Pub Date : 2025-12-01Epub Date: 2025-11-11DOI: 10.1016/j.epidem.2025.100866
Jason Olejarz , Till Hoffmann , Alex Zapf , Douaa Mugahid , Ross Molinaro , Chadwick Brown , Artem Boltyenkov , Taras Dudykevych , Ankit Gupta , Marc Lipsitch , Rifat Atun , Jukka-Pekka Onnela , Sarah Fortune , Rangarajan Sampath , Yonatan H. Grad
Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost, , whenever the system is abnormal and no action is taken, or an immediate cost, , with action, where . If action costs are too high, then surveillance is detrimental, and intervention should never occur. If action costs are sufficiently low, then surveillance is detrimental, and intervention should always occur. Only when action costs are intermediate and surveillance costs are sufficiently low is surveillance beneficial. Our equations provide a framework for assessing which approach may apply under a range of scenarios and, if surveillance is warranted, facilitate methodical classification of intervention strategies. Our model thus offers a conceptual basis for designing real-world public health surveillance systems.
{"title":"A binary prototype for time-series surveillance and intervention","authors":"Jason Olejarz , Till Hoffmann , Alex Zapf , Douaa Mugahid , Ross Molinaro , Chadwick Brown , Artem Boltyenkov , Taras Dudykevych , Ankit Gupta , Marc Lipsitch , Rifat Atun , Jukka-Pekka Onnela , Sarah Fortune , Rangarajan Sampath , Yonatan H. Grad","doi":"10.1016/j.epidem.2025.100866","DOIUrl":"10.1016/j.epidem.2025.100866","url":null,"abstract":"<div><div>Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost, <span><math><mi>c</mi></math></span>, whenever the system is abnormal and no action is taken, or an immediate cost, <span><math><mi>k</mi></math></span>, with action, where <span><math><mrow><mi>k</mi><mo><</mo><mi>c</mi></mrow></math></span>. If action costs are too high, then surveillance is detrimental, and intervention should never occur. If action costs are sufficiently low, then surveillance is detrimental, and intervention should always occur. Only when action costs are intermediate and surveillance costs are sufficiently low is surveillance beneficial. Our equations provide a framework for assessing which approach may apply under a range of scenarios and, if surveillance is warranted, facilitate methodical classification of intervention strategies. Our model thus offers a conceptual basis for designing real-world public health surveillance systems.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100866"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528693","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: 2025-11-28DOI: 10.1016/j.epidem.2025.100873
Ofer Cornfeld , Kaicheng Niu , Oded Neeman , Michael Roswell , Gabi Steinbach , Stephen J. Beckett , Yorai Wardi , Joshua S. Weitz , Eran Yashiv
Infectious disease outbreaks with pandemic potential present challenges for mitigation and control. Policymakers must reduce disease-associated morbidity and mortality while also minimizing socioeconomic costs of interventions. At present, robust decision frameworks that integrate epidemic and macroeconomic dynamics to inform policy choices, given uncertainty in the current and future state of the outbreak and economic activity, are not widely available. In this study, we propose and analyze an economic-epidemic model to identify robust planning policies that limit epidemic impacts while maintaining economic activity. We compare alternative fixed, dynamic open-loop optimal control, and feedback control policies via a welfare loss framework. We find that open-loop policies that adjust employment dynamically while maintaining a flat epidemic curve outperform fixed employment reduction policies. However, open-loop policies are highly sensitive to misestimation of parameters associated with intrinsic disease strength and feedback between economic activity and transmission, leading to potentially significant increases in welfare loss. In contrast, feedback control policies guided by open-loop dynamical targets of the time-varying reproduction number perform near-optimally when parameters are well-estimated, while significantly outperforming open-loop policies whenever disease transmission and population-scale behavioral response parameters are misestimated — as they inevitably are. Our study provides a template for integrating principled economic models with epidemic scenarios to identify policy vulnerabilities and expand policy options in preparation for future pandemics. Across disease scenarios, we show that policies that temporarily limit economic activity and disease transmission reduce both disease-driven mortality and cumulative loss of economic activity. Our study suggests that future preparedness depends on feasible, robust, and adaptive policies and can help avoid false dichotomies in choosing between public health and economic outcomes.
