Pub Date : 2025-12-01Epub 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":"2025-12-01","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 : 2025-12-01Epub Date: 2025-10-21DOI: 10.1016/j.epidem.2025.100862
Majd Al Aawar, Ajitesh Srivastava
Forecasting the hospitalizations caused by the Influenza virus is vital for public health planning so hospitals can be better prepared for an influx of patients. Many forecasting methods have been used in real-time during the Influenza seasons and submitted to the CDC for public communication. We hypothesize that we can improve forecasting by using multiple mechanistic models to produce potential trajectories and use machine learning to learn how to combine those trajectories into an improved forecast. We propose a Tree Ensemble model design that utilizes the individual predictors of our baseline model SIkJalpha to improve its performance. Each predictor is generated by changing a set of hyperparameters. We compare our prospective forecasts deployed for the FluSight challenge (seasons ending in 2022, 2023, and 2024) to all the other submitted approaches. Our approach is fully automated and does not require any manual tuning. Our submissions remained in the top 33% of the models in all seasons. We demonstrate that our Random Forest-based approach is able to improve upon the forecasts of the individual predictors in terms of mean absolute error, coverage, and weighted interval score. Our method retrospectively outperformed all other models in terms of the mean absolute error and the weighted interval score based on the mean across all weekly submissions of the 2021–22 season.
{"title":"Random Forest of epidemiological models for Influenza forecasting","authors":"Majd Al Aawar, Ajitesh Srivastava","doi":"10.1016/j.epidem.2025.100862","DOIUrl":"10.1016/j.epidem.2025.100862","url":null,"abstract":"<div><div>Forecasting the hospitalizations caused by the Influenza virus is vital for public health planning so hospitals can be better prepared for an influx of patients. Many forecasting methods have been used in real-time during the Influenza seasons and submitted to the CDC for public communication. We hypothesize that we can improve forecasting by using multiple mechanistic models to produce potential trajectories and use machine learning to learn how to combine those trajectories into an improved forecast. We propose a Tree Ensemble model design that utilizes the individual predictors of our baseline model SIkJalpha to improve its performance. Each predictor is generated by changing a set of hyperparameters. We compare our prospective forecasts deployed for the FluSight challenge (seasons ending in 2022, 2023, and 2024) to all the other submitted approaches. Our approach is fully automated and does not require any manual tuning. Our submissions remained in the top 33% of the models in all seasons. We demonstrate that our Random Forest-based approach is able to improve upon the forecasts of the individual predictors in terms of mean absolute error, coverage, and weighted interval score. Our method retrospectively outperformed all other models in terms of the mean absolute error and the weighted interval score based on the mean across all weekly submissions of the 2021–22 season.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100862"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363570","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 : 2025-12-01Epub Date: 2025-10-28DOI: 10.1016/j.epidem.2025.100860
Gabriella Santiago , Carla White , Brendan Collins , Simon Cottrell , Chris Williams , Biagio Lucini , Mike B. Gravenor
Introduction:
Respiratory Syncytial Virus (RSV) is a single-stranded RNA virus and a major cause of hospitalisations in paediatric and geriatric populations. In the Northern Hemisphere, the RSV season is typically between October and March. Following the introduction of Non-pharmaceutical Interventions (NPIs), in response to the COVID-19 pandemic, disruptions in seasonality have been observed.
Methods:
We used an age-structured, deterministic SE2I2R model with time-dependent contact rates to study RSV hospitalisations and seasonality in the context of specific NPIs in Wales. The transmission process was linked to a clinical events model, to allow comparison to paediatric admissions data from Public Health Wales. The model was calibrated using Welsh demographics, social contact surveys and a severity index of Welsh NPI impact.
Results:
Admissions data revealed three out-of-season outbreaks (Autumn 2020, Autumn 2021 and Summer 2022). A surge of admissions in Winter 2022-23 and Winter 2023-24 were forecasted, with peak timings correctly predicted, despite a more protracted outbreak observed in the data. Approximately, 90% of RSV admissions in Wales from 2016-22 were in infants under 1 year old; with the greatest shift in admissions age-structure in 2-4 year olds (quintupling in 2021). The model predicted a rapid return to pre-pandemic patterns after disruptions.
