Pub Date : 2025-10-28DOI: 10.1016/j.idm.2025.10.004
Linus Wilson
This paper estimates COVID-19 infection fatality rate (IFR) in early 2020 before pharmaceutical interventions were available on a large population in the United States. The better estimates of COVID-19 deaths in New York City and its high COVID-19 infection rate made it ideal to accurately estimate the IFR. Further, we analyze the deaths and infections in New York City to estimate an overall IFR for the United States of 0.86 percent.
{"title":"COVID-19, infection fatality rate (IFR) implied by the serology, antibody, testing in New York City","authors":"Linus Wilson","doi":"10.1016/j.idm.2025.10.004","DOIUrl":"10.1016/j.idm.2025.10.004","url":null,"abstract":"<div><div>This paper estimates COVID-19 infection fatality rate (IFR) in early 2020 before pharmaceutical interventions were available on a large population in the United States. The better estimates of COVID-19 deaths in New York City and its high COVID-19 infection rate made it ideal to accurately estimate the IFR. Further, we analyze the deaths and infections in New York City to estimate an overall IFR for the United States of 0.86 percent.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 303-309"},"PeriodicalIF":2.5,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462892","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}
In recent years, the rapid mutation of SARS-CoV-2 has led to the emergence of new variants. Despite advancements in pandemic control, these new variants could pose substantial public health issues. This study introduces a comprehensive compartmental model that can handle multiple virus variants and population groups. The model also factors the influence of international visitors, per variant and group, on the population, which is pertinent for populations with a high ratio of incoming travellers. The model was applied to simulate the coexistence of different variants in the Emirate of Abu Dhabi from August 2022 until March 2023. Calibration was conducted using the data available from Abu Dhabi health authorities and international data from GISAID to estimate the prevalence of each variant. The model seems to effectively depict the temporal coexistence of multiple strains and, ultimately, the rise of a dominant variant. The simulation results from Abu Dhabi indicate that the XBB variant became the dominant strain by the end of the simulation period. The calibrated parameters for the XBB variant suggest that its dominance can be attributed to its superior ability to evade immunity and its increased infectiousness, estimated to be approximately 15 % more than the BQ.1 variant. The introduction of the XBB variant through infected visitors further amplified its emergence.
{"title":"A compartmental model of variant coexistence, dynamics and dominance in infectious diseases: Case for SARS-CoV-2 in Abu Dhabi","authors":"Mauricio Patón , Mireille Hantouche , Farida Al-Hosani , Amrit Sadani , Jorge Rodríguez , Rowan Abuyadek","doi":"10.1016/j.idm.2025.10.006","DOIUrl":"10.1016/j.idm.2025.10.006","url":null,"abstract":"<div><div>In recent years, the rapid mutation of SARS-CoV-2 has led to the emergence of new variants. Despite advancements in pandemic control, these new variants could pose substantial public health issues. This study introduces a comprehensive compartmental model that can handle multiple virus variants and population groups. The model also factors the influence of international visitors, per variant and group, on the population, which is pertinent for populations with a high ratio of incoming travellers. The model was applied to simulate the coexistence of different variants in the Emirate of Abu Dhabi from August 2022 until March 2023. Calibration was conducted using the data available from Abu Dhabi health authorities and international data from GISAID to estimate the prevalence of each variant. The model seems to effectively depict the temporal coexistence of multiple strains and, ultimately, the rise of a dominant variant. The simulation results from Abu Dhabi indicate that the XBB variant became the dominant strain by the end of the simulation period. The calibrated parameters for the XBB variant suggest that its dominance can be attributed to its superior ability to evade immunity and its increased infectiousness, estimated to be approximately 15 % more than the BQ.1 variant. The introduction of the XBB variant through infected visitors further amplified its emergence.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 310-324"},"PeriodicalIF":2.5,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-27DOI: 10.1016/j.idm.2025.10.008
Jinyi Hu , Haoyue Zheng , Yunyi Cai , Yixiu Kong , Yao Wang , Gui-Quan Sun , Jiancheng Lv , Quan-Hui Liu
Surveillance of infectious disease transmission is crucial for early detection and timely intervention. Existing studies mainly focus on static single-layer networks, primarily aiming to identify which types of nodes can provide early warning signals and accurate information on infections. Yet, real-world contact patterns are multiplex and time-varying, strongly shaping epidemic dynamics. Here, we propose a modeling framework for disease spread on time-varying multiplex networks and evaluate five node selection strategies: most connected, random, friends of random, most recent contacts, and most frequent contacts by using three metrics: early warning, peak timing, and peak ratio. These strategies are also tested across three scenarios with varying levels of structural information: Aggregated, Single-layer and Coupled networks. Simulation results show that the most connected strategy yields the best results across all metrics and scenarios, but it is costly and often impractical when full network information is unavailable. Importantly, our findings exhibit that the frequent-contact strategy on Coupled networks offers a practical alternative, achieving performance comparable to the most connected approach. Sensitivity analyses confirm the robustness of these findings. Our results highlight the importance of accounting for multiplexity and temporal dynamics in surveillance design and provide guidance for effective sentinel placement in epidemic monitoring.