{"title":"Integrating macroeconomic and public health impacts in social planning policies for pandemic response","authors":"Ofer Cornfeld , Kaicheng Niu , Oded Neeman , Michael Roswell , Gabi Steinbach , Stephen J. Beckett , Yorai Wardi , Joshua S. Weitz , Eran Yashiv","doi":"10.1016/j.epidem.2025.100873","DOIUrl":"10.1016/j.epidem.2025.100873","url":null,"abstract":"<div><div>Infectious disease outbreaks with pandemic potential present challenges for mitigation and control. Policymakers must reduce disease-associated morbidity and mortality while also minimizing socioeconomic costs of interventions. At present, robust decision frameworks that integrate epidemic and macroeconomic dynamics to inform policy choices, given uncertainty in the current and future state of the outbreak and economic activity, are not widely available. In this study, we propose and analyze an economic-epidemic model to identify robust planning policies that limit epidemic impacts while maintaining economic activity. We compare alternative fixed, dynamic open-loop optimal control, and feedback control policies via a welfare loss framework. We find that open-loop policies that adjust employment dynamically while maintaining a flat epidemic curve outperform fixed employment reduction policies. However, open-loop policies are highly sensitive to misestimation of parameters associated with intrinsic disease strength and feedback between economic activity and transmission, leading to potentially significant increases in welfare loss. In contrast, feedback control policies guided by open-loop dynamical targets of the time-varying reproduction number perform near-optimally when parameters are well-estimated, while significantly outperforming open-loop policies whenever disease transmission and population-scale behavioral response parameters are misestimated — as they inevitably are. Our study provides a template for integrating principled economic models with epidemic scenarios to identify policy vulnerabilities and expand policy options in preparation for future pandemics. Across disease scenarios, we show that policies that temporarily limit economic activity and disease transmission reduce both disease-driven mortality and cumulative loss of economic activity. Our study suggests that future preparedness depends on feasible, robust, and adaptive policies and can help avoid false dichotomies in choosing between public health and economic outcomes.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100873"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694157","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: 2025-08-08DOI: 10.1016/j.epidem.2025.100851
Jana S. Huisman , Shotaro Torii , Htet Kyi Wynn , Charles Gan , Irene K. Voellmy , Michael Huber , Timothy R. Julian , Tamar Kohn
Noroviruses and enteroviruses are major causes of endemic gastrointestinal disease associated with substantial disease burden. However, viral gastroenteritis is often diagnosed based on symptoms, with etiology infrequently tested or reported, so little information exists on community-level transmission dynamics. In this study, we demonstrate that norovirus (NoV) genogroup II and enterovirus (EV) viral loads in wastewater reveal transmission dynamics of these viruses. We report NoV and EV concentrations in wastewater from 363 samples between December 5 2020 and October 10 2022 (sampled every second day). Virus concentrations in wastewater were low during 2021, and increased in 2022. Wastewater recapitulated periods of increased clinical cases, and also identified silent waves of transmission. We used the measured wastewater loads to estimate the effective reproductive number (Re). The Re for both NoV and EV peaked between 1.1 and 1.2. However, the usual seasonality of NoV transmission was upended by non-pharmaceutical interventions implemented to mitigate the COVID-19 pandemic, leading to correlated transmission dynamics of NoV GII and EV during 2021–2022. This highlights the use of wastewater to understand transmission dynamics of endemic enteric viruses and estimate relevant epidemiological parameters, including Re.
