Infectious Disease Modelling最新文献

{"title":"Optimizing spatiotemporal nonpharmaceutical interventions for influenza: An adaptive reinforcement learning approach for regional heterogeneity","authors":"Yuxing Tian ,&nbsp;Xin Li ,&nbsp;Hualing Wang ,&nbsp;Heng Yuan ,&nbsp;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}

{"title":"Feasibility of eliminating adult hepatitis B in Guangdong by 2030: A modeling study","authors":"Ning Sun ,&nbsp;Xiaoping Shao ,&nbsp;Chen Hou ,&nbsp;Xiaowen Wei ,&nbsp;Weizhao Lin ,&nbsp;Ying Yang ,&nbsp;Liang Chen ,&nbsp;Chitin Hon ,&nbsp;Guanghu Zhu ,&nbsp;Jiufeng Sun ,&nbsp;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}

{"title":"Applications and reporting of causal inference modelling in infectious disease studies: A systematic review","authors":"Yukiko Ezure ,&nbsp;Mark Chatfield ,&nbsp;David L. Paterson ,&nbsp;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}

{"title":"Vaccination games of boundedly rational parents toward new childhood immunization","authors":"Wei Yin ,&nbsp;Martial L. Ndeffo-Mbah ,&nbsp;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}

{"title":"Application of the type and target reproduction numbers to the evaluation of the influence of each prefecture in Japan on the disease spread","authors":"Toshikazu Kuniya","doi":"10.1016/j.idm.2025.09.003","DOIUrl":"10.1016/j.idm.2025.09.003","url":null,"abstract":"<div><div>In this study, by applying population mobility data in July, August and September of 2019–2023 in Japan to a multigroup epidemic model, we calculate the type and target reproduction numbers of each prefecture in Japan. Regarding these values as a kind of network centrality measure, we discuss which prefectures are influential on the disease spread in Japan. We show that the values of the type reproduction number are relatively high in 10 prefectures consisting of Hokkaido, Saitama, Chiba, Tokyo, Kanagawa, Aichi, Kyoto, Osaka, Hyogo and Fukuoka. In particular, by calculating the target reproduction number, we show that Tokyo and Kyoto could be the most influential on the disease spread, and the population mobility between the Kanto and Kansai regions could be the key factor for the nationwide epidemic in Japan.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 143-149"},"PeriodicalIF":2.5,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097457","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}

{"title":"Travel-related importation risk of mpox from Hong Kong to Shenzhen in 2023: A modeling study","authors":"Ruohan Chen ,&nbsp;Jia Wan ,&nbsp;Dongfeng Kong ,&nbsp;Cong Niu ,&nbsp;Zengyang Shao ,&nbsp;Chijun Zhang ,&nbsp;Mingda Xu ,&nbsp;Yuan Bai ,&nbsp;Eric Lau ,&nbsp;Zhen Zhang ,&nbsp;Zhanwei Du","doi":"10.1016/j.idm.2025.09.005","DOIUrl":"10.1016/j.idm.2025.09.005","url":null,"abstract":"<div><div>Mpox, a viral zoonotic disease formerly known as monkeypox, has gained global attention following a multi-country outbreak in 2022-23, primarily linked to close intimate contact. In China, mpox cases surged in June 2023, with nearly a quarter of new cases concentrated in Guangdong Province, particularly Shenzhen. This study aimed to estimate the importation risk of mpox cases from Hong Kong to Shenzhen in 2023, utilizing cross-regional population mobility data from January to October 2023. The analysis focused on local transmission in Hong Kong and the probability of mpox importation into Shenzhen. Results revealed a significant importation risk, with over a 50 % chance of at least one travel-based mpox case from Hong Kong in June 2023. The study underscores the necessity of enhancing inbound surveillance for travelers from high mpox prevalence regions. It is suggested that regional governments implement tailored strategies, including enhanced surveillance and dynamic risk assessment for effective cross-border disease management, supported by robust monitoring and coordinated actions across jurisdictions.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 185-190"},"PeriodicalIF":2.5,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267449","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}