{"title":"Surveillance of infectious diseases spreading on time-varying multiplex networks","authors":"Jinyi Hu , Haoyue Zheng , Yunyi Cai , Yixiu Kong , Yao Wang , Gui-Quan Sun , Jiancheng Lv , Quan-Hui Liu","doi":"10.1016/j.idm.2025.10.008","DOIUrl":"10.1016/j.idm.2025.10.008","url":null,"abstract":"<div><div>Surveillance of infectious disease transmission is crucial for early detection and timely intervention. Existing studies mainly focus on static single-layer networks, primarily aiming to identify which types of nodes can provide early warning signals and accurate information on infections. Yet, real-world contact patterns are multiplex and time-varying, strongly shaping epidemic dynamics. Here, we propose a modeling framework for disease spread on time-varying multiplex networks and evaluate five node selection strategies: most connected, random, friends of random, most recent contacts, and most frequent contacts by using three metrics: early warning, peak timing, and peak ratio. These strategies are also tested across three scenarios with varying levels of structural information: Aggregated, Single-layer and Coupled networks. Simulation results show that the most connected strategy yields the best results across all metrics and scenarios, but it is costly and often impractical when full network information is unavailable. Importantly, our findings exhibit that the frequent-contact strategy on Coupled networks offers a practical alternative, achieving performance comparable to the most connected approach. Sensitivity analyses confirm the robustness of these findings. Our results highlight the importance of accounting for multiplexity and temporal dynamics in surveillance design and provide guidance for effective sentinel placement in epidemic monitoring.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 256-267"},"PeriodicalIF":2.5,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-23DOI: 10.1016/j.idm.2025.10.009
Cheng Peng , Nana Chen , Bo-Wen Ming , Anqi Zhang , Yao Zuo , Paulo C. Ventura , Hongjie Yu , Marco Ajelli , Juanjuan Zhang
Background
Understanding human mobility changes during epidemics is critical for predicting disease spread and planning interventions. However, capturing fine-scale dynamics is challenging.
Methods
This study analyzed high-resolution human mobility patterns in Shanghai, China, during the 2022 SARS-CoV-2 Omicron BA.2 outbreak using large-scale anonymized cellular signaling data. We investigated mobility shifts across five distinct epidemic phases (pre-outbreak, targeted interventions, citywide lockdown, targeted lifting, and reopening) stratified by age, sex, and travel purpose. A comprehensive evaluation of four gravity and four radiation spatial interaction models was conducted to assess their ability to explain the observed mobility patterns under varying demographic and behavioral conditions.
Results
Population size and distance were found to be primary drivers of mobility, with notable variations across demographic groups and travel purposes. During the lockdown, mobility significantly decreased, particularly for social-related trips and the working-age population, while the effect of distance was substantially higher. Although mobility volumes recovered post-lockdown, a larger effect of distance persisted, implying long-lasting behavioral changes. Our comparative analysis showed that while several variants of gravity and radiation models captured overall patterns effectively, their performance was context-dependent, varying significantly across epidemic phases, population subgroups, and travel purposes.
Conclusion
These findings highlight the importance of integrating different mobility models to capture the complex human mobility picture by different population groups during an epidemic outbreak. Overall, this study advances our understanding of behavioral adaptations during crises, enhancing preparedness and response planning.
