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.
{"title":"Changing contact patterns in Newfoundland and Labrador, Canada in response to public health measures during the COVID-19 pandemic.","authors":"Renny Doig, Amy Hurford, Suzette Spurrell, Andrea Morrissey, Liangliang Wang, Caroline Colijn","doi":"10.1016/j.epidem.2026.100892","DOIUrl":"https://doi.org/10.1016/j.epidem.2026.100892","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"100892"},"PeriodicalIF":2.4,"publicationDate":"2026-01-30","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}
Pub Date : 2026-01-28DOI: 10.1016/j.epidem.2026.100889
Rachel Lobay , Ajitesh Srivastava , Daniel J. McDonald
Reconstructing the course of the COVID-19 pandemic through estimating incident infections is important for assessing disease burden and characterizing transmission dynamics. While wastewater concentration data have been used to estimate infections in localized pre-Omicron studies, a scalable approach that estimates variant-specific shedding rates and that accounts for underreporting remains underdeveloped. To this end, we develop a multi-source approach to retrospectively estimate daily COVID-19 infections in U.S. states during the Omicron era. Our approach integrates wastewater and seroprevalence surveillance data to improve infection estimates during the Delta-Omicron transition period. These refined estimates, along with wastewater concentration data adjusted for limited coverage, are used to calculate variant-specific shedding rates, which inform daily infection estimates going forward. While case-based estimates tend to exhibit striking volatility, these infection estimates show more stable and interpretable patterns that closely align with Omicron subvariant transitions. Moreover, we directly quantify the degree of underreporting, showing the extent that reported cases significantly underestimate disease burden in a sample of seven U.S. states. In these states, case reports capture less than a quarter of total infections, leaving the vast majority unaccounted for in official reports. Finally, we estimate time-varying effective reproduction numbers and growth rates to provide a more accurate and timely picture of transmission dynamics over the Omicron era in U.S. states.
{"title":"From wastewater to infection estimates: Incident COVID-19 infections during Omicron in the U.S.","authors":"Rachel Lobay , Ajitesh Srivastava , Daniel J. McDonald","doi":"10.1016/j.epidem.2026.100889","DOIUrl":"10.1016/j.epidem.2026.100889","url":null,"abstract":"<div><div>Reconstructing the course of the COVID-19 pandemic through estimating incident infections is important for assessing disease burden and characterizing transmission dynamics. While wastewater concentration data have been used to estimate infections in localized pre-Omicron studies, a scalable approach that estimates variant-specific shedding rates and that accounts for underreporting remains underdeveloped. To this end, we develop a multi-source approach to retrospectively estimate daily COVID-19 infections in U.S. states during the Omicron era. Our approach integrates wastewater and seroprevalence surveillance data to improve infection estimates during the Delta-Omicron transition period. These refined estimates, along with wastewater concentration data adjusted for limited coverage, are used to calculate variant-specific shedding rates, which inform daily infection estimates going forward. While case-based estimates tend to exhibit striking volatility, these infection estimates show more stable and interpretable patterns that closely align with Omicron subvariant transitions. Moreover, we directly quantify the degree of underreporting, showing the extent that reported cases significantly underestimate disease burden in a sample of seven U.S. states. In these states, case reports capture less than a quarter of total infections, leaving the vast majority unaccounted for in official reports. Finally, we estimate time-varying effective reproduction numbers and growth rates to provide a more accurate and timely picture of transmission dynamics over the Omicron era in U.S. states.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100889"},"PeriodicalIF":2.4,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078035","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 : 2026-01-22DOI: 10.1016/j.epidem.2026.100891
Erin E. Rees , Mani Sotoodeh , José Denis-Robichaud , Hélène Carabin , Simon de de Montigny
Background
Early warning for known infectious disease threats use methods that focus on detection of outbreaks, often at large geographical scales. However, earlier warning, specifically at the onset of disease emergence (i.e., first case(s)) and at finer spatial scales could significantly improve timeliness and targeting of prevention and control efforts. As a proof-of-concept, we demonstrate that a early classification time-series approach can predict COVID-19 emergence at a local jurisdictional level with a 10-day lead time.
