand caused by multiple overlapping waves of infections. Therefore, public health agencies and health system managers also benefitted from short-term forecasts for respiratory infections that allowed them to manage services. While quantitative forecasts treating hospital admissions as continuous variables existed, many health managers prefer discrete levels of demand, similar to approaches used in weather and flooding. However, effective tools for generating precise sub-national forecasts remained limited.</div></div><div><h3>Methods</h3><div>We forecast regional COVID-19 hospitalisations in England, using the period from March 2020 to December 2021 for training and evaluating predictions using data from January to December 2022. We transform regional admission counts into an ordinal variable using n-tile and n-uniform methods. We further developed a method based on XGBoost, and used previously for influenza, to enable it to exploit the ordering information in ordinal hospital admission levels. We incorporated different types of data as predictors: epidemiological data including weekly region COVID-19 cases and hospital admissions, weather conditions and mobility data for multiple categories of locations. The impact of different discretisation methods and the number of ordinal levels was also considered.</div></div><div><h3>Results</h3><div>We found that mobility data brings about a more substantial improvement in predictive performance than relying only on epidemiological data and the inclusion of weather data. When both weather and mobility data are used in addition to epidemiological data, the results are very similar to models with only epidemiological data and mobility data. These results are robust in terms of the number of levels chosen for the forecast target.</div></div><div><h3>Conclusion</h3><div>Accurate ordinal forecasts of COVID-19 hospitalisations were obtained using XGBoost and mobility data. While uniform ordinal levels showed higher apparent accuracy, we recommend n-tile ordinal levels which contain far richer information.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100856"},"PeriodicalIF":2.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201860","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}
Pub Date : 2025-09-08DOI: 10.1016/j.epidem.2025.100854
Younjung Kim , Benoît Jaulhac , Juan F. Vesga , Laurence Zilliox , Nathalie Boulanger , W.John Edmunds , Raphaëlle Métras
Ixodes ricinus is the primary vector for Lyme disease and tick-borne encephalitis across Europe. However, the environmental drivers of the tick's complex life cycle have not been quantified with real-world data, making it challenging to incorporate tick demography into tick-borne disease transmission models. To address this gap, we fitted a mechanistic model to a detailed 10-year longitudinal dataset from four sites in Northern France, where I. ricinus is abundant and Lyme disease and tick-borne encephalitis have been reported for decades. By incorporating key demographic processes and the influence of environmental conditions on these processes, our model estimated oviposition, hatching, and moulting rates across a range of temperature or saturation deficit, as well as questing and vertebrate host contact rates. In the studied tick population, moulting peaked at 14.2 °C (95 %HDI: 12.5–16.1 °C), substantially lower than commonly suggested by laboratory-based studies, whereas oviposition and hatching peaked at 24.4 °C (95 %HDI: 10.9–27.2 °C) and 24.7 °C (95 %HDI: 17.8–27.2 °C), respectively. Furthermore, the parameter scaling the empirical baseline vertebrate host contact rates was found to vary significantly between the four study sites, with one site presenting up to 2.90 (95 %HDI: 2.15–3.86) times higher contact rates than the other three sites. Additionally, for ticks overwintering through diapause, moulting in spring more accurately matched the predominantly unimodal questing patterns observed, compared to moulting in summer. Finally, model projections under pessimistic climate change scenarios indicated decreasing tick abundance trends over the next two decades, while no significant decrease was predicted under moderate scenarios. This study provides a foundation for models of I. ricinus-borne pathogen transmission and can be adapted to other Ixodidae populations of public health significance.