{"title":"Understanding human mobility patterns under a public health emergency","authors":"Cheng Peng , Nana Chen , Bo-Wen Ming , Anqi Zhang , Yao Zuo , Paulo C. Ventura , Hongjie Yu , Marco Ajelli , Juanjuan Zhang","doi":"10.1016/j.idm.2025.10.009","DOIUrl":"10.1016/j.idm.2025.10.009","url":null,"abstract":"<div><h3>Background</h3><div>Understanding human mobility changes during epidemics is critical for predicting disease spread and planning interventions. However, capturing fine-scale dynamics is challenging.</div></div><div><h3>Methods</h3><div>This study analyzed high-resolution human mobility patterns in Shanghai, China, during the 2022 SARS-CoV-2 Omicron BA.2 outbreak using large-scale anonymized cellular signaling data. We investigated mobility shifts across five distinct epidemic phases (pre-outbreak, targeted interventions, citywide lockdown, targeted lifting, and reopening) stratified by age, sex, and travel purpose. A comprehensive evaluation of four gravity and four radiation spatial interaction models was conducted to assess their ability to explain the observed mobility patterns under varying demographic and behavioral conditions.</div></div><div><h3>Results</h3><div>Population size and distance were found to be primary drivers of mobility, with notable variations across demographic groups and travel purposes. During the lockdown, mobility significantly decreased, particularly for social-related trips and the working-age population, while the effect of distance was substantially higher. Although mobility volumes recovered post-lockdown, a larger effect of distance persisted, implying long-lasting behavioral changes. Our comparative analysis showed that while several variants of gravity and radiation models captured overall patterns effectively, their performance was context-dependent, varying significantly across epidemic phases, population subgroups, and travel purposes.</div></div><div><h3>Conclusion</h3><div>These findings highlight the importance of integrating different mobility models to capture the complex human mobility picture by different population groups during an epidemic outbreak. Overall, this study advances our understanding of behavioral adaptations during crises, enhancing preparedness and response planning.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 241-255"},"PeriodicalIF":2.5,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-16DOI: 10.1016/j.idm.2025.10.002
Elizabeth Hunter , Jim Duggan
Calibrating process models such as compartmental SIR Models to real data can be performed using either optimization or Bayesian techniques. To accurately assess the performance of these methods, synthetic outbreak data can be used. All information about the data generative process is known for synthetic data, while when using real data there are many unknowns such as under-reporting of cases or real parameter values. We propose using an agent-based model to generate synthetic data. Calibrating to synthetic datasets created using different agent contact structures can provide us with information on how changes in contact structures impact SIR model parameters. We compare results for two calibration methods: Nelder-Mead an optimization technique and HMC, a Bayesian technique. The analysis finds that the two calibration methods perform similar in terms of accuracy when looking at the Mean Absolute Error, Mean Absolute Scaled Error, and Relative Root Mean Squared Error. Looking at the model parameters, HMC is better able to capture the ground truth parameters then Nelder-Mead. The results of the calibration additionally show that the effective infectious period is sensitive to the changes in contact patterns and the proportion of susceptible individuals in the population. For choosing a calibration method, if overall accuracy is the desired outcome, either method should perform equally well, however, if the aim is to understand and analyse the model parameters HMC is a better choice. Understanding how the effective parameters such as the infectious period changes as contact patterns and vaccination rates change can provide valuable information in understanding how to interpret parameters calibrated from real world data that captures both isolation and vaccination.
{"title":"A multi-method study evaluating the inference of compartmental model parameters from a generative agent-based model","authors":"Elizabeth Hunter , Jim Duggan","doi":"10.1016/j.idm.2025.10.002","DOIUrl":"10.1016/j.idm.2025.10.002","url":null,"abstract":"<div><div>Calibrating process models such as compartmental SIR Models to real data can be performed using either optimization or Bayesian techniques. To accurately assess the performance of these methods, synthetic outbreak data can be used. All information about the data generative process is known for synthetic data, while when using real data there are many unknowns such as under-reporting of cases or real parameter values. We propose using an agent-based model to generate synthetic data. Calibrating to synthetic datasets created using different agent contact structures can provide us with information on how changes in contact structures impact SIR model parameters. We compare results for two calibration methods: Nelder-Mead an optimization technique and HMC, a Bayesian technique. The analysis finds that the two calibration methods perform similar in terms of accuracy when looking at the Mean Absolute Error, Mean Absolute Scaled Error, and Relative Root Mean Squared Error. Looking at the model parameters, HMC is better able to capture the ground truth parameters then Nelder-Mead. The results of the calibration additionally show that the effective infectious period is sensitive to the changes in contact patterns and the proportion of susceptible individuals in the population. For choosing a calibration method, if overall accuracy is the desired outcome, either method should perform equally well, however, if the aim is to understand and analyse the model parameters HMC is a better choice. Understanding how the effective parameters such as the infectious period changes as contact patterns and vaccination rates change can provide valuable information in understanding how to interpret parameters calibrated from real world data that captures both isolation and vaccination.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 218-240"},"PeriodicalIF":2.5,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.idm.2025.10.003
Li Yang , Junjun Wang , Jiale Peng , Lijie Zhang , Xiaoqing Cheng
Background
Hepatitis E virus (HEV) causes substantial morbidity globally, with frequent outbreaks in low-resource settings due to fecal-oral transmission. Temperature and extreme heat may influence waterborne pathogens, but their impact on HEV risk is unclear.