{"title":"Transmission dynamics of Norovirus GII and Enterovirus in Switzerland during the COVID-19 pandemic (2021–2022) as evidenced in wastewater","authors":"Jana S. Huisman , Shotaro Torii , Htet Kyi Wynn , Charles Gan , Irene K. Voellmy , Michael Huber , Timothy R. Julian , Tamar Kohn","doi":"10.1016/j.epidem.2025.100851","DOIUrl":"10.1016/j.epidem.2025.100851","url":null,"abstract":"<div><div>Noroviruses and enteroviruses are major causes of endemic gastrointestinal disease associated with substantial disease burden. However, viral gastroenteritis is often diagnosed based on symptoms, with etiology infrequently tested or reported, so little information exists on community-level transmission dynamics. In this study, we demonstrate that norovirus (NoV) genogroup II and enterovirus (EV) viral loads in wastewater reveal transmission dynamics of these viruses. We report NoV and EV concentrations in wastewater from 363 samples between December 5 2020 and October 10 2022 (sampled every second day). Virus concentrations in wastewater were low during 2021, and increased in 2022. Wastewater recapitulated periods of increased clinical cases, and also identified silent waves of transmission. We used the measured wastewater loads to estimate the effective reproductive number (R<sub>e</sub>). The R<sub>e</sub> for both NoV and EV peaked between 1.1 and 1.2. However, the usual seasonality of NoV transmission was upended by non-pharmaceutical interventions implemented to mitigate the COVID-19 pandemic, leading to correlated transmission dynamics of NoV GII and EV during 2021–2022. This highlights the use of wastewater to understand transmission dynamics of endemic enteric viruses and estimate relevant epidemiological parameters, including R<sub>e</sub>.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100851"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879110","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}
Seasonal influenza causes on average 425,000 hospitalizations and 32,000 deaths per year in the United States. Forecasts of influenza-like illness (ILI) — a surrogate for the proportion of patients infected with influenza — support public health decision making. The goal of an ensemble forecast of ILI is to increase accuracy and calibration compared to individual forecasts and to provide a single, cohesive prediction of future influenza. However, an ensemble may be composed of models that produce similar forecasts, causing issues with ensemble forecast performance and non-identifiability. To improve upon the above issues we propose a novel Cluster-Aggregate-Pool or ‘CAP’ ensemble algorithm that first groups together individual forecasts into clusters, aggregates forecasts that belong to the same cluster into a single forecast (called a cluster forecast), and then pools together cluster forecasts via a linear pool. We evaluated this algorithm on a benchmark dataset of 7 seasons of ILI plus forecasts generated by 27 individual models as part of the FluSight project. When compared to a non-CAP approach, we find that a CAP ensemble improves calibration by approximately 10% while maintaining similar accuracy to non-CAP alternatives. In addition, our CAP algorithm (i) generalizes past ensemble work associated with influenza forecasting and introduces a framework for future ensemble work, (ii) automatically accounts for missing forecasts from individual models, (iii) allows public health officials to participate in the ensemble by assigning individual models to clusters, and (iv) provide an additional signal about when peak influenza may be near.
{"title":"A Cluster-Aggregate-Pool (CAP) ensemble algorithm for improved forecast performance of influenza-like illness","authors":"Ningxi Wei , Xinze Zhou , Wei-Min Huang , Thomas McAndrew","doi":"10.1016/j.epidem.2025.100832","DOIUrl":"10.1016/j.epidem.2025.100832","url":null,"abstract":"<div><div>Seasonal influenza causes on average 425,000 hospitalizations and 32,000 deaths per year in the United States. Forecasts of influenza-like illness (ILI) — a surrogate for the proportion of patients infected with influenza — support public health decision making. The goal of an ensemble forecast of ILI is to increase accuracy and calibration compared to individual forecasts and to provide a single, cohesive prediction of future influenza. However, an ensemble may be composed of models that produce similar forecasts, causing issues with ensemble forecast performance and non-identifiability. To improve upon the above issues we propose a novel Cluster-Aggregate-Pool or ‘CAP’ ensemble algorithm that first groups together individual forecasts into clusters, aggregates forecasts that belong to the same cluster into a single forecast (called a cluster forecast), and then pools together cluster forecasts via a linear pool. We evaluated this algorithm on a benchmark dataset of 7 seasons of ILI plus forecasts generated by 27 individual models as part of the FluSight project. When compared to a non-CAP approach, we find that a CAP ensemble improves calibration by approximately 10% while maintaining similar accuracy to non-CAP alternatives. In addition, our CAP algorithm (i) generalizes past ensemble work associated with influenza forecasting and introduces a framework for future ensemble work, (ii) automatically accounts for missing forecasts from individual models, (iii) allows public health officials to participate in the ensemble by assigning individual models to clusters, and (iv) provide an additional signal about when peak influenza may be near.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100832"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297581","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.2025.100849
Gavrila A. Puspitarani , Yan-Shin Jackson Liao , Reinhard Fuchs , Amélie Desvars-Larrive
Traceability of animal movements and robust surveillance are crucial for prevention and control of animal diseases. While network analysis has emerged as a powerful tool for identifying higher-risk holdings through centrality metrics, its effectiveness depends on two methodological choices: (1) edge-weighting schemes (movement frequency vs. animal volume) and (2) centrality metric selection. This study investigates how alternative edge-weighting approaches (frequency vs. volume) influence network topology and node centrality rankings in a pig movement network.