Discussion/Conclusions:
Out-of-season peaks chiefly coincided with NPI relaxation. The post-pandemic response of RSV, in terms of timings, magnitude and age-structure shift, were all broadly consistent with simple interruptions in population exposure during the pandemic and the build up of immune naïve cohorts. Our model forms the basis of medium-term projections for paediatric RSV admissions in Wales.
{"title":"Investigating the impact of non-pharmaceutical interventions (NPIs) on post-pandemic Respiratory Syncytial Virus (RSV) hospitalisations and seasonality in Wales, UK","authors":"Gabriella Santiago , Carla White , Brendan Collins , Simon Cottrell , Chris Williams , Biagio Lucini , Mike B. Gravenor","doi":"10.1016/j.epidem.2025.100860","DOIUrl":"10.1016/j.epidem.2025.100860","url":null,"abstract":"<div><h3>Introduction:</h3><div>Respiratory Syncytial Virus (RSV) is a single-stranded RNA virus and a major cause of hospitalisations in paediatric and geriatric populations. In the Northern Hemisphere, the RSV season is typically between October and March. Following the introduction of Non-pharmaceutical Interventions (NPIs), in response to the COVID-19 pandemic, disruptions in seasonality have been observed.</div></div><div><h3>Methods:</h3><div>We used an age-structured, deterministic SE<sub>2</sub>I<sub>2</sub>R model with time-dependent contact rates to study RSV hospitalisations and seasonality in the context of specific NPIs in Wales. The transmission process was linked to a clinical events model, to allow comparison to paediatric admissions data from Public Health Wales. The model was calibrated using Welsh demographics, social contact surveys and a severity index of Welsh NPI impact.</div></div><div><h3>Results:</h3><div>Admissions data revealed three out-of-season outbreaks (Autumn 2020, Autumn 2021 and Summer 2022). A surge of admissions in Winter 2022-23 and Winter 2023-24 were forecasted, with peak timings correctly predicted, despite a more protracted outbreak observed in the data. Approximately, 90% of RSV admissions in Wales from 2016-22 were in infants under 1 year old; with the greatest shift in admissions age-structure in 2-4 year olds (quintupling in 2021). The model predicted a rapid return to pre-pandemic patterns after disruptions.</div></div><div><h3>Discussion/Conclusions:</h3><div>Out-of-season peaks chiefly coincided with NPI relaxation. The post-pandemic response of RSV, in terms of timings, magnitude and age-structure shift, were all broadly consistent with simple interruptions in population exposure during the pandemic and the build up of immune naïve cohorts. Our model forms the basis of medium-term projections for paediatric RSV admissions in Wales.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100860"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417477","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 : 2025-12-01Epub Date: 2025-11-26DOI: 10.1016/j.epidem.2025.100868
Pietro Coletti , Niel Hens , Christel Faes , Huong Q. McLean , Edward A. Belongia , Melissa Rolfes , Alexandra Mellis , Carrie Reed , Jessica Biddle , Ahra Kim , Yuwei Zhu , H. Keipp Talbot , Carlos G. Grijalva
Background
Studies on SARS-CoV-2 household transmission often assume random mixing, overlooking detailed contact patterns and the timing of physical distancing.
Methods
To address this, we examined interactions within 280 households, including 280 index cases and 544 members, enrolled from April 2020 to April 2021 in Nashville, Tennessee, and central Wisconsin. Eligible households were enrolled within 7 days of index case symptom onset if at least one member was initially asymptomatic. Participants were monitored for 14 days, with symptoms and respiratory specimens collected daily, and contact data retrospectively assessed at three time points: the day before index case symptom onset, the day before enrollment, and 14 days post-enrollment. We fitted Exponential Random Graph Models to the contact pattern to identify drivers of household contact. We used the fitted household models to inform a two-level mixing model to account for community infection risk, and we calibrated it to the infection data. We then used the calibrated model to study different implementation of physical distancing.