Methods
We performed a time-stratified case-crossover study using 42,481 laboratory-confirmed hepatitis E cases reported in Jiangsu Province (2010–2023). Daily mean, maximum, and minimum temperatures were obtained from fixed-site monitoring data. We examined associations of short-term temperature (single-day and cumulative lags up to 21 days) and heat wave episodes with hepatitis E risk using conditional logistic regression. Heat waves were defined using percentile-based thresholds for consecutive days. Analyses were adjusted for relative humidity and time trends, and stratified by sex, age, residence, and occupation. Sensitivity analyses used alternative heat wave definitions and lag structures.
Results
Higher ambient temperature was associated with increases in hepatitis E risk. Each 1 °C rise in daily mean temperature (lag 0–1 days) was linked to a 0.6 % higher odds of hepatitis E (OR 1.006, 95 % CI 1.002–1.010). Similar associations were observed for maximum and minimum temperatures (e.g. OR 1.005 [1.002–1.009] per 1 °C at lag 0–1 for max temperature; OR 1.009 [1.004–1.014] at lag 0–3 for min temperature). Heat waves defined by more extreme and prolonged thresholds showed stronger effects. For example, a three-day daytime heat wave above the 95th percentile (Day_HW95_3d) was associated with an 18 % higher hepatitis E risk (OR 1.18, 95 % CI 1.08–1.29), and a four-day compound heat wave >90th percentile had an OR of 1.14 (95 % CI 1.04–1.24).
Conclusions
Short-term exposure to higher ambient temperatures and heat wave events was associated with increased risk of hepatitis E in Jiangsu, China. These results suggest that climate warming and extreme heat may elevate transmission of HEV, underscoring the need for strengthened water and sanitation interventions and targeted public health planning during hot weather.
戊型肝炎病毒（HEV）在全球范围内引起大量发病率，在低资源环境中由于粪口传播而频繁暴发。温度和极热可能影响水传播病原体，但它们对戊型肝炎风险的影响尚不清楚。方法对江苏省2010-2023年报告的42481例实验室确诊戊型肝炎病例进行时间分层病例交叉研究。日平均、最高和最低温度由固定站点监测数据获得。我们使用条件逻辑回归检查了短期温度（一天和累计滞后21天）和热浪发作与戊型肝炎风险的关系。热浪是用连续几天的百分位数阈值来定义的。分析调整了相对湿度和时间趋势，并按性别、年龄、居住地和职业分层。敏感性分析使用了不同的热浪定义和滞后结构。结果较高的环境温度与戊型肝炎发病风险增加有关。每日平均温度每升高1°C（滞后0-1天）与戊型肝炎的发病率增加0.6%相关（OR 1.006, 95% CI 1.002-1.010）。在最高和最低温度上也观察到类似的关联（例如，在最高温度0-1滞后时，每1°C的OR值为1.005[1.002-1.009]；在最低温度0-3滞后时，OR值为1.009[1.004-1.014]）。由更极端和更持久的阈值定义的热浪显示出更强的影响。例如，三天的白天热浪超过第95百分位（Day_HW95_3d）与戊型肝炎风险增加18%相关（OR 1.18, 95% CI 1.08-1.29），四天的复合热浪>；第90百分位的OR为1.14 （95% CI 1.04-1.24）。结论：在中国江苏省，短期暴露于较高的环境温度和热浪事件与戊型肝炎的风险增加有关。这些结果表明，气候变暖和极端高温可能加剧HEV的传播，强调需要在炎热天气期间加强水和卫生干预措施以及有针对性的公共卫生规划。
{"title":"Associations of ambient temperature and heat waves with risks of hepatitis E in Jiangsu, China (2010–2023): A time-stratified case-crossover study","authors":"Li Yang , Junjun Wang , Jiale Peng , Lijie Zhang , Xiaoqing Cheng","doi":"10.1016/j.idm.2025.10.003","DOIUrl":"10.1016/j.idm.2025.10.003","url":null,"abstract":"<div><h3>Background</h3><div>Hepatitis E virus (HEV) causes substantial morbidity globally, with frequent outbreaks in low-resource settings due to fecal-oral transmission. Temperature and extreme heat may influence waterborne pathogens, but their impact on HEV risk is unclear.</div></div><div><h3>Methods</h3><div>We performed a time-stratified case-crossover study using 42,481 laboratory-confirmed hepatitis E cases reported in Jiangsu Province (2010–2023). Daily mean, maximum, and minimum temperatures were obtained from fixed-site monitoring data. We examined associations of short-term temperature (single-day and cumulative lags up to 21 days) and heat wave episodes with hepatitis E risk using conditional logistic regression. Heat waves were defined using percentile-based thresholds for consecutive days. Analyses were adjusted for relative humidity and time trends, and stratified by sex, age, residence, and occupation. Sensitivity analyses used alternative heat wave definitions and lag structures.