Using 2021 pig movement data from Upper Austria (5,766 holdings; 92,914 movements), we: (1) quantify how edge-weighting schemes (frequency vs. volume) affect network topology and community structure, and (2) evaluate node ranking robustness across three centrality metrics (strength, betweenness, closeness) against epidemic simulation rankings. Our analysis reveals distinct edge weight distributions: frequency-based network exhibited a bimodal pattern, while volume-based was more uniform. We observed strong positive correlations ( 0.42–0.84; ) in node rankings across all centrality metrics (strength, closeness, betweenness), with consistent patterns observed both: (i) between frequency- and volume-weighted networks, and (ii) within each network representation. Strength centrality exhibited the highest correlation with the simulation-based rankings, particularly for the top 5% highest-ranked nodes ( 0.51 for frequency-based and 0.5 for volume-based). These findings highlight that strength centrality provides a computationally efficient and field-practical alternative to epidemic simulations for identifying high-risk holdings. This enables resource-efficient, data-driven surveillance while maintaining epidemiological relevance.
{"title":"Investigating the impact of edge weight selection on the pig trade network topology","authors":"Gavrila A. Puspitarani , Yan-Shin Jackson Liao , Reinhard Fuchs , Amélie Desvars-Larrive","doi":"10.1016/j.epidem.2025.100849","DOIUrl":"10.1016/j.epidem.2025.100849","url":null,"abstract":"<div><div>Traceability of animal movements and robust surveillance are crucial for prevention and control of animal diseases. While network analysis has emerged as a powerful tool for identifying higher-risk holdings through centrality metrics, its effectiveness depends on two methodological choices: (1) edge-weighting schemes (movement frequency vs. animal volume) and (2) centrality metric selection. This study investigates how alternative edge-weighting approaches (frequency vs. volume) influence network topology and node centrality rankings in a pig movement network.</div><div>Using 2021 pig movement data from Upper Austria (5,766 holdings; 92,914 movements), we: (1) quantify how edge-weighting schemes (frequency vs. volume) affect network topology and community structure, and (2) evaluate node ranking robustness across three centrality metrics (strength, betweenness, closeness) against epidemic simulation rankings. Our analysis reveals distinct edge weight distributions: frequency-based network exhibited a bimodal pattern, while volume-based was more uniform. We observed strong positive correlations (<span><math><mi>τ</mi></math></span> <span><math><mo>></mo></math></span> 0.42–0.84; <span><math><mrow><mi>p</mi><mo><</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>) in node rankings across all centrality metrics (strength, closeness, betweenness), with consistent patterns observed both: (i) between frequency- and volume-weighted networks, and (ii) within each network representation. Strength centrality exhibited the highest correlation with the simulation-based rankings, particularly for the top 5% highest-ranked nodes (<span><math><mrow><mi>τ</mi><mi>b</mi></mrow></math></span> <span><math><mo>=</mo></math></span> 0.51 for frequency-based and <span><math><mrow><mi>τ</mi><mi>b</mi></mrow></math></span> <span><math><mo>=</mo></math></span> 0.5 for volume-based). These findings highlight that strength centrality provides a computationally efficient and field-practical alternative to epidemic simulations for identifying high-risk holdings. This enables resource-efficient, data-driven surveillance while maintaining epidemiological relevance.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100849"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-07-22DOI: 10.1016/j.epidem.2025.100843
Jingsi Xu , Martín López-García , Thomas House , Ian Hall
Interpreting the viral mechanism of SARS-CoV-2 based on the human body level is critical for developing more efficient interventions. Due to the limitation of data, limited models consider the viral dynamics of the early phase of infection. The Human Challenge Study (Killingley et al., 2022) enables us to obtain data from inoculation to the 14th day after infection, which provides an overview of the dynamics of SARS-CoV-2 infection within the host. In the Human Challenge Study, each volunteer was inoculated with 10TCID50, approximately 55PFU, of a wild-type of virus (Killingley et al., 2022), and the data indicates that the viral load reduced below the detectable level within a day.