Methods
To predict emergence with a 10-day lead time in Canadian health regions (HRs) during January to November 2020, we developed three classification models. Predictor variables were restricted to information about COVID-19 and included daily metrics at the HR level for social media and traditional EBS data (i.e., news media), and at the provincial/territorial (P/T) level for search engine data. Predictor contributions from neighbouring areas additionally included reported case data (with the other predictors) from the nearest region, or weighted by distance and/or population size of all adjacent regions.
Results
Using the highest performing model, Deep Gated Recurrent Unit, the classification balanced accuracy was higher for distance- and population-based spatial weighting (0.78), than for nearest neighbour data only (0.64). It was also higher when open-access information was included with traditional EBS information (0.78), compared to excluding open-access information (0.63).
Conclusions
In a Canadian context for COVID-19, using a retrospective approach, study results demonstrate classification models can predict emergence with a 10-day lead time at the finest spatial scale of health governance (i.e., HRs) used by P/Ts. Furthermore, prediction accuracy improves with information from neighbouring regions and open-access data (social media, search engine). Implications for operationalizing our method in event-based surveillance systems are discussed.
{"title":"Predicting local COVID-19 emergences: A time-series classification approach and value of data from social media, search engines, and neighbouring regions","authors":"Erin E. Rees , Mani Sotoodeh , José Denis-Robichaud , Hélène Carabin , Simon de de Montigny","doi":"10.1016/j.epidem.2026.100891","DOIUrl":"10.1016/j.epidem.2026.100891","url":null,"abstract":"<div><h3>Background</h3><div>Early warning for known infectious disease threats use methods that focus on detection of outbreaks, often at large geographical scales. However, earlier warning, specifically at the onset of disease emergence (i.e., first case(s)) and at finer spatial scales could significantly improve timeliness and targeting of prevention and control efforts. As a proof-of-concept, we demonstrate that a early classification time-series approach can predict COVID-19 emergence at a local jurisdictional level with a 10-day lead time.</div></div><div><h3>Methods</h3><div>To predict emergence with a 10-day lead time in Canadian health regions (HRs) during January to November 2020, we developed three classification models. Predictor variables were restricted to information about COVID-19 and included daily metrics at the HR level for social media and traditional EBS data (i.e., news media), and at the provincial/territorial (P/T) level for search engine data. Predictor contributions from neighbouring areas additionally included reported case data (with the other predictors) from the nearest region, or weighted by distance and/or population size of all adjacent regions.</div></div><div><h3>Results</h3><div>Using the highest performing model, Deep Gated Recurrent Unit, the classification balanced accuracy was higher for <em>distance-</em> and <em>population-based</em> spatial weighting (0.78), than for nearest neighbour data only (0.64). It was also higher when open-access information was included with traditional EBS information (0.78), compared to excluding open-access information (0.63).</div></div><div><h3>Conclusions</h3><div>In a Canadian context for COVID-19, using a retrospective approach, study results demonstrate classification models can predict emergence with a 10-day lead time at the finest spatial scale of health governance (i.e., HRs) used by P/Ts. Furthermore, prediction accuracy improves with information from neighbouring regions and open-access data (social media, search engine). Implications for operationalizing our method in event-based surveillance systems are discussed.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100891"},"PeriodicalIF":2.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-13DOI: 10.1016/j.epidem.2026.100887
Farrukh A. Chishtie , John Drozd , X. Li , A. Benterki , Sree R. Valluri
This study presents a comprehensive framework for infectious disease monitoring using fractional differential equations, specifically developing the SEIQRDP (Susceptible, Exposed, Infected, Quarantined, Recovered, Deceased, Protected) model. Traditional compartmental models are extended by incorporating fractional calculus, with orders , which provides enhanced flexibility in capturing memory effects and non-local behaviors inherent in disease transmission dynamics. The framework demonstrates improved accuracy when fitted to Canadian COVID-19 data compared to classical integer-order models, with Wave 1 achieving 22.1% improvement (95% CI: 17.4–26.8%) and Wave 2 achieving 6.2% improvement (95% CI: 3.1–9.3%) in predictive accuracy (average 14%). Fractional orders both below and above unity yield superior fits to empirical data depending on epidemic phase, successfully capturing multi-wave dynamics across different pandemic phases. The model incorporates time-dependent parameters to account for varying intervention strategies. Rigorous mathematical analysis including existence, uniqueness, and stability of solutions is provided alongside comprehensive sensitivity analysis. Out-of-sample validation using rolling-origin cross-validation demonstrates robust forecasting performance across 7-, 14-, and 21-day horizons. This research provides public health authorities with an evidence-based tool for epidemic modeling, with proposed extensions for AI-enhanced surveillance, interoperability standards, and Long COVID monitoring discussed as future research directions.