{"title":"Environmental drivers of Ixodes ricinus tick population dynamics: Mechanistic modelling using longitudinal field surveys and climate data","authors":"Younjung Kim , Benoît Jaulhac , Juan F. Vesga , Laurence Zilliox , Nathalie Boulanger , W.John Edmunds , Raphaëlle Métras","doi":"10.1016/j.epidem.2025.100854","DOIUrl":"10.1016/j.epidem.2025.100854","url":null,"abstract":"<div><div><em>Ixodes ricinus</em> is the primary vector for Lyme disease and tick-borne encephalitis across Europe. However, the environmental drivers of the tick's complex life cycle have not been quantified with real-world data, making it challenging to incorporate tick demography into tick-borne disease transmission models. To address this gap, we fitted a mechanistic model to a detailed 10-year longitudinal dataset from four sites in Northern France, where <em>I. ricinus</em> is abundant and Lyme disease and tick-borne encephalitis have been reported for decades. By incorporating key demographic processes and the influence of environmental conditions on these processes, our model estimated oviposition, hatching, and moulting rates across a range of temperature or saturation deficit, as well as questing and vertebrate host contact rates. In the studied tick population, moulting peaked at 14.2 °C (95 %HDI: 12.5–16.1 °C), substantially lower than commonly suggested by laboratory-based studies, whereas oviposition and hatching peaked at 24.4 °C (95 %HDI: 10.9–27.2 °C) and 24.7 °C (95 %HDI: 17.8–27.2 °C), respectively. Furthermore, the parameter scaling the empirical baseline vertebrate host contact rates was found to vary significantly between the four study sites, with one site presenting up to 2.90 (95 %HDI: 2.15–3.86) times higher contact rates than the other three sites. Additionally, for ticks overwintering through diapause, moulting in spring more accurately matched the predominantly unimodal questing patterns observed, compared to moulting in summer. Finally, model projections under pessimistic climate change scenarios indicated decreasing tick abundance trends over the next two decades, while no significant decrease was predicted under moderate scenarios. This study provides a foundation for models of <em>I. ricinus</em>-borne pathogen transmission and can be adapted to other <em>Ixodidae</em> populations of public health significance.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100854"},"PeriodicalIF":2.4,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050415","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}
Pub Date : 2025-09-08DOI: 10.1016/j.epidem.2025.100853
Eugene SK LO, Serana CY SO, LT WONG, Kirran N. MOHAMMAD, KY LAW, KS CHAN, Shirley WY TSANG, Dawin LO, KH KUNG, Albert KW AU, SK CHUANG
Wastewater surveillance (WWS) was critical to Hong Kong’s COVID-19 response, providing early warning indicators and enabling targeted measures to control the epidemic in the city during the pandemic. As the approach to COVID-19 transitioned from containment to long-term management, maintaining the WWS programme became challenging owing to financial limitations. This article chronicles our efforts to optimize the programme to guarantee its long-term sustainability while preserving its efficacy in tracking disease trends and detecting novel variants. Prior to optimization, our WWS programme gathered samples from 120 locations weekly, encompassing 80 % of the population. Drawing from our experience, we examined several optimization measures, such as decreasing frequency of sampling and altering testing procedures. Nonetheless, these methods were deemed impractical owing to operational and technical difficulties. Ultimately, we determined that a reduction in sampling sites was the most viable method. Statistical analyses utilizing data from April 2023 to March 2024 corroborated this methodology, indicating that despite an 85 % decrease in sample locations (from 120 to 18), the surveillance data retained a high degree of reliability (R² > 0.97) compared to the original model. This optimized methodology decreased expenses by about 80 % while maintaining data reliability. By disseminating our methodology and findings, we aim to provide useful information that may aid other jurisdictions in establishing cost-effective WWS systems as they confront analogous difficulties globally.