The simplified within-host models developed by Xu et al. (2023) explain the data from the Human Challenge Study (Killingley et al., 2022). However, they do not explain the viral decay from Day 0 to Day 1. Hence, in this paper, we aim to develop a new viral mechanism to explain this phenomenon. Based on the simplified within-host models developed by Xu et al. (2023), we consider that the virus will first go through an adjustment phase and then start to replicate. A new dose-response model is developed to evaluate the probability of infection by constructing a boundary problem. We will discuss this viral mechanism and fit the model to the data of the Human Challenge Study (Killingley et al., 2022) by adopting AMC-SMC (approximate Bayesian computation-sequential Monte Carlo). Based on the results of parameter inference, we estimate that the adjusted viral load is around 1% of the inoculated viral load.
基于人体水平解释SARS-CoV-2的病毒机制对于制定更有效的干预措施至关重要。由于数据的限制，有限的模型考虑了感染早期的病毒动力学。人类挑战研究（Killingley et al., 2022）使我们能够获得从接种到感染后第14天的数据，从而概述了宿主内SARS-CoV-2感染的动态。在人类挑战研究中，每位志愿者接种了10TCID50，约55PFU的野生型病毒（Killingley et al., 2022），数据表明病毒载量在一天内降至可检测水平以下。Xu等人（2023）开发的简化宿主内模型解释了人类挑战研究（Killingley等人，2022）的数据。然而，它们并不能解释病毒从第0天到第1天的衰减。因此，在本文中，我们的目标是建立一个新的病毒机制来解释这一现象。根据Xu等人（2023）开发的简化宿主内模型，我们认为病毒将首先经历一个调整阶段，然后开始复制。建立了一种新的剂量-反应模型，通过构造边界问题来评估感染的概率。我们将讨论这种病毒机制，并通过采用AMC-SMC（近似贝叶斯计算-顺序蒙特卡罗）将模型拟合到人类挑战研究（Killingley等人，2022）的数据中。根据参数推断的结果，我们估计调整后的病毒载量约为接种病毒载量的1%。
{"title":"Modelling the dynamics of SARS-CoV-2 during the first 14 days of infection","authors":"Jingsi Xu , Martín López-García , Thomas House , Ian Hall","doi":"10.1016/j.epidem.2025.100843","DOIUrl":"10.1016/j.epidem.2025.100843","url":null,"abstract":"<div><div>Interpreting the viral mechanism of SARS-CoV-2 based on the human body level is critical for developing more efficient interventions. Due to the limitation of data, limited models consider the viral dynamics of the early phase of infection. The Human Challenge Study (Killingley et al., 2022) enables us to obtain data from inoculation to the 14th day after infection, which provides an overview of the dynamics of SARS-CoV-2 infection within the host. In the Human Challenge Study, each volunteer was inoculated with 10TCID50, approximately 55PFU, of a wild-type of virus (Killingley et al., 2022), and the data indicates that the viral load reduced below the detectable level within a day.</div><div>The simplified within-host models developed by Xu et al. (2023) explain the data from the Human Challenge Study (Killingley et al., 2022). However, they do not explain the viral decay from Day 0 to Day 1. Hence, in this paper, we aim to develop a new viral mechanism to explain this phenomenon. Based on the simplified within-host models developed by Xu et al. (2023), we consider that the virus will first go through an adjustment phase and then start to replicate. A new dose-response model is developed to evaluate the probability of infection by constructing a boundary problem. We will discuss this viral mechanism and fit the model to the data of the Human Challenge Study (Killingley et al., 2022) by adopting AMC-SMC (approximate Bayesian computation-sequential Monte Carlo). Based on the results of parameter inference, we estimate that the adjusted viral load is around 1% of the inoculated viral load.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100843"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781205","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: 2025-06-06DOI: 10.1016/j.epidem.2025.100833
Jessica R. Conrad , Paul W. Fenimore , Kelly R. Moran , Marisa C. Eisenberg
In the face of the COVID-19 pandemic, the literature saw a spike in publications for epidemic models, and a renewed interest in capturing contact networks and geographic movement of populations. There remains a general lack of consensus in the modeling community around best practices for spatiotemporal epi-modeling, specifically as it pertains to the infection rate formulation and the underlying contact or mixing model.
