Pub Date : 2025-10-31DOI: 10.1016/j.idm.2025.10.007
Romain Glèlè Kakaï, Ludosky Crédo Djomatin
Malaria remains a significant public health problem, particularly in sub-Saharan Africa, where its burden remains a concern despite existing control measures. Recent advances in vaccination, particularly with RTS, S and R21, offer new opportunities to mitigate malaria transmission and disease severity. In this study, we develop an age-structured mathematical model to assess the combined effects of vaccination and non-vaccine interventions on malaria transmission dynamics. The model integrates demographic parameters, long-lasting insecticide-treated nets (LLIN), access to treatment for clinical cases, vaccine efficacy profiles, and waning immunity to simulate disease burden in age cohorts over a long-term period. Our approach enables a comparative assessment of projected malaria transmission rates, disease burden, and disability-adjusted life years (DALYs) under different vaccination scenarios. For application purposes, the model is calibrated using national-level demographic data for a low transmission setting (Ethiopia) and a high transmission setting (Nigeria). Preliminary findings suggest that the integration of vaccination with existing malaria control strategies could significantly reduce malaria incidence and mortality over time. This study provides valuable insights into optimizing vaccination strategies and guiding public health policies aimed at long-term malaria control in endemic regions.
{"title":"An age-structured mathematical model to assess the combined effects of vaccine and non-vaccine interventions on malaria transmission and burden","authors":"Romain Glèlè Kakaï, Ludosky Crédo Djomatin","doi":"10.1016/j.idm.2025.10.007","DOIUrl":"10.1016/j.idm.2025.10.007","url":null,"abstract":"<div><div>Malaria remains a significant public health problem, particularly in sub-Saharan Africa, where its burden remains a concern despite existing control measures. Recent advances in vaccination, particularly with <em>RTS</em>, <em>S</em> and <em>R</em>21, offer new opportunities to mitigate malaria transmission and disease severity. In this study, we develop an age-structured mathematical model to assess the combined effects of vaccination and non-vaccine interventions on malaria transmission dynamics. The model integrates demographic parameters, long-lasting insecticide-treated nets (LLIN), access to treatment for clinical cases, vaccine efficacy profiles, and waning immunity to simulate disease burden in age cohorts over a long-term period. Our approach enables a comparative assessment of projected malaria transmission rates, disease burden, and disability-adjusted life years (DALYs) under different vaccination scenarios. For application purposes, the model is calibrated using national-level demographic data for a low transmission setting (Ethiopia) and a high transmission setting (Nigeria). Preliminary findings suggest that the integration of vaccination with existing malaria control strategies could significantly reduce malaria incidence and mortality over time. This study provides valuable insights into optimizing vaccination strategies and guiding public health policies aimed at long-term malaria control in endemic regions.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 355-376"},"PeriodicalIF":2.5,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519783","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}
HIV testing and contact tracing facilitate early detection of HIV/AIDS infections for interrupting the hidden transmission. In this study, a susceptible-undiagnosed-diagnosed transmission model with testing and contact tracing is proposed in heterosexual and homosexual populations. The basic reproduction number, the heterosexual-transmission and homosexual-transmission reproduction numbers of the transmission model are derived by the next generation matrix method. The optimal-fitting results present that the basic reproduction number of Fujian 2012–2022 HIV/AIDS epidemics ranges from 1.93 to 2.74 by phases, which is consistent with the heterosexual-transmission reproduction number. The optimal fittings and the global sensitivity analysis show that the transmission risk mainly originates from the heterosexual contacts. The transmission risk assessments and the 2023–2035 tendency predictions are performed for HIV/AIDS prevalence in Fujian Province by scenarios. The numerical simulation results point out that the decrease of female-to-male transmission rate, the decline of proportion of active diagnosed population, and the reduction of the awareness delay, are the main contributors for declining HIV/AIDS prevalence. The 2023–2035 tendency predictions of Fujian Province reveal that, when the awareness delay is fixed, the rising trends of the infection scales caused by the transmission rates are more significant than those caused by the proportion of active diagnosed population. According to the transmission risk assessments and the tendency prediction results, several suggestions are provided to the policymakers for curbing the HIV/AIDS transmission in Fujian Province.