{"title":"Optimisation of wastewater surveillance for COVID-19 after resumption of normalcy from the pandemic: A case of Hong Kong","authors":"Eugene SK LO, Serana CY SO, LT WONG, Kirran N. MOHAMMAD, KY LAW, KS CHAN, Shirley WY TSANG, Dawin LO, KH KUNG, Albert KW AU, SK CHUANG","doi":"10.1016/j.epidem.2025.100853","DOIUrl":"10.1016/j.epidem.2025.100853","url":null,"abstract":"<div><div>Wastewater surveillance (WWS) was critical to Hong Kong’s COVID-19 response, providing early warning indicators and enabling targeted measures to control the epidemic in the city during the pandemic. As the approach to COVID-19 transitioned from containment to long-term management, maintaining the WWS programme became challenging owing to financial limitations. This article chronicles our efforts to optimize the programme to guarantee its long-term sustainability while preserving its efficacy in tracking disease trends and detecting novel variants. Prior to optimization, our WWS programme gathered samples from 120 locations weekly, encompassing 80 % of the population. Drawing from our experience, we examined several optimization measures, such as decreasing frequency of sampling and altering testing procedures. Nonetheless, these methods were deemed impractical owing to operational and technical difficulties. Ultimately, we determined that a reduction in sampling sites was the most viable method. Statistical analyses utilizing data from April 2023 to March 2024 corroborated this methodology, indicating that despite an 85 % decrease in sample locations (from 120 to 18), the surveillance data retained a high degree of reliability (R² > 0.97) compared to the original model. This optimized methodology decreased expenses by about 80 % while maintaining data reliability. By disseminating our methodology and findings, we aim to provide useful information that may aid other jurisdictions in establishing cost-effective WWS systems as they confront analogous difficulties globally.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100853"},"PeriodicalIF":2.4,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050262","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}
Pub Date : 2025-08-22DOI: 10.1016/j.epidem.2025.100849
Gavrila A. Puspitarani , Yan-Shin Jackson Liao , Reinhard Fuchs , Amélie Desvars-Larrive
Traceability of animal movements and robust surveillance are crucial for prevention and control of animal diseases. While network analysis has emerged as a powerful tool for identifying higher-risk holdings through centrality metrics, its effectiveness depends on two methodological choices: (1) edge-weighting schemes (movement frequency vs. animal volume) and (2) centrality metric selection. This study investigates how alternative edge-weighting approaches (frequency vs. volume) influence network topology and node centrality rankings in a pig movement network.
Using 2021 pig movement data from Upper Austria (5,766 holdings; 92,914 movements), we: (1) quantify how edge-weighting schemes (frequency vs. volume) affect network topology and community structure, and (2) evaluate node ranking robustness across three centrality metrics (strength, betweenness, closeness) against epidemic simulation rankings. Our analysis reveals distinct edge weight distributions: frequency-based network exhibited a bimodal pattern, while volume-based was more uniform. We observed strong positive correlations ( 0.42–0.84; ) in node rankings across all centrality metrics (strength, closeness, betweenness), with consistent patterns observed both: (i) between frequency- and volume-weighted networks, and (ii) within each network representation. Strength centrality exhibited the highest correlation with the simulation-based rankings, particularly for the top 5% highest-ranked nodes ( 0.51 for frequency-based and 0.5 for volume-based). These findings highlight that strength centrality provides a computationally efficient and field-practical alternative to epidemic simulations for identifying high-risk holdings. This enables resource-efficient, data-driven surveillance while maintaining epidemiological relevance.