Results
Contact patterns showed a significant reduction in physical interactions after infection awareness, particularly avoidance of index cases, with a 77% reduction in contact density (95% CI [65%-84%], ). Simulations from the two-level mixing model indicated that initiating contact reductions at symptom onset could lower secondary infections by over 25% in households of 4-5 members.
Conclusions
These results demonstrate how behavior changes following infection awareness reduce transmission. Implementing physical distancing earlier, at symptom onset, could further limit secondary infections and enhance household transmission control.
{"title":"The impact of household physical distancing and its timing on the transmission of SARS-CoV-2: Insights from a household transmission evaluation study","authors":"Pietro Coletti , Niel Hens , Christel Faes , Huong Q. McLean , Edward A. Belongia , Melissa Rolfes , Alexandra Mellis , Carrie Reed , Jessica Biddle , Ahra Kim , Yuwei Zhu , H. Keipp Talbot , Carlos G. Grijalva","doi":"10.1016/j.epidem.2025.100868","DOIUrl":"10.1016/j.epidem.2025.100868","url":null,"abstract":"<div><h3>Background</h3><div>Studies on SARS-CoV-2 household transmission often assume random mixing, overlooking detailed contact patterns and the timing of physical distancing.</div></div><div><h3>Methods</h3><div>To address this, we examined interactions within 280 households, including 280 index cases and 544 members, enrolled from April 2020 to April 2021 in Nashville, Tennessee, and central Wisconsin. Eligible households were enrolled within 7 days of index case symptom onset if at least one member was initially asymptomatic. Participants were monitored for 14 days, with symptoms and respiratory specimens collected daily, and contact data retrospectively assessed at three time points: the day before index case symptom onset, the day before enrollment, and 14 days post-enrollment. We fitted Exponential Random Graph Models to the contact pattern to identify drivers of household contact. We used the fitted household models to inform a two-level mixing model to account for community infection risk, and we calibrated it to the infection data. We then used the calibrated model to study different implementation of physical distancing.</div></div><div><h3>Results</h3><div>Contact patterns showed a significant reduction in physical interactions after infection awareness, particularly avoidance of index cases, with a 77% reduction in contact density (95% CI [65%-84%], <span><math><mrow><mi>p</mi><mo><</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>). Simulations from the two-level mixing model indicated that initiating contact reductions at symptom onset could lower secondary infections by over 25% in households of 4-5 members.</div></div><div><h3>Conclusions</h3><div>These results demonstrate how behavior changes following infection awareness reduce transmission. Implementing physical distancing earlier, at symptom onset, could further limit secondary infections and enhance household transmission control.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100868"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662465","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 : 2025-12-01Epub 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":"2025-12-01","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 : 2025-12-01Epub Date: 2025-11-11DOI: 10.1016/j.epidem.2025.100870
Claire J. Standley , J. Gabrielle Breugelmans , Amol Chaudhari , Neil Cherian , Sabrina Chwalek , Arminder Deol , Janan Dietrich , Lora du Moulin , Geoffrey Otim , Wilmot James , Stefan Kloth , Sana Masmoudi , Nicaise Ndembi , Nqobile Ndlovu , Danny Scarponi , Franz Schnetzinger , Molly Shapiro , Andrew Hebbeler
Artificial intelligence (AI) provides paradigm-shifting opportunities to accelerate epidemic preparedness and response and ensure health security. Such benefits may be particularly applicable to countries in Africa, which have to date struggled to meet compliance obligations under international health security frameworks. Here, we build on discussions that took place at the March 2024 Health Security Partnership for Africa workshop in Addis Ababa, Ethiopia, to describe potential applications of AI-enabled approaches to accelerate activities throughout the preparedness ecosystem, with a particular focus on the rapid development and deployment of novel vaccines in support of the 100 Days Mission, focusing on Africa. We also consider the risks and barriers that may challenge successful deployment of AI for health security in African settings, and opportunities to elevate African leadership on governance and implementation.