{"title":"A robust compartmental modeling framework for infectious disease monitoring and analysis via fractional differential equations","authors":"Farrukh A. Chishtie , John Drozd , X. Li , A. Benterki , Sree R. Valluri","doi":"10.1016/j.epidem.2026.100887","DOIUrl":"10.1016/j.epidem.2026.100887","url":null,"abstract":"<div><div>This study presents a comprehensive framework for infectious disease monitoring using fractional differential equations, specifically developing the SEIQRDP (Susceptible, Exposed, Infected, Quarantined, Recovered, Deceased, Protected) model. Traditional compartmental models are extended by incorporating fractional calculus, with orders <span><math><mrow><mi>α</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>2</mn><mo>]</mo></mrow></mrow></math></span>, which provides enhanced flexibility in capturing memory effects and non-local behaviors inherent in disease transmission dynamics. The framework demonstrates improved accuracy when fitted to Canadian COVID-19 data compared to classical integer-order models, with Wave 1 achieving 22.1% improvement (95% CI: 17.4–26.8%) and Wave 2 achieving 6.2% improvement (95% CI: 3.1–9.3%) in predictive accuracy (average <span><math><mo>∼</mo></math></span>14%). Fractional orders both below and above unity yield superior fits to empirical data depending on epidemic phase, successfully capturing multi-wave dynamics across different pandemic phases. The model incorporates time-dependent parameters to account for varying intervention strategies. Rigorous mathematical analysis including existence, uniqueness, and stability of solutions is provided alongside comprehensive sensitivity analysis. Out-of-sample validation using rolling-origin cross-validation demonstrates robust forecasting performance across 7-, 14-, and 21-day horizons. This research provides public health authorities with an evidence-based tool for epidemic modeling, with proposed extensions for AI-enhanced surveillance, interoperability standards, and Long COVID monitoring discussed as future research directions.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100887"},"PeriodicalIF":2.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020351","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 : 2026-01-12DOI: 10.1016/j.epidem.2026.100888
Jessica E. Biddle , Stacey House , Jennie H. Kwon , Rachel M. Presti , Stephanie A. Fritz , Tara Curley , Son H. McLaren , Melissa S. Stockwell , Jonathan Schmitz , H. Keipp Talbot , Carlos G. Grijalva , Elie A. Saade , Zainab Albar , Vel Murugan , Rick A. Cruz , Emily T. Martin , Ivana A. Vaughn , Karen J. Wernli , Brianna M. Wickersham , Richard K. Zimmerman , Olivia L. Williams
Household studies play a critical role in estimating influenza transmission parameters, which are essential for real-time modeling of epidemic and pandemic dynamics to inform influenza control strategies. We compared two approaches for estimating household influenza transmission parameters from multisite studies conducted in the United States during the 2023–2024 influenza season: interviewing index cases about illnesses among household contacts (n = 1537 contacts) and prospective enrollment of index cases and their household contacts with systematic, daily symptom assessment and testing (n = 857 contacts). We compared the detection of symptomatic illness, influenza-like illness (ILI; fever and either cough or sore throat), influenza virus infection, and estimated serial illness onset intervals among household contacts across studies. Symptomatic illness episodes among household contacts were identified in 40 % of contacts by index case interview compared to 59 % of contacts from individual daily follow-up. Reports of ILI were more comparable between platforms (20 % vs. 26 % respectively). Index case interviews identified 12 % of household contacts with positive influenza tests while systematic, daily testing in the individual daily follow-up platform identified influenza infection among 44 % of household contacts. Both platforms yielded a median serial interval of 4 days. While index case interviews offer rapid, resource-efficient data collection and can inform epidemiological outcomes such as age-related risks and serial intervals, they substantially underestimate laboratory-confirmed influenza cases compared to systematic daily follow-up. These findings highlight the importance of study design in accurately capturing transmission dynamics and underscore the need for systematic laboratory testing to inform public health responses.