</div></div><div><h3>Results</h3><div>Higher ambient temperature was associated with increases in hepatitis E risk. Each 1 °C rise in daily mean temperature (lag 0–1 days) was linked to a 0.6 % higher odds of hepatitis E (OR 1.006, 95 % CI 1.002–1.010). Similar associations were observed for maximum and minimum temperatures (e.g. OR 1.005 [1.002–1.009] per 1 °C at lag 0–1 for max temperature; OR 1.009 [1.004–1.014] at lag 0–3 for min temperature). Heat waves defined by more extreme and prolonged thresholds showed stronger effects. For example, a three-day daytime heat wave above the 95th percentile (Day_HW95_3d) was associated with an 18 % higher hepatitis E risk (OR 1.18, 95 % CI 1.08–1.29), and a four-day compound heat wave >90th percentile had an OR of 1.14 (95 % CI 1.04–1.24).</div></div><div><h3>Conclusions</h3><div>Short-term exposure to higher ambient temperatures and heat wave events was associated with increased risk of hepatitis E in Jiangsu, China. These results suggest that climate warming and extreme heat may elevate transmission of HEV, underscoring the need for strengthened water and sanitation interventions and targeted public health planning during hot weather.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 268-277"},"PeriodicalIF":2.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-03DOI: 10.1016/j.idm.2025.10.001
Yuxing Tian , Xin Li , Hualing Wang , Heng Yuan , Tao Zhang
Background
Influenza remains a significant global public health challenge because of its high transmissibility, widespread circulation, and considerable societal impact. Conventional threshold-based nonpharmaceutical interventions (NPIs) provide valuable frameworks for outbreak control; however, these standardized approaches may not fully account for important regional heterogeneity. It remains difficult to weigh regional characteristics and accurately balance infection control and socioeconomic costs.
Methods
We propose a susceptible-exposed-infectious-quarantined-removed compartmental model-dueling deep Q-network (SEIQR-Dueling DQN) framework tailored to plains, hilly, and plateau cities. By integrating climatic, demographic, and health care resource data, the model captures regional differences in transmission and recovery dynamics. A multidimensional state space and discrete intervention set allow for the adaptive optimization of NPI strategies across varying epidemic and resource conditions. Model parameters were estimated by sequential Bayesian optimization, and bootstrap resampling was used to quantify uncertainty. In addition, the performance of the SEIQR-Dueling DQN strategy was compared with that of the threshold-based strategy in terms of the reduction in cumulative infections, peak prevalence and length of intervention periods.
Results
The threshold-based intervention policy reduced cumulative infections by 3.05 %–3.67 % and peak incidence by 8.26 %–12.58 % but showed limited responsiveness to regional variation, often resulting in either under- or over-control. The SEIQR-Dueling DQN framework dynamically adjusted intervention timing and combinations on the basis of local demographic structures and epidemic trends and reduced cumulative infections by 5.87 %, 5.99 %, and 5.21 % in plains, hilly, and plateau cities, respectively, while achieving peak reductions of 34.92 %, 22.23 %, and 8.12 %, respectively, with a balanced consideration of socioeconomic impact. To assess generalizability, the trained model was applied to cities with differing transmission dynamics and demonstrated consistent performance across settings.
Conclusion
The SEIQR-Dueling DQN framework supports tailored interventions across regions and shows promise for broader application in the management of regional heterogeneity and future emerging infectious diseases.
