Pub Date : 2025-09-01Epub Date: 2025-08-07DOI: 10.1016/j.epidem.2025.100850
Jiani Chen , Deven V. Gokhale , Ludy Registre Carmola , Liang Liu , Pejman Rohani , Justin Bahl
RSV and seasonal influenza are two of the most prevalent causes of respiratory infection in the U.S. In this study, we used weekly positive case reports and genetic surveillance data to characterize the circulation of these viruses in the United States between 2011 and 2019 and a mathematical modeling approach to explore their potential interaction at a regional level. Our analyses showed that RSV and seasonal influenza co-circulate with different relative epidemic sizes and seasonal overlaps across regions and seasons. We found that RSV had a different evolutionary dynamic compared to seasonal influenza and that local persistence may play a role in underlying annual epidemics. Our analysis supports a potential competitive interaction between RSV and seasonal influenza in most regions across the United States. The multiple-pathogen modeling framework suggests that cross-immunity following infection of either virus might be one of the key drivers of viral competition. However, this finding is based on model-derived inferences and limited surveillance data; further investigation is needed to confirm its robustness and gain a better understanding of the underlying mechanisms. These findings underscore the importance of continued research into the immunological and ecological mechanisms of viral inference, which might be important for the development of more effective protective strategies against co-circulating respiratory viruses.
{"title":"Characterizing potential interaction between respiratory syncytial virus and seasonal influenza in the U.S.","authors":"Jiani Chen , Deven V. Gokhale , Ludy Registre Carmola , Liang Liu , Pejman Rohani , Justin Bahl","doi":"10.1016/j.epidem.2025.100850","DOIUrl":"10.1016/j.epidem.2025.100850","url":null,"abstract":"<div><div>RSV and seasonal influenza are two of the most prevalent causes of respiratory infection in the U.S. In this study, we used weekly positive case reports and genetic surveillance data to characterize the circulation of these viruses in the United States between 2011 and 2019 and a mathematical modeling approach to explore their potential interaction at a regional level. Our analyses showed that RSV and seasonal influenza co-circulate with different relative epidemic sizes and seasonal overlaps across regions and seasons. We found that RSV had a different evolutionary dynamic compared to seasonal influenza and that local persistence may play a role in underlying annual epidemics. Our analysis supports a potential competitive interaction between RSV and seasonal influenza in most regions across the United States. The multiple-pathogen modeling framework suggests that cross-immunity following infection of either virus might be one of the key drivers of viral competition. However, this finding is based on model-derived inferences and limited surveillance data; further investigation is needed to confirm its robustness and gain a better understanding of the underlying mechanisms. These findings underscore the importance of continued research into the immunological and ecological mechanisms of viral inference, which might be important for the development of more effective protective strategies against co-circulating respiratory viruses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100850"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831576","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 : 2025-09-01Epub Date: 2025-07-18DOI: 10.1016/j.epidem.2025.100842
Katia Koelle , Brooke Lappe , Benjamin A. Lopman , Max S.Y. Lau , Emma Viscidi , Katherine B. Carlson
Norovirus diversity has major implications for vaccine design. The number of circulating genogroups and genotypes, and the way this viral diversity interacts at the population level, will factor into how many and which genotypes should be included in an effective vaccine. Here, we develop an age-stratified, multi-strain model for norovirus to project potential population-level impacts of different vaccine formulations on genotype-specific and overall annual attack rates. Our model assumes that vaccination impacts susceptibility to infection but not infectiousness or the risk of developing disease. We parameterize the baseline model (without vaccination) based on literature estimates and the ability to recover observed epidemiological patterns. We then simulate this model under seven different potential vaccine formulations, initially assuming only pediatric vaccination. While we find that increases in coverage result in declines in annual norovirus attack rates for all formulations considered, we also find that vaccine formulations that include genotype GII.4 would be most effective at lowering overall norovirus attack rates. Inclusion of additional genotypes in a vaccine would further lower attack rates but more incrementally, with the addition of GI.3, GII.2, GII.3, and GII.6 together having a similar impact to that of GII.4 alone on reducing overall norovirus incidence. We further find that transient dynamics are expected for 10-20 years following roll-out with any pediatric vaccine. During this time, there may be unanticipated changes in genotype circulation patterns, although long-term increases in non-vaccine genotype attack rates above baseline levels are not expected. Finally, we anticipate that annual vaccination of older-aged individuals with a GII.4-containing vaccine can, under certain conditions but not others, provide appreciable direct benefits to individuals in this age group beyond what pediatric vaccination affords. Together, our results indicate that there is a clear population-level benefit of primary pediatric vaccination with a GII.4-inclusive norovirus vaccine plus incremental value of other genotypes, with additional direct benefits of annual vaccination to older adults provided that vaccination results in a considerable (multi-month) duration of broadly protective immunity to infection. More empirical studies are needed to validate the structure of the model and refine its parameterization, both of which affect projections of vaccine impact.