{"title":"Estimating influenza transmission parameters: Comparing two study designs, 2023–2024","authors":"Jessica E. Biddle , Stacey House , Jennie H. Kwon , Rachel M. Presti , Stephanie A. Fritz , Tara Curley , Son H. McLaren , Melissa S. Stockwell , Jonathan Schmitz , H. Keipp Talbot , Carlos G. Grijalva , Elie A. Saade , Zainab Albar , Vel Murugan , Rick A. Cruz , Emily T. Martin , Ivana A. Vaughn , Karen J. Wernli , Brianna M. Wickersham , Richard K. Zimmerman , Olivia L. Williams","doi":"10.1016/j.epidem.2026.100888","DOIUrl":"10.1016/j.epidem.2026.100888","url":null,"abstract":"<div><div>Household studies play a critical role in estimating influenza transmission parameters, which are essential for real-time modeling of epidemic and pandemic dynamics to inform influenza control strategies. We compared two approaches for estimating household influenza transmission parameters from multisite studies conducted in the United States during the 2023–2024 influenza season: interviewing index cases about illnesses among household contacts (n = 1537 contacts) and prospective enrollment of index cases and their household contacts with systematic, daily symptom assessment and testing (n = 857 contacts). We compared the detection of symptomatic illness, influenza-like illness (ILI; fever and either cough or sore throat), influenza virus infection, and estimated serial illness onset intervals among household contacts across studies. Symptomatic illness episodes among household contacts were identified in 40 % of contacts by index case interview compared to 59 % of contacts from individual daily follow-up. Reports of ILI were more comparable between platforms (20 % vs. 26 % respectively). Index case interviews identified 12 % of household contacts with positive influenza tests while systematic, daily testing in the individual daily follow-up platform identified influenza infection among 44 % of household contacts. Both platforms yielded a median serial interval of 4 days. While index case interviews offer rapid, resource-efficient data collection and can inform epidemiological outcomes such as age-related risks and serial intervals, they substantially underestimate laboratory-confirmed influenza cases compared to systematic daily follow-up. These findings highlight the importance of study design in accurately capturing transmission dynamics and underscore the need for systematic laboratory testing to inform public health responses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100888"},"PeriodicalIF":2.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978271","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 : 2026-01-08DOI: 10.1016/j.epidem.2026.100886
Huynh Thi Phuong , Janik Suer , Vitaly Belik , Alejandra Rincón Hidalgo , Andrzej K. Jarynowski , Richard Pastor , Steven Schulz , Ashish Thampi , Chao Xu , Marlli Zambrano , Rafael Mikolajczyk , André Karch , Veronika K. Jaeger , on behalf of OptimAgent Consortium
The parametrisation of contact behaviour is crucial for infectious disease transmission models. Contact information derived from self-reported surveys and from co-location in space and time (GPS-based) may reflect different dimensions of contact behaviour, which might be associated with distinct epidemiological risks depending on the contagion of interest. This study explores whether and how contacts measured using these distinct approaches exhibit similar or complementary contact patterns. We compare the mean number of contacts and the mean excess number of contacts (i.e. the ratio of mean squared contacts to mean contacts) from the COVIMOD survey and NETCHECK GPS co-location data between April 2020 and December 2021. While mean contacts measure contact intensity, mean excess contacts reflect dispersion, which is important for understanding superspreading behaviour. Mean contacts were considerably higher in co-location data (11.04; 95 %CI: 10.90–11.19) than in survey data (3.38; 95 % CI: 3.30–3.47); however, both data sources correlated well with each other. Mean excess contacts were similar during periods of strict non-pharmaceutical interventions (NPIs) but diverged when NPIs were lifted, with co-location data values rising more markedly. Setting-specific contact patterns also differed, potentially due to methodological differences in setting classification and data capture. Furthermore, regional variation was more pronounced in co-location data, with densely populated city-states showing higher contact numbers. Comparative insights from the two data sources demonstrate that GPS-based and survey-based contact data capture complementary and distinct aspects of human interaction. Combining both sources could provide a more comprehensive picture of human interactions relevant to infectious disease modelling.