{"title":"Optimizing spatiotemporal nonpharmaceutical interventions for influenza: An adaptive reinforcement learning approach for regional heterogeneity","authors":"Yuxing Tian , Xin Li , Hualing Wang , Heng Yuan , Tao Zhang","doi":"10.1016/j.idm.2025.10.001","DOIUrl":"10.1016/j.idm.2025.10.001","url":null,"abstract":"<div><h3>Background</h3><div>Influenza remains a significant global public health challenge because of its high transmissibility, widespread circulation, and considerable societal impact. Conventional threshold-based nonpharmaceutical interventions (NPIs) provide valuable frameworks for outbreak control; however, these standardized approaches may not fully account for important regional heterogeneity. It remains difficult to weigh regional characteristics and accurately balance infection control and socioeconomic costs.</div></div><div><h3>Methods</h3><div>We propose a susceptible-exposed-infectious-quarantined-removed compartmental model-dueling deep Q-network (SEIQR-Dueling DQN) framework tailored to plains, hilly, and plateau cities. By integrating climatic, demographic, and health care resource data, the model captures regional differences in transmission and recovery dynamics. A multidimensional state space and discrete intervention set allow for the adaptive optimization of NPI strategies across varying epidemic and resource conditions. Model parameters were estimated by sequential Bayesian optimization, and bootstrap resampling was used to quantify uncertainty. In addition, the performance of the SEIQR-Dueling DQN strategy was compared with that of the threshold-based strategy in terms of the reduction in cumulative infections, peak prevalence and length of intervention periods.</div></div><div><h3>Results</h3><div>The threshold-based intervention policy reduced cumulative infections by 3.05 %–3.67 % and peak incidence by 8.26 %–12.58 % but showed limited responsiveness to regional variation, often resulting in either under- or over-control. The SEIQR-Dueling DQN framework dynamically adjusted intervention timing and combinations on the basis of local demographic structures and epidemic trends and reduced cumulative infections by 5.87 %, 5.99 %, and 5.21 % in plains, hilly, and plateau cities, respectively, while achieving peak reductions of 34.92 %, 22.23 %, and 8.12 %, respectively, with a balanced consideration of socioeconomic impact. To assess generalizability, the trained model was applied to cities with differing transmission dynamics and demonstrated consistent performance across settings.</div></div><div><h3>Conclusion</h3><div>The SEIQR-Dueling DQN framework supports tailored interventions across regions and shows promise for broader application in the management of regional heterogeneity and future emerging infectious diseases.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 203-217"},"PeriodicalIF":2.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267451","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 : 2025-09-28DOI: 10.1016/j.idm.2025.09.008
Ning Sun , Xiaoping Shao , Chen Hou , Xiaowen Wei , Weizhao Lin , Ying Yang , Liang Chen , Chitin Hon , Guanghu Zhu , Jiufeng Sun , Limei Sun
Objectives
Eliminating hepatitis B remains challenging, especially in Guangdong, the region with China's highest burden. Predicting incidence, optimizing vaccination, and reducing illness are essential to meet the WHO goal of a 90 % reduction by 2030.
Methods
Based on the HBV surveillance data from 2005 to 2022, disease clustering patterns, correlation between vaccination and incidence were determined. A six-compartment transmission model was established and validated by estimating infectivity using nonlinear least squares and polynomial fitting.
Results
From 2005 to 2022, acute HBV cases in Guangdong declined from 7509 to 2,097, while chronic cases in adults aged ≥15 rose from 38,595 to 146,658. High-risk clusters remained in Guangzhou, Foshan, and Shenzhen. Infant vaccination was linked to reduced acute infections but had limited effect on chronic cases. By 2030, acute HBV infectivity is projected to reach 1872 cases, with 100,354 new chronic infections expected in adults. To meet the WHO 2030 elimination target, average recovery time for chronic carriers must be reduced from 40 years to 7.7 years. For full elimination, it should be shortened to 1.85 years.
Conclusions
Infant vaccination curbed acute HBV in youth, but chronic cases in adults threaten elimination goals. Scaling therapies to accelerate chronic HBV recovery is urgent.