{"title":"Projecting the population-level impact of norovirus vaccines","authors":"Katia Koelle , Brooke Lappe , Benjamin A. Lopman , Max S.Y. Lau , Emma Viscidi , Katherine B. Carlson","doi":"10.1016/j.epidem.2025.100842","DOIUrl":"10.1016/j.epidem.2025.100842","url":null,"abstract":"<div><div>Norovirus diversity has major implications for vaccine design. The number of circulating genogroups and genotypes, and the way this viral diversity interacts at the population level, will factor into how many and which genotypes should be included in an effective vaccine. Here, we develop an age-stratified, multi-strain model for norovirus to project potential population-level impacts of different vaccine formulations on genotype-specific and overall annual attack rates. Our model assumes that vaccination impacts susceptibility to infection but not infectiousness or the risk of developing disease. We parameterize the baseline model (without vaccination) based on literature estimates and the ability to recover observed epidemiological patterns. We then simulate this model under seven different potential vaccine formulations, initially assuming only pediatric vaccination. While we find that increases in coverage result in declines in annual norovirus attack rates for all formulations considered, we also find that vaccine formulations that include genotype GII.4 would be most effective at lowering overall norovirus attack rates. Inclusion of additional genotypes in a vaccine would further lower attack rates but more incrementally, with the addition of GI.3, GII.2, GII.3, and GII.6 together having a similar impact to that of GII.4 alone on reducing overall norovirus incidence. We further find that transient dynamics are expected for 10-20 years following roll-out with any pediatric vaccine. During this time, there may be unanticipated changes in genotype circulation patterns, although long-term increases in non-vaccine genotype attack rates above baseline levels are not expected. Finally, we anticipate that annual vaccination of older-aged individuals with a GII.4-containing vaccine can, under certain conditions but not others, provide appreciable direct benefits to individuals in this age group beyond what pediatric vaccination affords. Together, our results indicate that there is a clear population-level benefit of primary pediatric vaccination with a GII.4-inclusive norovirus vaccine plus incremental value of other genotypes, with additional direct benefits of annual vaccination to older adults provided that vaccination results in a considerable (multi-month) duration of broadly protective immunity to infection. More empirical studies are needed to validate the structure of the model and refine its parameterization, both of which affect projections of vaccine impact.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100842"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672569","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 : 2025-09-01Epub 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":"2025-09-01","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 : 2025-09-01Epub Date: 2025-07-01DOI: 10.1016/j.epidem.2025.100841
Hester Korthals Altes , Jan Van De Kassteele , Bram Wisse , Maria Xiridou , Albert Jan Van Hoek , Jacco Wallinga
The monitoring of work absenteeism can inform pandemic decision making, besides the surveillance of disease end-points like mortality and intensive care bed occupancy. For instance, high disease prevalence accompanied by elevated levels of absenteeism in the healthcare sector will increase the strain on the health care system, and may necessitate adaptation of the control measures. This highlights the need to assess the association between COVID-19 disease prevalence and absenteeism in relevant economic sectors. We initiated the comprehensive monitoring and analysis of work absenteeism and developed an autoregressive time series model which combined COVID-19 prevalence as measured through syndromic surveillance, with absenteeism across various economic activity sectors in the Netherlands. The analysis was updated regularly and shared with policy makers. Overall, prevalence of COVID-19-like illnesses was the most important contributor to variation in absenteeism over the period November 2020-May 2023, with absenteeism rates varying markedly between activity sectors. Of the sectors well-covered by the absenteeism database, the Education and Logistics sectors showed the greatest contribution of a seasonal pattern independent of COVID-19 to absenteeism.