We mathematically verify several common modeling assumptions in the literature, to prove when certain choices can provide consistent results across different geographic resolutions, population densities and patterns, and mixing assumptions. The most common infection rate formulation, a computationally low cost per capita infection rate assumption, fails the consistency tests for heterogeneous populations and gravity-weighting assumptions. Future modeling efforts in spatiotemporal disease modeling should be wary of this limitation, particularly when working with more heterogeneous or sparse populations.
Our results provide guidance for testing that a model preserves desirable properties even when model inputs mask potential problems due to symmetry or homogeneity. We also provide a recipe for performing this type of verification, strengthening decision support tools.
{"title":"Verifying infectious disease scenario planning for geographically diverse populations","authors":"Jessica R. Conrad , Paul W. Fenimore , Kelly R. Moran , Marisa C. Eisenberg","doi":"10.1016/j.epidem.2025.100833","DOIUrl":"10.1016/j.epidem.2025.100833","url":null,"abstract":"<div><div>In the face of the COVID-19 pandemic, the literature saw a spike in publications for epidemic models, and a renewed interest in capturing contact networks and geographic movement of populations. There remains a general lack of consensus in the modeling community around best practices for spatiotemporal epi-modeling, specifically as it pertains to the infection rate formulation and the underlying contact or mixing model.</div><div>We mathematically verify several common modeling assumptions in the literature, to prove when certain choices can provide consistent results across different geographic resolutions, population densities and patterns, and mixing assumptions. The most common infection rate formulation, a computationally low cost <em>per capita</em> infection rate assumption, fails the consistency tests for heterogeneous populations and gravity-weighting assumptions. Future modeling efforts in spatiotemporal disease modeling should be wary of this limitation, particularly when working with more heterogeneous or sparse populations.</div><div>Our results provide guidance for testing that a model preserves desirable properties even when model inputs mask potential problems due to symmetry or homogeneity. We also provide a recipe for performing this type of verification, strengthening decision support tools.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100833"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501024","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: 2025-08-19DOI: 10.1016/j.epidem.2025.100847
Dhorasso Temfack, Jason Wyse
Effective epidemic modeling and surveillance require computationally efficient methods that can continuously update parameter estimates as new data becomes available. This paper explores the application of an online variant of Sequential Monte Carlo Squared (O-SMC) to the stochastic Susceptible–Exposed–Infectious–Removed (SEIR) model for real-time epidemic tracking. The advantage of O-SMC lies in its ability to update parameter estimates using a particle Metropolis–Hastings kernel by only utilizing a fixed window of recent observations. This feature enables timely parameter updates and significantly enhances computational efficiency compared to standard SMC, which requires processing all past observations. First, we demonstrate the efficiency of O-SMC on simulated epidemic data, where both the true parameter values and the observation process are known. We then make an application to a real-world COVID-19 dataset from Ireland, successfully tracking the epidemic and estimating a time-dependent reproduction number of the disease. Our results show that O-SMC provides accurate online estimates of both static and dynamic epidemiological parameters while substantially reducing computational cost. These findings highlight the potential of O-SMC for real-time epidemic monitoring and supporting adaptive public health interventions.
{"title":"Sequential Monte Carlo Squared for online inference in stochastic epidemic models","authors":"Dhorasso Temfack, Jason Wyse","doi":"10.1016/j.epidem.2025.100847","DOIUrl":"10.1016/j.epidem.2025.100847","url":null,"abstract":"<div><div>Effective epidemic modeling and surveillance require computationally efficient methods that can continuously update parameter estimates as new data becomes available. This paper explores the application of an online variant of Sequential Monte Carlo Squared (O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>) to the stochastic Susceptible–Exposed–Infectious–Removed (SEIR) model for real-time epidemic tracking. The advantage of O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> lies in its ability to update parameter estimates using a particle Metropolis–Hastings kernel by only utilizing a fixed window of recent observations. This feature enables timely parameter updates and significantly enhances computational efficiency compared to standard SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, which requires processing all past observations. First, we demonstrate the efficiency of O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> on simulated epidemic data, where both the true parameter values and the observation process are known. We then make an application to a real-world COVID-19 dataset from Ireland, successfully tracking the epidemic and estimating a time-dependent reproduction number of the disease. Our results show that O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> provides accurate online estimates of both static and dynamic epidemiological parameters while substantially reducing computational cost. These findings highlight the potential of O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> for real-time epidemic monitoring and supporting adaptive public health interventions.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100847"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-07-09DOI: 10.1016/j.epidem.2025.100844
Tianlong Yang , Xunbo Du , Junfan Li , Tin Zhang , Yao Wang , Liang Wang
Background
Scarlet fever (SF) is an acute infectious disease that poses a significant public health threat; however, its transmission dynamics, particularly the impact of asymptomatic carriers and socioeconomic determinants, remain unclear.