{"title":"Feasibility of eliminating adult hepatitis B in Guangdong by 2030: A modeling study","authors":"Ning Sun , Xiaoping Shao , Chen Hou , Xiaowen Wei , Weizhao Lin , Ying Yang , Liang Chen , Chitin Hon , Guanghu Zhu , Jiufeng Sun , Limei Sun","doi":"10.1016/j.idm.2025.09.008","DOIUrl":"10.1016/j.idm.2025.09.008","url":null,"abstract":"<div><h3>Objectives</h3><div>Eliminating hepatitis B remains challenging, especially in Guangdong, the region with China's highest burden. Predicting incidence, optimizing vaccination, and reducing illness are essential to meet the WHO goal of a 90 % reduction by 2030.</div></div><div><h3>Methods</h3><div>Based on the HBV surveillance data from 2005 to 2022, disease clustering patterns, correlation between vaccination and incidence were determined. A six-compartment transmission model was established and validated by estimating infectivity using nonlinear least squares and polynomial fitting.</div></div><div><h3>Results</h3><div>From 2005 to 2022, acute HBV cases in Guangdong declined from 7509 to 2,097, while chronic cases in adults aged ≥15 rose from 38,595 to 146,658. High-risk clusters remained in Guangzhou, Foshan, and Shenzhen. Infant vaccination was linked to reduced acute infections but had limited effect on chronic cases. By 2030, acute HBV infectivity is projected to reach 1872 cases, with 100,354 new chronic infections expected in adults. To meet the WHO 2030 elimination target, average recovery time for chronic carriers must be reduced from 40 years to 7.7 years. For full elimination, it should be shortened to 1.85 years.</div></div><div><h3>Conclusions</h3><div>Infant vaccination curbed acute HBV in youth, but chronic cases in adults threaten elimination goals. Scaling therapies to accelerate chronic HBV recovery is urgent.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 191-202"},"PeriodicalIF":2.5,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267450","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 : 2025-09-24DOI: 10.1016/j.idm.2025.09.006
Yukiko Ezure , Mark Chatfield , David L. Paterson , Lisa Hall
Causal inference is increasingly employed in infectious disease (ID) epidemiology. Despite the increasing adoption of causal inference methods in infectious disease research, there has been no comprehensive review of their implementation trends, estimation approaches, and key specifications. A systematic examination of how these methods were being applied in practice could identify both successful strategies and common pitfalls. This systematic review aimed to describe the usage and reporting of causal methods in observational ID studies. The applications of causal methods in the analyses of ID observational data were identified from systematic searches of PubMed, Medline, Web of Science, and Scopus. Our analysis focused on detailing the adoption trends of causal inference methods and assessing the comprehensiveness of their reporting and publication between 2010 and 2023. Of the 172 studies, the majority utilised propensity score-based methods (n = 133, 77 %). We identified only 39 studies that explicitly described the use of causal frameworks and employed variations of causal analyses. The most common reason for using causal methods was to address time-varying variables that are prominent in ID research. Consequently, a common approach used was inverse probability treatment weighting with the marginal structural model; additionally, targeted maximum likelihood estimation has become popular in minimising bias.
There is substantial variation in reporting causal methods in ID research. Development of reporting guidelines is needed for clear reporting alongside training on how to use and appraise applications of causal inference in observational ID research. This is particularly important for ID modelling, where time-varying factors and complex transmissions and dynamics of treatment often necessitate complex modelling approaches.
因果推理越来越多地应用于传染病流行病学。尽管在传染病研究中越来越多地采用因果推理方法，但尚未对其实施趋势、估计方法和关键规范进行全面审查。对这些方法在实践中如何应用进行系统的检查，既可以确定成功的策略，也可以确定常见的缺陷。本系统综述旨在描述因果方法在观察性ID研究中的使用和报告。因果方法在ID观测数据分析中的应用是通过PubMed、Medline、Web of Science和Scopus的系统搜索确定的。我们的分析重点是详细介绍因果推理方法的采用趋势，并评估其报告和出版在2010年至2023年间的全面性。在这172项研究中，大多数使用了基于倾向得分的方法（n = 133,77 %）。我们发现只有39项研究明确描述了因果框架的使用，并采用了因果分析的变体。使用因果方法的最常见原因是解决在ID研究中突出的时变变量。因此，常用的方法是利用边际结构模型进行逆概率处理加权；此外，有针对性的最大似然估计在最小化偏差方面已经变得流行。在ID研究中，报告因果关系的方法有很大的差异。需要制定报告准则，以便明确报告，同时培训如何在观察性ID研究中使用和评估因果推理的应用。这对ID建模尤其重要，因为时变因素和复杂的传输和治疗动态往往需要复杂的建模方法。
{"title":"Applications and reporting of causal inference modelling in infectious disease studies: A systematic review","authors":"Yukiko Ezure , Mark Chatfield , David L. Paterson , Lisa Hall","doi":"10.1016/j.idm.2025.09.006","DOIUrl":"10.1016/j.idm.2025.09.006","url":null,"abstract":"<div><div>Causal inference is increasingly employed in infectious disease (ID) epidemiology. Despite the increasing adoption of causal inference methods in infectious disease research, there has been no comprehensive review of their implementation trends, estimation approaches, and key specifications. A systematic examination of how these methods were being applied in practice could identify both successful strategies and common pitfalls. This systematic review aimed to describe the usage and reporting of causal methods in observational ID studies. The applications of causal methods in the analyses of ID observational data were identified from systematic searches of PubMed, Medline, Web of Science, and Scopus. Our analysis focused on detailing the adoption trends of causal inference methods and assessing the comprehensiveness of their reporting and publication between 2010 and 2023. Of the 172 studies, the majority utilised propensity score-based methods (n = 133, 77 %). We identified only 39 studies that explicitly described the use of causal frameworks and employed variations of causal analyses. The most common reason for using causal methods was to address time-varying variables that are prominent in ID research. Consequently, a common approach used was inverse probability treatment weighting with the marginal structural model; additionally, targeted maximum likelihood estimation has become popular in minimising bias.