{"title":"Work absenteeism across economic activity sectors and its association with COVID-19-like illness prevalence in the Netherlands, 2020–2023","authors":"Hester Korthals Altes , Jan Van De Kassteele , Bram Wisse , Maria Xiridou , Albert Jan Van Hoek , Jacco Wallinga","doi":"10.1016/j.epidem.2025.100841","DOIUrl":"10.1016/j.epidem.2025.100841","url":null,"abstract":"<div><div>The monitoring of work absenteeism can inform pandemic decision making, besides the surveillance of disease end-points like mortality and intensive care bed occupancy. For instance, high disease prevalence accompanied by elevated levels of absenteeism in the healthcare sector will increase the strain on the health care system, and may necessitate adaptation of the control measures. This highlights the need to assess the association between COVID-19 disease prevalence and absenteeism in relevant economic sectors. We initiated the comprehensive monitoring and analysis of work absenteeism and developed an autoregressive time series model which combined COVID-19 prevalence as measured through syndromic surveillance, with absenteeism across various economic activity sectors in the Netherlands. The analysis was updated regularly and shared with policy makers. Overall, prevalence of COVID-19-like illnesses was the most important contributor to variation in absenteeism over the period November 2020-May 2023, with absenteeism rates varying markedly between activity sectors. Of the sectors well-covered by the absenteeism database, the Education and Logistics sectors showed the greatest contribution of a seasonal pattern independent of COVID-19 to absenteeism.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100841"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572646","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 : 2025-09-01Epub Date: 2025-07-14DOI: 10.1016/j.epidem.2025.100840
K. Ken Peng , Charmaine B. Dean , X. Joan Hu , Robert Delatolla
Recent research highlights a strong correlation between COVID-19 hospitalizations and wastewater viral signals. Increases in wastewater viral signals may be early warnings of increases in hospital admissions. That indicates a promising opportunity to assess and predict the burden of infectious diseases and has driven the widespread adoption and development of wastewater monitoring tools by public health organizations. Previous studies utilize distributed lag models to explore associations of COVID-19 hospitalizations with lagged SARS-CoV-2 wastewater viral signals. However, the conventional distributed lag models assume the duration time of the lag to be fixed, which is not always plausible. This paper presents Markov-modulated models with distributed lasting time, treating the duration of the lag as a random variable defined by a hidden process. We evaluate exposure effects over the duration time and estimate the distribution of the lasting time using the wastewater data and COVID-19 hospitalization records from Ottawa, Canada during June 2020 to November 2022. The different COVID-19 pandemic waves are accommodated in the statistical learning. Moreover, two strategies for comparing the associations over different time intervals are exemplified using the Ottawa data. Of note, the proposed Markov modulated models, an extension of distributed lag models, are potentially applicable to many different problems where the lag time is not fixed.