{"title":"Social contact patterns derived from an epidemiological survey and GPS-based co-location data – A systematic comparison using parallel data collections during the COVID-19 pandemic in Germany","authors":"Huynh Thi Phuong , Janik Suer , Vitaly Belik , Alejandra Rincón Hidalgo , Andrzej K. Jarynowski , Richard Pastor , Steven Schulz , Ashish Thampi , Chao Xu , Marlli Zambrano , Rafael Mikolajczyk , André Karch , Veronika K. Jaeger , on behalf of OptimAgent Consortium","doi":"10.1016/j.epidem.2026.100886","DOIUrl":"10.1016/j.epidem.2026.100886","url":null,"abstract":"<div><div>The parametrisation of contact behaviour is crucial for infectious disease transmission models. Contact information derived from self-reported surveys and from co-location in space and time (GPS-based) may reflect different dimensions of contact behaviour, which might be associated with distinct epidemiological risks depending on the contagion of interest. This study explores whether and how contacts measured using these distinct approaches exhibit similar or complementary contact patterns. We compare the mean number of contacts and the mean excess number of contacts (i.e. the ratio of mean squared contacts to mean contacts) from the COVIMOD survey and NETCHECK GPS co-location data between April 2020 and December 2021. While mean contacts measure contact intensity, mean excess contacts reflect dispersion, which is important for understanding superspreading behaviour. Mean contacts were considerably higher in co-location data (11.04; 95 %CI: 10.90–11.19) than in survey data (3.38; 95 % CI: 3.30–3.47); however, both data sources correlated well with each other. Mean excess contacts were similar during periods of strict non-pharmaceutical interventions (NPIs) but diverged when NPIs were lifted, with co-location data values rising more markedly. Setting-specific contact patterns also differed, potentially due to methodological differences in setting classification and data capture. Furthermore, regional variation was more pronounced in co-location data, with densely populated city-states showing higher contact numbers. Comparative insights from the two data sources demonstrate that GPS-based and survey-based contact data capture complementary and distinct aspects of human interaction. Combining both sources could provide a more comprehensive picture of human interactions relevant to infectious disease modelling.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100886"},"PeriodicalIF":2.4,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1016/j.epidem.2025.100881
Thomas Bayley , Tony Ward , Fabian Sturman , Akashaditya Das , Luca Imeneo , Cliff Kerr , Christophe Fraser , Simon Maskell , Jasmina Panovska-Griffiths
Mathematical modelling with agent-based models (ABMs) has gained popularity during the COVID-19 pandemic, but their complexity makes efficient and robust calibration to data challenging, particularly when applying Bayesian methods to quantify parameter uncertainty. We propose a method for calibrating ABMs that combines a Machine-Learning step with Approximate Bayesian Computation (ML-ABC). We showcase ML-ABC application with a proof-of-principle case study, in which we calibrate the Covasim -a stochastic ABM that has been used to model the English COVID-19 epidemic and inform policy at important junctions. Benchmarking against traditional Rejection-ABC (R-ABC), we illustrate the advantage of ML-ABC application in calibrating Covasim to data on hospitalisations and deaths from COVID-19 during the first and the second COVID-19 epidemic waves of 2020 and early 2021. Across scenarios, we demonstrate that using an ML screening step allows us to derive identical posterior distributions of the calibrated Covasim parameters as with the traditional R-ABC method, but faster. Specifically, we derive posterior distributions for input parameters around 52% faster when calibrating to the first epidemic wave and around 33% faster when calibrating parameters for the second epidemic wave, compared to the traditional R-ABC. Policy modelling requires calibration which is both efficient to adapt to fast-changing pandemic environments and robust to ensure confidence in policy decisions. However, existing ABM calibration often relies on myopic non-exhaustive searches in order to remain tractable, resulting in point parameter estimates. In this preliminary study, ML-ABC strictly improves upon existing ABC calibration approaches in all tested scenarios, indicating its potential to make ABC competitive with point-estimate calibration approaches. This novel approach offers a pathway to effectively calibrate ABMs in a way which is both efficient and quantifies parameter uncertainty, crucial for realising the potential of ABMs for timely and responsively modelling during an emerging epidemic.