</div><div>There is substantial variation in reporting causal methods in ID research. Development of reporting guidelines is needed for clear reporting alongside training on how to use and appraise applications of causal inference in observational ID research. This is particularly important for ID modelling, where time-varying factors and complex transmissions and dynamics of treatment often necessitate complex modelling approaches.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 165-184"},"PeriodicalIF":2.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1016/j.idm.2025.09.004
Wei Yin , Martial L. Ndeffo-Mbah , Tamer Oraby
Infectious diseases harm societies through disease-induced morbidity, mortality, loss of productivity, and inequality. Thus, controlling and preventing them is critical for public health and societal well-being. However, societies can hinder efforts to control the spread of diseases by failing to adhere to public health recommendations, such as through vaccine hesitancy. Various disease-transmission models have been utilized to help policymakers respond to (re)emerging outbreaks. The usefulness of such models in assessing the effectiveness of public health policies is significantly dependent on human behavior. This paper introduces a new model of parental behavior toward a new childhood immunization. The model incorporates societal features, social norms, and bounded rationality. We integrate this model with the dynamics of childhood disease, as depicted by a standard susceptible-infected-recovered model, to offer a detailed perspective on vaccine acceptance dynamics. We found that the behavioral model provides a new population game theory's replicator dynamical equation with an entropy-like term. Interestingly, societal norms and bounded rationality play a crucial role in shaping vaccine uptake through a novel function, which we term the critical societal vaccine cost. The results suggest that reduced vaccine costs below the critical societal vaccine cost and higher initial acceptance rates increase the probability of disease elimination. A gradual increase in vaccination costs, as an adaptive dynamic policy for disease eradication, is also possible. In particular, strong social norms and low levels of bounded rationality positively contribute to disease eradication even when the basic reproduction number of the disease in that society is large.
{"title":"Vaccination games of boundedly rational parents toward new childhood immunization","authors":"Wei Yin , Martial L. Ndeffo-Mbah , Tamer Oraby","doi":"10.1016/j.idm.2025.09.004","DOIUrl":"10.1016/j.idm.2025.09.004","url":null,"abstract":"<div><div>Infectious diseases harm societies through disease-induced morbidity, mortality, loss of productivity, and inequality. Thus, controlling and preventing them is critical for public health and societal well-being. However, societies can hinder efforts to control the spread of diseases by failing to adhere to public health recommendations, such as through vaccine hesitancy. Various disease-transmission models have been utilized to help policymakers respond to (re)emerging outbreaks. The usefulness of such models in assessing the effectiveness of public health policies is significantly dependent on human behavior. This paper introduces a new model of parental behavior toward a new childhood immunization. The model incorporates societal features, social norms, and bounded rationality. We integrate this model with the dynamics of childhood disease, as depicted by a standard susceptible-infected-recovered model, to offer a detailed perspective on vaccine acceptance dynamics. We found that the behavioral model provides a new population game theory's replicator dynamical equation with an entropy-like term. Interestingly, societal norms and bounded rationality play a crucial role in shaping vaccine uptake through a novel function, which we term the critical societal vaccine cost. The results suggest that reduced vaccine costs below the critical societal vaccine cost and higher initial acceptance rates increase the probability of disease elimination. A gradual increase in vaccination costs, as an adaptive dynamic policy for disease eradication, is also possible. In particular, strong social norms and low levels of bounded rationality positively contribute to disease eradication even when the basic reproduction number of the disease in that society is large.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 150-164"},"PeriodicalIF":2.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158855","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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