{"title":"Artificial intelligence for health security in Africa: Benefits, risks and opportunities","authors":"Claire J. Standley , J. Gabrielle Breugelmans , Amol Chaudhari , Neil Cherian , Sabrina Chwalek , Arminder Deol , Janan Dietrich , Lora du Moulin , Geoffrey Otim , Wilmot James , Stefan Kloth , Sana Masmoudi , Nicaise Ndembi , Nqobile Ndlovu , Danny Scarponi , Franz Schnetzinger , Molly Shapiro , Andrew Hebbeler","doi":"10.1016/j.epidem.2025.100870","DOIUrl":"10.1016/j.epidem.2025.100870","url":null,"abstract":"<div><div>Artificial intelligence (AI) provides paradigm-shifting opportunities to accelerate epidemic preparedness and response and ensure health security. Such benefits may be particularly applicable to countries in Africa, which have to date struggled to meet compliance obligations under international health security frameworks. Here, we build on discussions that took place at the March 2024 Health Security Partnership for Africa workshop in Addis Ababa, Ethiopia, to describe potential applications of AI-enabled approaches to accelerate activities throughout the preparedness ecosystem, with a particular focus on the rapid development and deployment of novel vaccines in support of the 100 Days Mission, focusing on Africa. We also consider the risks and barriers that may challenge successful deployment of AI for health security in African settings, and opportunities to elevate African leadership on governance and implementation.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100870"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145574407","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 : 2025-12-01Epub Date: 2025-10-24DOI: 10.1016/j.epidem.2025.100864
Bimandra A. Djaafara , Mumbua Mutunga , Obiora A. Eneanya , Alpha Forna , Zulma M. Cucunubá
International training of Global South researchers represents a strategic investment that yields substantial returns, rather than the traditional “brain drain” framing. This perspective synthesises the experiences of infectious disease epidemiologists from Colombia, Indonesia, Kenya, Nigeria, and Sierra Leone who completed training in Global North institutions between 2015 and 2024. Despite facing challenges, language barriers, and representational pressures, we demonstrate how Global South researchers transform these obstacles into unique strengths that enhance local research capabilities. Our experiences also show that Global South researchers serve as vital bridges between academic worlds, contributing irreplaceable contextual knowledge while building collaborative networks that advance infectious disease epidemiology research regardless of geographic location. We provide four strategic recommendations for better infectious disease epidemiology research ecosystems: 1) creating supportive institutional environments in Global North institutions, 2) building sustainable partnerships that strengthen home institutions, 3) embracing individual agency and responsibility, and 4) strengthening regional collaborations while adapting to evolving global contexts. Our narrative progresses from challenges to empowerment, demonstrating that Global South researchers are valuable contributors essential to advancing infectious disease epidemiology research.
{"title":"The bridge between two worlds: Global South researchers' journeys through Global North academic training and beyond","authors":"Bimandra A. Djaafara , Mumbua Mutunga , Obiora A. Eneanya , Alpha Forna , Zulma M. Cucunubá","doi":"10.1016/j.epidem.2025.100864","DOIUrl":"10.1016/j.epidem.2025.100864","url":null,"abstract":"<div><div>International training of Global South researchers represents a strategic investment that yields substantial returns, rather than the traditional “brain drain” framing. This perspective synthesises the experiences of infectious disease epidemiologists from Colombia, Indonesia, Kenya, Nigeria, and Sierra Leone who completed training in Global North institutions between 2015 and 2024. Despite facing challenges, language barriers, and representational pressures, we demonstrate how Global South researchers transform these obstacles into unique strengths that enhance local research capabilities. Our experiences also show that Global South researchers serve as vital bridges between academic worlds, contributing irreplaceable contextual knowledge while building collaborative networks that advance infectious disease epidemiology research regardless of geographic location. We provide four strategic recommendations for better infectious disease epidemiology research ecosystems: 1) creating supportive institutional environments in Global North institutions, 2) building sustainable partnerships that strengthen home institutions, 3) embracing individual agency and responsibility, and 4) strengthening regional collaborations while adapting to evolving global contexts. Our narrative progresses from challenges to empowerment, demonstrating that Global South researchers are valuable contributors essential to advancing infectious disease epidemiology research.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100864"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145410676","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 : 2025-12-01Epub 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":"2025-12-01","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 : 2025-12-01Epub 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":"2025-12-01","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 : 2025-12-01Epub 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":"2025-12-01","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号