{"title":"ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis","authors":"Thomas Bayley , Tony Ward , Fabian Sturman , Akashaditya Das , Luca Imeneo , Cliff Kerr , Christophe Fraser , Simon Maskell , Jasmina Panovska-Griffiths","doi":"10.1016/j.epidem.2025.100881","DOIUrl":"10.1016/j.epidem.2025.100881","url":null,"abstract":"<div><div>Mathematical modelling with agent-based models (ABMs) has gained popularity during the COVID-19 pandemic, but their complexity makes efficient and robust calibration to data challenging, particularly when applying Bayesian methods to quantify parameter uncertainty. We propose a method for calibrating ABMs that combines a Machine-Learning step with Approximate Bayesian Computation (ML-ABC). We showcase ML-ABC application with a proof-of-principle case study, in which we calibrate the Covasim -a stochastic ABM that has been used to model the English COVID-19 epidemic and inform policy at important junctions. Benchmarking against traditional Rejection-ABC (R-ABC), we illustrate the advantage of ML-ABC application in calibrating Covasim to data on hospitalisations and deaths from COVID-19 during the first and the second COVID-19 epidemic waves of 2020 and early 2021. Across scenarios, we demonstrate that using an ML screening step allows us to derive identical posterior distributions of the calibrated Covasim parameters as with the traditional R-ABC method, but faster. Specifically, we derive posterior distributions for input parameters around 52% faster when calibrating to the first epidemic wave and around 33% faster when calibrating parameters for the second epidemic wave, compared to the traditional R-ABC. Policy modelling requires calibration which is both efficient to adapt to fast-changing pandemic environments and robust to ensure confidence in policy decisions. However, existing ABM calibration often relies on myopic non-exhaustive searches in order to remain tractable, resulting in point parameter estimates. In this preliminary study, ML-ABC strictly improves upon existing ABC calibration approaches in all tested scenarios, indicating its potential to make ABC competitive with point-estimate calibration approaches. This novel approach offers a pathway to effectively calibrate ABMs in a way which is both efficient and quantifies parameter uncertainty, crucial for realising the potential of ABMs for timely and responsively modelling during an emerging epidemic.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100881"},"PeriodicalIF":2.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078037","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 : 2025-12-20DOI: 10.1016/j.epidem.2025.100884
Byul Nim Kim , Minchan Choi , Hyosun Lee, Sunmi Lee
Understanding the spatial and temporal dynamics of infectious disease transmission is critical for effective epidemic preparedness and response. COVID-19 transmission is influenced by mobility patterns, regional connectivity, and evolving public health interventions, making it challenging to quantify region-specific transmission risks. Our study integrates intervention-driven analysis, real-world data, and high-resolution modeling to establish a robust computational framework for assessing interregional transmission dynamics. We employ a multi-patch model to estimate the time-dependent regional effective reproduction number and systematically quantify interregional infection contributions. By integrating high-resolution mobility and COVID-19 incidence data from South Korea, we identify key transmission hubs and assess the impact of mobility-driven transmission across different epidemic phases. Our results highlight Seoul and Gyeonggi as dominant sources of interregional spread, with their influence varying across phases of the pandemic. By distinguishing locally transmitted infections from mobility-induced cases, we introduce a data-driven approach to evaluate the effectiveness of movement restrictions and targeted interventions. Findings from the Pre-Delta phase demonstrate that mobility controls in transmission hubs significantly reduced the spread of infections. Our results underscore that densely connected regions disproportionately drive nationwide transmission, emphasizing the need for adaptive, phase-dependent intervention strategies rather than uniform nationwide policies. This study advances computational epidemiology by providing a scalable framework for integrating real-world mobility data with epidemic modeling to inform targeted, data-driven public health responses.