Methods
We developed a susceptible–exposed–infectious–asymptomatic–recovered (SEIAR) model that incorporates asymptomatic infections to estimate the time-varying reproduction number (Rt) for SF in Chengdu (2005–2019) using local epidemiological data. The model was evaluated using the coefficient of determination (R²), and sensitivity analysis confirmed its robustness. We further integrated Boruta, Extreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP) to systematically assess the influence of socioeconomic variables on Rt.
Results
Between 2005 and 2019, Chengdu reported 11,499 cases of SF, with an average incidence of 4.87 per 100,000. Two distinct seasonal peaks occurred in April–May and November–December, and incidence rates were notably lower during school holidays. The majority of cases affected children aged 3–7, with a male-to-female ratio of 1.59:1. In addition, core districts such as Wuhou and Xindu exhibited the highest incidence. The SEIAR model demonstrated strong predictive performance (overall R² = 0.831, P < 0.001) and estimated a median Rt of 0.963; however, several regions exceeded this threshold, with Rt peaking approximately two months prior to incidence surges. Spatial analyses revealed significant clustering in central urban areas, and integrated socioeconomic analysis identified the one-child rate as the primary driver of Rt, followed by population density and healthcare facility density (P < 0.01).
Conclusion
By integrating epidemiological data with socioeconomic factors, this study quantitatively elucidates the transmission characteristics of SF in Chengdu, providing data-driven support for monitoring and targeted intervention strategies in the absence of vaccination.
{"title":"Modeling transmission dynamics and socio-economic determinants of scarlet fever in Chengdu, China: An integrated SEIAR and machine learning approach","authors":"Tianlong Yang , Xunbo Du , Junfan Li , Tin Zhang , Yao Wang , Liang Wang","doi":"10.1016/j.epidem.2025.100844","DOIUrl":"10.1016/j.epidem.2025.100844","url":null,"abstract":"<div><h3>Background</h3><div>Scarlet fever (SF) is an acute infectious disease that poses a significant public health threat; however, its transmission dynamics, particularly the impact of asymptomatic carriers and socioeconomic determinants, remain unclear.</div></div><div><h3>Methods</h3><div>We developed a susceptible–exposed–infectious–asymptomatic–recovered (SEIAR) model that incorporates asymptomatic infections to estimate the time-varying reproduction number (<em>R</em><sub><em>t</em></sub>) for SF in Chengdu (2005–2019) using local epidemiological data. The model was evaluated using the coefficient of determination (<em>R</em>²), and sensitivity analysis confirmed its robustness. We further integrated Boruta, Extreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP) to systematically assess the influence of socioeconomic variables on <em>R</em><sub><em>t</em></sub>.</div></div><div><h3>Results</h3><div>Between 2005 and 2019, Chengdu reported 11,499 cases of SF, with an average incidence of 4.87 per 100,000. Two distinct seasonal peaks occurred in April–May and November–December, and incidence rates were notably lower during school holidays. The majority of cases affected children aged 3–7, with a male-to-female ratio of 1.59:1. In addition, core districts such as Wuhou and Xindu exhibited the highest incidence. The SEIAR model demonstrated strong predictive performance (overall <em>R</em>² = 0.831, <em>P</em> < 0.001) and estimated a median <em>R</em><sub><em>t</em></sub> of 0.963; however, several regions exceeded this threshold, with <em>R</em><sub><em>t</em></sub> peaking approximately two months prior to incidence surges. Spatial analyses revealed significant clustering in central urban areas, and integrated socioeconomic analysis identified the one-child rate as the primary driver of <em>R</em><sub><em>t</em></sub>, followed by population density and healthcare facility density (<em>P</em> < 0.01).</div></div><div><h3>Conclusion</h3><div>By integrating epidemiological data with socioeconomic factors, this study quantitatively elucidates the transmission characteristics of SF in Chengdu, providing data-driven support for monitoring and targeted intervention strategies in the absence of vaccination.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100844"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597434","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: 2025-08-07DOI: 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}
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