{"title":"Learning associations of COVID-19 hospitalizations with wastewater viral signals by Markov modulated models","authors":"K. Ken Peng , Charmaine B. Dean , X. Joan Hu , Robert Delatolla","doi":"10.1016/j.epidem.2025.100840","DOIUrl":"10.1016/j.epidem.2025.100840","url":null,"abstract":"<div><div>Recent research highlights a strong correlation between COVID-19 hospitalizations and wastewater viral signals. Increases in wastewater viral signals may be early warnings of increases in hospital admissions. That indicates a promising opportunity to assess and predict the burden of infectious diseases and has driven the widespread adoption and development of wastewater monitoring tools by public health organizations. Previous studies utilize distributed lag models to explore associations of COVID-19 hospitalizations with lagged SARS-CoV-2 wastewater viral signals. However, the conventional distributed lag models assume the duration time of the lag to be fixed, which is not always plausible. This paper presents Markov-modulated models with distributed lasting time, treating the duration of the lag as a random variable defined by a hidden process. We evaluate exposure effects over the duration time and estimate the distribution of the lasting time using the wastewater data and COVID-19 hospitalization records from Ottawa, Canada during June 2020 to November 2022. The different COVID-19 pandemic waves are accommodated in the statistical learning. Moreover, two strategies for comparing the associations over different time intervals are exemplified using the Ottawa data. Of note, the proposed Markov modulated models, an extension of distributed lag models, are potentially applicable to many different problems where the lag time is not fixed.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100840"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666047","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 : 2025-09-01Epub Date: 2025-08-06DOI: 10.1016/j.epidem.2025.100848
Pleuni S. Pennings
Drug resistance is a problem in many pathogens. While overall, levels of resistance have risen in recent decades, there are many examples where after an initial rise, levels of resistance have stabilized. The stable coexistence of resistance and susceptibility has proven hard to explain – in most evolutionary models, either resistance or susceptibility ultimately “wins” and takes over the population. Here, we show that a simple model, mathematically akin to mutation-selection balance theory, can explain several key observations about drug resistance: (1) the stable coexistence of resistant and susceptible strains (2) at levels that depend on population-level drug usage and (3) with resistance often due to many different strains (resistance is present on many different genetic backgrounds). The model is applicable to resistance due to both mutations and horizontal gene transfer (HGT). It predicts that new resistant strains should continuously appear (through mutation or HGT and positive selection within treated hosts) and disappear (due to a fitness cost of resistance). The result is that while resistance is stable, which strains carry resistance is constantly changing. We used data from a longitudinal genomic study on E. coli in Norway to test this prediction for resistance to five different drugs and found that, consistent with the model, most resistant strains indeed disappear quickly after they appear in the dataset. Having a model that explains the dynamics of drug resistance will allow us to plan science-backed interventions to reduce the burden of drug resistance.
{"title":"Explaining the stable coexistence of drug-resistant and -susceptible pathogens: the resistance acquisition purifying selection model","authors":"Pleuni S. Pennings","doi":"10.1016/j.epidem.2025.100848","DOIUrl":"10.1016/j.epidem.2025.100848","url":null,"abstract":"<div><div>Drug resistance is a problem in many pathogens. While overall, levels of resistance have risen in recent decades, there are many examples where after an initial rise, levels of resistance have stabilized. The stable coexistence of resistance and susceptibility has proven hard to explain – in most evolutionary models, either resistance or susceptibility ultimately “wins” and takes over the population. Here, we show that a simple model, mathematically akin to mutation-selection balance theory, can explain several key observations about drug resistance: (1) the stable coexistence of resistant and susceptible strains (2) at levels that depend on population-level drug usage and (3) with resistance often due to many different strains (resistance is present on many different genetic backgrounds). The model is applicable to resistance due to both mutations and horizontal gene transfer (HGT). It predicts that new resistant strains should continuously appear (through mutation or HGT and positive selection within treated hosts) and disappear (due to a fitness cost of resistance). The result is that while resistance is stable, <em>which</em> strains carry resistance is constantly changing. We used data from a longitudinal genomic study on <em>E. coli</em> in Norway to test this prediction for resistance to five different drugs and found that, consistent with the model, most resistant strains indeed disappear quickly after they appear in the dataset. Having a model that explains the dynamics of drug resistance will allow us to plan science-backed interventions to reduce the burden of drug resistance.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100848"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907266","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 : 2025-09-01Epub Date: 2025-07-16DOI: 10.1016/j.epidem.2025.100846
Fabrícia F. Nascimento , Sanjay R. Mehta , Susan J. Little , Erik M. Volz
The robustness and statistical efficiency of phylodynamic models have been tested by many investigators. However, little attention has been given to model specification and inductive bias that can occur if the model is misspecified or provides an overly simplistic representation of the evolutionary process. Here, we carried out a study involving the simulation of HIV epidemics using a complex model and calibrated to men who have sex with men from San Diego, USA. We then used this epidemic trajectory to simulate genealogies, sequence alignments equivalent to HIV partial pol gene and the complete genome. We proceeded to estimate migration rates using a simplistic representation of the epidemiological model by testing model-based phylodynamics and phylogeographic methods. We observed that even though there were some biases on the estimates using a simplistic representation of the epidemiological model, we were still able to estimate the migration rates depending on the method and sample size used in the analyses.