{"title":"Investigating the impact of edge weight selection on the pig trade network topology","authors":"Gavrila A. Puspitarani , Yan-Shin Jackson Liao , Reinhard Fuchs , Amélie Desvars-Larrive","doi":"10.1016/j.epidem.2025.100849","DOIUrl":"10.1016/j.epidem.2025.100849","url":null,"abstract":"<div><div>Traceability of animal movements and robust surveillance are crucial for prevention and control of animal diseases. While network analysis has emerged as a powerful tool for identifying higher-risk holdings through centrality metrics, its effectiveness depends on two methodological choices: (1) edge-weighting schemes (movement frequency vs. animal volume) and (2) centrality metric selection. This study investigates how alternative edge-weighting approaches (frequency vs. volume) influence network topology and node centrality rankings in a pig movement network.</div><div>Using 2021 pig movement data from Upper Austria (5,766 holdings; 92,914 movements), we: (1) quantify how edge-weighting schemes (frequency vs. volume) affect network topology and community structure, and (2) evaluate node ranking robustness across three centrality metrics (strength, betweenness, closeness) against epidemic simulation rankings. Our analysis reveals distinct edge weight distributions: frequency-based network exhibited a bimodal pattern, while volume-based was more uniform. We observed strong positive correlations (<span><math><mi>τ</mi></math></span> <span><math><mo>></mo></math></span> 0.42–0.84; <span><math><mrow><mi>p</mi><mo><</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>) in node rankings across all centrality metrics (strength, closeness, betweenness), with consistent patterns observed both: (i) between frequency- and volume-weighted networks, and (ii) within each network representation. Strength centrality exhibited the highest correlation with the simulation-based rankings, particularly for the top 5% highest-ranked nodes (<span><math><mrow><mi>τ</mi><mi>b</mi></mrow></math></span> <span><math><mo>=</mo></math></span> 0.51 for frequency-based and <span><math><mrow><mi>τ</mi><mi>b</mi></mrow></math></span> <span><math><mo>=</mo></math></span> 0.5 for volume-based). These findings highlight that strength centrality provides a computationally efficient and field-practical alternative to epidemic simulations for identifying high-risk holdings. This enables resource-efficient, data-driven surveillance while maintaining epidemiological relevance.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100849"},"PeriodicalIF":2.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907267","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}
Pub Date : 2025-08-22DOI: 10.1016/j.epidem.2025.100852
Anca Farkas , Rahela Carpa , Edina Szekeres , Adela Teban-Man , Cristian Coman , Anca Butiuc-Keul
The most menacing sources of environmental contamination with antibiotic resistant bacteria are effluents derived from anthropic activities. Even when wastewater treatment processes are implemented, conventional methods are not able to completely retain the antibiotic resistance determinants. We propose an antibiotic resistance risk assessment, incorporating the characterisation of ARB, ARGs and MGEs in different environmental compartments.
Antibiotic susceptibility testing of 678 Enterobacterales isolates revealed an increased degree of intrinsic resistance to erythromycin (77.9 %), high level of resistance to ampicillin (39.7 %), low frequency of carbapenem resistance (2.36 %), and a percentage of 34.4 % MDR strains. The most frequent resistance determinants were blaTEM-1 (26.5 %) and tetA (8.26 %), while the intI1 gene was found in 7.37 % of isolates. Resistant Enterobacterales from aquatic matrices with different degrees of contamination were identified as Citrobacter spp. (n = 46), Enterobacter spp. (n = 35), Klebsiella spp. (n = 54) and Escherichia coli (n = 107). A strong statistical correlation was observed between the presence of intI1 and the ARG index (0.768) in resistant Enterobacter spp.
Distinct clustering of strains was not observed across different environmental matrices, especially in those directly impacted by human-derived bacteria. Also, distribution of ARB patterns and diversity of ARGs was stable from the taxonomic perspective. Dendrogram analysis based on ERIC-PCR profiles confirmed the presence of strains with identical DNA fingerprints in non-related aquatic ecosystems. The epidemiology of resistant Citrobacter, Enterobacter, Klebsiella and Escherichia isolates confirmed an extensive migration and environmental dispersion of strains with human health significance, particularly important for water resources.