{"title":"Evaluating mobility restrictions through spatiotemporal effective reproduction number analysis in a multi-patch model with complex mobility data","authors":"Byul Nim Kim , Minchan Choi , Hyosun Lee, Sunmi Lee","doi":"10.1016/j.epidem.2025.100884","DOIUrl":"10.1016/j.epidem.2025.100884","url":null,"abstract":"<div><div>Understanding the spatial and temporal dynamics of infectious disease transmission is critical for effective epidemic preparedness and response. COVID-19 transmission is influenced by mobility patterns, regional connectivity, and evolving public health interventions, making it challenging to quantify region-specific transmission risks. Our study integrates intervention-driven analysis, real-world data, and high-resolution modeling to establish a robust computational framework for assessing interregional transmission dynamics. We employ a multi-patch model to estimate the time-dependent regional effective reproduction number and systematically quantify interregional infection contributions. By integrating high-resolution mobility and COVID-19 incidence data from South Korea, we identify key transmission hubs and assess the impact of mobility-driven transmission across different epidemic phases. Our results highlight Seoul and Gyeonggi as dominant sources of interregional spread, with their influence varying across phases of the pandemic. By distinguishing locally transmitted infections from mobility-induced cases, we introduce a data-driven approach to evaluate the effectiveness of movement restrictions and targeted interventions. Findings from the Pre-Delta phase demonstrate that mobility controls in transmission hubs significantly reduced the spread of infections. Our results underscore that densely connected regions disproportionately drive nationwide transmission, emphasizing the need for adaptive, phase-dependent intervention strategies rather than uniform nationwide policies. This study advances computational epidemiology by providing a scalable framework for integrating real-world mobility data with epidemic modeling to inform targeted, data-driven public health responses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100884"},"PeriodicalIF":2.4,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.epidem.2025.100877
Martin S. Wohlfender , Judith A. Bouman , Olga Endrich , Alban Ramette , Alexander B. Leichtle , Guido Beldi , Christian L. Althaus , Julien Riou
During the COVID-19 pandemic, the field of infectious disease modeling advanced rapidly, with forecasting tools developed to track trends in transmission dynamics and anticipate potential shortages of critical resources such as hospital capacity. In this study, we compared short-term forecasting approaches for COVID-19 hospital admissions that generate forecasts one to five weeks ahead, using retrospective electronic health records. We extracted different features (e.g., daily emergency department visits) from an individual-level patient dataset covering six hospitals located in the region of Bern, Switzerland, from February 2020 to June 2023. We then applied five methods – last-observation carried forward (baseline), linear regression, XGBoost and two types of neural networks – to time series using a leave-future-out training scheme with multiple cutting points and optimized hyperparameters. Performance was evaluated using the root mean square error between forecasts and observations. Generally, we found that XGBoost outperformed the other methods in predicting future hospital admissions. Our results also show that adding features such as the number of hospital admissions with fever and augmenting hospital data with measurements of viral concentration in wastewater improves forecast accuracy. This study offers a thorough and systematic comparison of methods applicable to routine hospital data for real-time epidemic forecasting. With the increasing availability and volume of electronic health records, improved forecasting methods will contribute to more precise and timely information during epidemic waves of COVID-19 and other respiratory viruses, thereby strengthening evidence-based public health decision-making.