{"title":"Robust phylodynamic inference and model specification for HIV transmission dynamics","authors":"Fabrícia F. Nascimento , Sanjay R. Mehta , Susan J. Little , Erik M. Volz","doi":"10.1016/j.epidem.2025.100846","DOIUrl":"10.1016/j.epidem.2025.100846","url":null,"abstract":"<div><div>The robustness and statistical efficiency of phylodynamic models have been tested by many investigators. However, little attention has been given to model specification and inductive bias that can occur if the model is misspecified or provides an overly simplistic representation of the evolutionary process. Here, we carried out a study involving the simulation of HIV epidemics using a complex model and calibrated to men who have sex with men from San Diego, USA. We then used this epidemic trajectory to simulate genealogies, sequence alignments equivalent to HIV partial <em>pol</em> gene and the complete genome. We proceeded to estimate migration rates using a simplistic representation of the epidemiological model by testing model-based phylodynamics and phylogeographic methods. We observed that even though there were some biases on the estimates using a simplistic representation of the epidemiological model, we were still able to estimate the migration rates depending on the method and sample size used in the analyses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100846"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655353","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 : 2025-09-01Epub Date: 2025-07-16DOI: 10.1016/j.epidem.2025.100845
David O’Gara , Cliff C. Kerr , Daniel J. Klein , Mickaël Binois , Roman Garnett , Ross A. Hammond
Advances in computing power and data availability have led to growing sophistication in mechanistic mathematical models of social dynamics. Increasingly these models are used to inform real-world policy decision-making, often with significant time sensitivity. One such modeling approach is agent-based modeling, which offers particular strengths for capturing spatial and behavioral realism, and for in-silico experiments (varying input parameters and assumptions to explore their downstream impact on key outcomes). To be useful in the real-world, these models must be able to qualitatively or quantitatively capture observed empirical phenomena, forming the starting point for subsequent experimentation. One recent example is the COVID-19 pandemic, where epidemiological agent-based models informed policy and response planning worldwide. Throughout, modeling teams often had to spend valuable time and effort aligning their models to data, also known as calibration. Since many agent-based models are computationally intensive, the calibration process constrains the questions and scenarios policymakers may explore in time-sensitive situations. In this paper, we combine history matching, heteroskedastic Gaussian process modeling, and approximate Bayesian computation to address this bottleneck, substantially increasing efficiency and thus widening the range of utility for policy models. We illustrate our approach with a case study using a previously published and widely used epidemiological model, the Covasim model.