{"title":"Epidemiology and environmental risks of antibiotic resistant Enterobacterales isolates in different aquatic matrices from North-Western Romania","authors":"Anca Farkas , Rahela Carpa , Edina Szekeres , Adela Teban-Man , Cristian Coman , Anca Butiuc-Keul","doi":"10.1016/j.epidem.2025.100852","DOIUrl":"10.1016/j.epidem.2025.100852","url":null,"abstract":"<div><div>The most menacing sources of environmental contamination with antibiotic resistant bacteria are effluents derived from anthropic activities. Even when wastewater treatment processes are implemented, conventional methods are not able to completely retain the antibiotic resistance determinants. We propose an antibiotic resistance risk assessment, incorporating the characterisation of ARB, ARGs and MGEs in different environmental compartments.</div><div>Antibiotic susceptibility testing of 678 <em>Enterobacterales</em> isolates revealed an increased degree of intrinsic resistance to erythromycin (77.9 %), high level of resistance to ampicillin (39.7 %), low frequency of carbapenem resistance (2.36 %), and a percentage of 34.4 % MDR strains. The most frequent resistance determinants were <em>bla</em><sub><em>TEM-1</em></sub> (26.5 %) and <em>tetA</em> (8.26 %), while the <em>intI1</em> gene was found in 7.37 % of isolates. Resistant <em>Enterobacterales</em> from aquatic matrices with different degrees of contamination were identified as <em>Citrobacter</em> spp. (n = 46), <em>Enterobacter</em> spp. (n = 35), <em>Klebsiella</em> spp. (n = 54) and <em>Escherichia coli</em> (n = 107). A strong statistical correlation was observed between the presence of <em>intI1</em> and the ARG index (0.768) in resistant <em>Enterobacter</em> spp.</div><div>Distinct clustering of strains was not observed across different environmental matrices, especially in those directly impacted by human-derived bacteria. Also, distribution of ARB patterns and diversity of ARGs was stable from the taxonomic perspective. Dendrogram analysis based on ERIC-PCR profiles confirmed the presence of strains with identical DNA fingerprints in non-related aquatic ecosystems. The epidemiology of resistant <em>Citrobacter, Enterobacter, Klebsiella</em> and <em>Escherichia</em> isolates confirmed an extensive migration and environmental dispersion of strains with human health significance, particularly important for water resources.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100852"},"PeriodicalIF":2.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911852","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}
Pub Date : 2025-08-19DOI: 10.1016/j.epidem.2025.100847
Dhorasso Temfack, Jason Wyse
Effective epidemic modeling and surveillance require computationally efficient methods that can continuously update parameter estimates as new data becomes available. This paper explores the application of an online variant of Sequential Monte Carlo Squared (O-SMC) to the stochastic Susceptible–Exposed–Infectious–Removed (SEIR) model for real-time epidemic tracking. The advantage of O-SMC lies in its ability to update parameter estimates using a particle Metropolis–Hastings kernel by only utilizing a fixed window of recent observations. This feature enables timely parameter updates and significantly enhances computational efficiency compared to standard SMC, which requires processing all past observations. First, we demonstrate the efficiency of O-SMC on simulated epidemic data, where both the true parameter values and the observation process are known. We then make an application to a real-world COVID-19 dataset from Ireland, successfully tracking the epidemic and estimating a time-dependent reproduction number of the disease. Our results show that O-SMC provides accurate online estimates of both static and dynamic epidemiological parameters while substantially reducing computational cost. These findings highlight the potential of O-SMC for real-time epidemic monitoring and supporting adaptive public health interventions.
{"title":"Sequential Monte Carlo Squared for online inference in stochastic epidemic models","authors":"Dhorasso Temfack, Jason Wyse","doi":"10.1016/j.epidem.2025.100847","DOIUrl":"10.1016/j.epidem.2025.100847","url":null,"abstract":"<div><div>Effective epidemic modeling and surveillance require computationally efficient methods that can continuously update parameter estimates as new data becomes available. This paper explores the application of an online variant of Sequential Monte Carlo Squared (O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>) to the stochastic Susceptible–Exposed–Infectious–Removed (SEIR) model for real-time epidemic tracking. The advantage of O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> lies in its ability to update parameter estimates using a particle Metropolis–Hastings kernel by only utilizing a fixed window of recent observations. This feature enables timely parameter updates and significantly enhances computational efficiency compared to standard SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, which requires processing all past observations. First, we demonstrate the efficiency of O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> on simulated epidemic data, where both the true parameter values and the observation process are known. We then make an application to a real-world COVID-19 dataset from Ireland, successfully tracking the epidemic and estimating a time-dependent reproduction number of the disease. Our results show that O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> provides accurate online estimates of both static and dynamic epidemiological parameters while substantially reducing computational cost. These findings highlight the potential of O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> for real-time epidemic monitoring and supporting adaptive public health interventions.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100847"},"PeriodicalIF":2.4,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890108","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}
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