{"title":"Machine learning-based short-term forecasting of COVID-19 hospital admissions using routine hospital patient data","authors":"Martin S. Wohlfender , Judith A. Bouman , Olga Endrich , Alban Ramette , Alexander B. Leichtle , Guido Beldi , Christian L. Althaus , Julien Riou","doi":"10.1016/j.epidem.2025.100877","DOIUrl":"10.1016/j.epidem.2025.100877","url":null,"abstract":"<div><div>During the COVID-19 pandemic, the field of infectious disease modeling advanced rapidly, with forecasting tools developed to track trends in transmission dynamics and anticipate potential shortages of critical resources such as hospital capacity. In this study, we compared short-term forecasting approaches for COVID-19 hospital admissions that generate forecasts one to five weeks ahead, using retrospective electronic health records. We extracted different features (e.g., daily emergency department visits) from an individual-level patient dataset covering six hospitals located in the region of Bern, Switzerland, from February 2020 to June 2023. We then applied five methods – last-observation carried forward (baseline), linear regression, XGBoost and two types of neural networks – to time series using a leave-future-out training scheme with multiple cutting points and optimized hyperparameters. Performance was evaluated using the root mean square error between forecasts and observations. Generally, we found that XGBoost outperformed the other methods in predicting future hospital admissions. Our results also show that adding features such as the number of hospital admissions with fever and augmenting hospital data with measurements of viral concentration in wastewater improves forecast accuracy. This study offers a thorough and systematic comparison of methods applicable to routine hospital data for real-time epidemic forecasting. With the increasing availability and volume of electronic health records, improved forecasting methods will contribute to more precise and timely information during epidemic waves of COVID-19 and other respiratory viruses, thereby strengthening evidence-based public health decision-making.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100877"},"PeriodicalIF":2.4,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927169","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 : 2025-12-17DOI: 10.1016/j.epidem.2025.100883
Hanna-Tina Fischer , Augustina Koduah
Infectious disease epidemiology is shaped by engrained research cultures that privilege biomedical and quantitative knowledge systems, systematically marginalizing qualitative, contextual, and locally informed approaches. These hierarchies reflect deeper inequities in who leads, who participates, and whose knowledge counts—disparities often patterned along geography, gender, language, and disciplinary background. This perspectives paper examines how funding priorities, academic training, and publishing norms sustain epistemic and structural exclusion, particularly for researchers based in the Global South. Drawing on Ghana’s COVID-19 response, we show how reliance on externally developed epidemiological models mirrored broader marginalization in research authorship, agenda-setting, and decision-making. We argue that equity-focused reforms in funding, training, and publishing—grounded in epistemic and distributive justice—are necessary to transform infectious disease research culture. A more just and inclusive research culture is not only an ethical imperative but essential to the effectiveness and legitimacy of epidemic responses.
{"title":"Whose knowledge counts? Equity, epistemic justice, and reforming infectious disease research culture","authors":"Hanna-Tina Fischer , Augustina Koduah","doi":"10.1016/j.epidem.2025.100883","DOIUrl":"10.1016/j.epidem.2025.100883","url":null,"abstract":"<div><div>Infectious disease epidemiology is shaped by engrained research cultures that privilege biomedical and quantitative knowledge systems, systematically marginalizing qualitative, contextual, and locally informed approaches. These hierarchies reflect deeper inequities in who leads, who participates, and whose knowledge counts—disparities often patterned along geography, gender, language, and disciplinary background. This perspectives paper examines how funding priorities, academic training, and publishing norms sustain epistemic and structural exclusion, particularly for researchers based in the Global South. Drawing on Ghana’s COVID-19 response, we show how reliance on externally developed epidemiological models mirrored broader marginalization in research authorship, agenda-setting, and decision-making. We argue that equity-focused reforms in funding, training, and publishing—grounded in epistemic and distributive justice—are necessary to transform infectious disease research culture. A more just and inclusive research culture is not only an ethical imperative but essential to the effectiveness and legitimacy of epidemic responses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"54 ","pages":"Article 100883"},"PeriodicalIF":2.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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