{"title":"Improving policy-oriented agent-based modeling with history matching: A case study","authors":"David O’Gara , Cliff C. Kerr , Daniel J. Klein , Mickaël Binois , Roman Garnett , Ross A. Hammond","doi":"10.1016/j.epidem.2025.100845","DOIUrl":"10.1016/j.epidem.2025.100845","url":null,"abstract":"<div><div>Advances in computing power and data availability have led to growing sophistication in mechanistic mathematical models of social dynamics. Increasingly these models are used to inform real-world policy decision-making, often with significant time sensitivity. One such modeling approach is agent-based modeling, which offers particular strengths for capturing spatial and behavioral realism, and for <em>in-silico</em> experiments (varying input parameters and assumptions to explore their downstream impact on key outcomes). To be useful in the real-world, these models must be able to qualitatively or quantitatively capture observed empirical phenomena, forming the starting point for subsequent experimentation. One recent example is the COVID-19 pandemic, where epidemiological agent-based models informed policy and response planning worldwide. Throughout, modeling teams often had to spend valuable time and effort aligning their models to data, also known as calibration. Since many agent-based models are computationally intensive, the calibration process constrains the questions and scenarios policymakers may explore in time-sensitive situations. In this paper, we combine history matching, heteroskedastic Gaussian process modeling, and approximate Bayesian computation to address this bottleneck, substantially increasing efficiency and thus widening the range of utility for policy models. We illustrate our approach with a case study using a previously published and widely used epidemiological model, the Covasim model.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100845"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672567","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 : 2025-09-01Epub Date: 2025-06-18DOI: 10.1016/j.epidem.2025.100839
Dustin T. Hill , Yifan Zhu , Christopher Dunham , E. Joe Moran , Yiquan Zhou , Mary B. Collins , Brittany L. Kmush , David A. Larsen
Background
The effective reproduction number (Rt) is a dynamic indicator of current disease spread risk. Wastewater measurements of viral concentrations are known to correlate with clinical measures of diseases and have been incorporated into methods for estimating the Rt.
Methods
We review wastewater-based methods to estimate the Rt for SARS-CoV-2 based on similarity to the reference case-based Rt, ease of use, and computational requirements. Using wastewater data collected between August 1, 2022, and February 20, 2024, from 205 wastewater treatment plants across New York State, we fit eight wastewater Rt models identified from the literature. Each model is compared to the Rt estimated from case data for New York at the sewershed (wastewater treatment plant catchment area), county, and state levels.
Results
We find a high degree of similarity across all eight methods despite differences in model parameters and approach. Further, two methods based on the common measures of percent change and linear fit reproduced the Rt from case data very well and a GLM accurately predicted case data. Model output varied between spatial scales with some models more closely estimating sewershed Rt values than county Rt values. Similarity to clinical models was also highly correlated with the proportion of the population served by sewer in the surveilled communities (r = 0.77).
Conclusions
While not all methods that estimate Rt from wastewater produce the same results, they all provide a way to incorporate wastewater concentration data into epidemic modeling. Our results show that straightforward measures like the percent change can produce similar results of more complex models. Based on the results, researchers and public health officials can select the method that is best for their situation.
{"title":"Estimating the effective reproduction number from wastewater (Rt): A methods comparison","authors":"Dustin T. Hill , Yifan Zhu , Christopher Dunham , E. Joe Moran , Yiquan Zhou , Mary B. Collins , Brittany L. Kmush , David A. Larsen","doi":"10.1016/j.epidem.2025.100839","DOIUrl":"10.1016/j.epidem.2025.100839","url":null,"abstract":"<div><h3>Background</h3><div>The effective reproduction number (R<sub>t</sub>) is a dynamic indicator of current disease spread risk. Wastewater measurements of viral concentrations are known to correlate with clinical measures of diseases and have been incorporated into methods for estimating the R<sub>t</sub>.</div></div><div><h3>Methods</h3><div>We review wastewater-based methods to estimate the R<sub>t</sub> for SARS-CoV-2 based on similarity to the reference case-based R<sub>t</sub>, ease of use, and computational requirements. Using wastewater data collected between August 1, 2022, and February 20, 2024, from 205 wastewater treatment plants across New York State, we fit eight wastewater R<sub>t</sub> models identified from the literature. Each model is compared to the R<sub>t</sub> estimated from case data for New York at the sewershed (wastewater treatment plant catchment area), county, and state levels.