{"title":"HIV/AIDS hidden transmission model with HIV testing and contact tracing in an SID community","authors":"Huiling Ouyang , Fengying Wei , Zhen Jin , Jianfeng Xie","doi":"10.1016/j.idm.2025.10.010","DOIUrl":"10.1016/j.idm.2025.10.010","url":null,"abstract":"<div><div>HIV testing and contact tracing facilitate early detection of HIV/AIDS infections for interrupting the hidden transmission. In this study, a susceptible-undiagnosed-diagnosed transmission model with testing and contact tracing is proposed in heterosexual and homosexual populations. The basic reproduction number, the heterosexual-transmission and homosexual-transmission reproduction numbers of the transmission model are derived by the next generation matrix method. The optimal-fitting results present that the basic reproduction number of Fujian 2012–2022 HIV/AIDS epidemics ranges from 1.93 to 2.74 by phases, which is consistent with the heterosexual-transmission reproduction number. The optimal fittings and the global sensitivity analysis show that the transmission risk mainly originates from the heterosexual contacts. The transmission risk assessments and the 2023–2035 tendency predictions are performed for HIV/AIDS prevalence in Fujian Province by scenarios. The numerical simulation results point out that the decrease of female-to-male transmission rate, the decline of proportion of active diagnosed population, and the reduction of the awareness delay, are the main contributors for declining HIV/AIDS prevalence. The 2023–2035 tendency predictions of Fujian Province reveal that, when the awareness delay is fixed, the rising trends of the infection scales caused by the transmission rates are more significant than those caused by the proportion of active diagnosed population. According to the transmission risk assessments and the tendency prediction results, several suggestions are provided to the policymakers for curbing the HIV/AIDS transmission in Fujian Province.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 325-337"},"PeriodicalIF":2.5,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466632","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 : 2025-10-28DOI: 10.1016/j.idm.2025.10.005
Mohammadi Qurratul Ain, Angela Peace
Rotavirus and norovirus are principal viral agents of acute gastroenteritis, primarily transmitted through close contact. Although each virus has its own capability to spread the epidemic, rotavirus and norovirus infection simultaneously is known to have more serious repercussions for children. To examine this coalition, we construct a robust co-infection mathematical model to analyze the prevalence of these diseases and the factors influenced by varying seasonal transmission rates. We conduct numerical simulations and equilibria analyses to capture the dynamics of the individual diseases and their co-infections. We investigate basic and seasonal reproductive numbers, perform sensitivity analysis on key parameters, and numerically explore shifts in the timing and strength of seasonal transmission rates. The research demonstrates how seasonal dynamics significantly impact reproductive numbers, as well as drive the potential burden of co-infection.
{"title":"Predicting the burden of co-infections in seasonally driven dynamics of pediatric rotavirus and norovirus","authors":"Mohammadi Qurratul Ain, Angela Peace","doi":"10.1016/j.idm.2025.10.005","DOIUrl":"10.1016/j.idm.2025.10.005","url":null,"abstract":"<div><div>Rotavirus and norovirus are principal viral agents of acute gastroenteritis, primarily transmitted through close contact. Although each virus has its own capability to spread the epidemic, rotavirus and norovirus infection simultaneously is known to have more serious repercussions for children. To examine this coalition, we construct a robust co-infection mathematical model to analyze the prevalence of these diseases and the factors influenced by varying seasonal transmission rates. We conduct numerical simulations and equilibria analyses to capture the dynamics of the individual diseases and their co-infections. We investigate basic and seasonal reproductive numbers, perform sensitivity analysis on key parameters, and numerically explore shifts in the timing and strength of seasonal transmission rates. The research demonstrates how seasonal dynamics significantly impact reproductive numbers, as well as drive the potential burden of co-infection.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 278-302"},"PeriodicalIF":2.5,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466635","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 : 2025-10-28DOI: 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}
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