</div></div><div><h3>Results</h3><div>We find a high degree of similarity across all eight methods despite differences in model parameters and approach. Further, two methods based on the common measures of percent change and linear fit reproduced the R<sub>t</sub> from case data very well and a GLM accurately predicted case data. Model output varied between spatial scales with some models more closely estimating sewershed R<sub>t</sub> values than county R<sub>t</sub> values. Similarity to clinical models was also highly correlated with the proportion of the population served by sewer in the surveilled communities (r = 0.77).</div></div><div><h3>Conclusions</h3><div>While not all methods that estimate R<sub>t</sub> from wastewater produce the same results, they all provide a way to incorporate wastewater concentration data into epidemic modeling. Our results show that straightforward measures like the percent change can produce similar results of more complex models. Based on the results, researchers and public health officials can select the method that is best for their situation.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100839"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338640","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 : 2025-09-01Epub Date: 2025-06-09DOI: 10.1016/j.epidem.2025.100838
Rachel Lobay , Ajitesh Srivastava , Ryan J. Tibshirani , Daniel J. McDonald
The timing and magnitude of COVID-19 infections are of interest to the public and to public health, but these are challenging to ascertain due to the volume of undetected asymptomatic cases and reporting delays. Accurate estimates of COVID-19 infections based on finalized data can improve understanding of the pandemic and provide more meaningful quantification of disease patterns and burden. Therefore, we retrospectively estimate daily incident infections for each U.S. state prior to Omicron. To this end, reported COVID-19 cases are deconvolved to their likely date of infection onset using delay distributions estimated from the CDC line list. Then, a novel serology-driven model is used to scale these deconvolved cases to account for the unreported infections. The resulting infection estimates incorporate variant-specific incubation periods, reinfections, and waning antigenic immunity. They clearly demonstrate that reported cases failed to reflect the full extent of disease burden in all states. Most notably, infections were severely underreported during the Delta wave, with an estimated reporting rate as low as 6.3% in New Jersey, 7.3% in Maryland, and 8.4% in Nevada. Moreover, in 44 states, fewer than 1/3 of infections eventually appeared as case reports, and there were sustained periods where surges in infections were virtually undetectable through reported cases. This pattern was clearly illustrated by North and South Dakota during the spring of 2021, as well as by several Northeastern states during the Delta wave of late summer that year. While reported cases offered a convenient proxy of disease burden, they failed to capture the full extent of infections and severely underestimated the true disease burden. Our retrospective analysis also estimates other important quantities for every state, including variant-specific deconvolved cases, time-varying case ascertainment ratios, as well as infection-hospitalization and infection-fatality ratios.
{"title":"Incident COVID-19 infections before Omicron in the U.S.","authors":"Rachel Lobay , Ajitesh Srivastava , Ryan J. Tibshirani , Daniel J. McDonald","doi":"10.1016/j.epidem.2025.100838","DOIUrl":"10.1016/j.epidem.2025.100838","url":null,"abstract":"<div><div>The timing and magnitude of COVID-19 infections are of interest to the public and to public health, but these are challenging to ascertain due to the volume of undetected asymptomatic cases and reporting delays. Accurate estimates of COVID-19 infections based on finalized data can improve understanding of the pandemic and provide more meaningful quantification of disease patterns and burden. Therefore, we retrospectively estimate daily incident infections for each U.S. state prior to Omicron. To this end, reported COVID-19 cases are deconvolved to their likely date of infection onset using delay distributions estimated from the CDC line list. Then, a novel serology-driven model is used to scale these deconvolved cases to account for the unreported infections. The resulting infection estimates incorporate variant-specific incubation periods, reinfections, and waning antigenic immunity. They clearly demonstrate that reported cases failed to reflect the full extent of disease burden in all states. Most notably, infections were severely underreported during the Delta wave, with an estimated reporting rate as low as 6.3% in New Jersey, 7.3% in Maryland, and 8.4% in Nevada. Moreover, in 44 states, fewer than 1/3 of infections eventually appeared as case reports, and there were sustained periods where surges in infections were virtually undetectable through reported cases. This pattern was clearly illustrated by North and South Dakota during the spring of 2021, as well as by several Northeastern states during the Delta wave of late summer that year. While reported cases offered a convenient proxy of disease burden, they failed to capture the full extent of infections and severely underestimated the true disease burden. Our retrospective analysis also estimates other important quantities for every state, including variant-specific deconvolved cases, time-varying case ascertainment ratios, as well as infection-hospitalization and infection-fatality ratios.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100838"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289026","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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