{"title":"Dynamics and optimal control for tuberculosis transmission via a data-validated periodic model","authors":"Chenkai Guo,&nbsp;Peng Wu","doi":"10.1016/j.idm.2025.09.002","DOIUrl":"10.1016/j.idm.2025.09.002","url":null,"abstract":"<div><div>China is the third-largest contributor to the global incidence of tuberculosis (TB), and there are significant differences in the prevalence of TB among different age groups. Therefore, it is necessary to study the contribution of adolescents to the transmission of tuberculosis. Given that tuberculosis in mainland China exhibits periodic transmission characteristics, a non-autonomous differential equation model that considers age stage and periodic transmission has been proposed. We derived the basic reproduction number <em>R</em>₀ of this model and proved the global asymptotic stability of the disease-free equilibrium when <em>R</em>₀ &lt; 1, as well as the persistence of the disease when <em>R</em>₀ &gt; 1. We estimated the basic reproduction number <em>R</em>₀ = 1.18, which indicates that tuberculosis in mainland China is of low endemicity. Sensitivity analysis tells us that the adolescent group has a significant impact on the transmission of tuberculosis and is an indispensable force. Furthermore, we constructed a tuberculosis transmission control model and proposed four optimal control strategies, calculated the strategy-related benefits (ACER) and the incremental benefits between strategies (ICER), and further provided targeted recommendations for controlling tuberculosis transmission among different groups.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 121-142"},"PeriodicalIF":2.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097456","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}

{"title":"Quantifying mpox transmission and control: A regional analysis of vaccination strategies in East Africa","authors":"Musa Rabiu ,&nbsp;Bosede Fagbemigun ,&nbsp;Sunday Fadugba ,&nbsp;Michael Shatalov ,&nbsp;Kekana Malesela ,&nbsp;Adejimi Adeniji","doi":"10.1016/j.idm.2025.09.001","DOIUrl":"10.1016/j.idm.2025.09.001","url":null,"abstract":"<div><div>Africa is home to the endemic mpox disease, especially in the tropical rain-forest regions of Central and West Africa. Although it is mostly found in the Democratic Republic of the Congo, reports of it have also come from other neighboring African nations. To understand the dynamics of mpox, we studied its spread in Burundi, Uganda, Rwanda, Congo, and Kenya before and after the implementation of interventions. Using a Bayesian framework, a simple mathematical model of Susceptible-Infected-Recovered type was calibrated and fitted to the 2022 mpox data covering the period before the introduction of intervention strategies. The model was then re-stratified to incorporate key epidemiological features, including vaccination with imperfect efficacy, partial immunity, exposure, and demographics. The transmission of mpox varied throughout East Africa, with Uganda exhibiting the highest basic reproduction number <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> = 2.51, suggesting the possibility of a rapid spread. Despite having the highest initial infection count and the lowest <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> (1.23), Congo may have had delayed detection. The moderate <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> values (1.35 and 1.88) in Rwanda and Burundi have implications for prompt intervention to control epidemics. Transmission and vaccination rates have a non-linear relationship with the thresholds required to contain mpox outbreaks. Our model shows that in high-transmission settings, substantially higher vaccination coverage (exceeding 80 % at an effectiveness of 70 %) is required to reduce the control reproduction number below unity, whereas in moderate-transmission contexts, coverage above 40 % may suffice. These quantitative thresholds provide actionable guidance for tailoring vaccination strategies to different epidemiological conditions. In particular, sustained vaccination strategies that achieve coverage above the threshold predicted by our model (approximately 80 %) can guarantee mpox eradication, even in situations with strong transmission rates. While real-world complexities such as heterogeneous risk groups and behavioral factors may affect outcomes, these findings shed light on potential quantitative thresholds and provide a foundation for more detailed, population-specific modeling of mpox interventions.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 29-46"},"PeriodicalIF":2.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019350","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}

{"title":"A framework using large time series model for early warning of infectious diseases","authors":"Yajie Liu ,&nbsp;Xiaoli Wang ,&nbsp;Zhidong Cao ,&nbsp;Tianyi Luo ,&nbsp;Peng Yang ,&nbsp;Quanyi Wang","doi":"10.1016/j.idm.2025.08.006","DOIUrl":"10.1016/j.idm.2025.08.006","url":null,"abstract":"<div><h3>Objective</h3><div>Infectious diseases controlling system is indispensable for weaken the damage to the people's life and property security caused by infectious diseases. An effective infectious diseases controlling system must incorporate an early warning mechanism designed to detect abnormal rising trends (outbreak) in spatial-temporal series. However, existing anomaly detection methods are often constrained by the quality and quantity of available data in specific application scenarios, particularly in infectious diseases early warning scenarios.</div></div><div><h3>Methods</h3><div>The emergence of generative pre-trained large time series models—hereafter referred to as large time series models—may provide a solution to this challenge. Based on these models, we propose an effective early warning framework.</div></div><div><h3>Results</h3><div>We compared the framework with statistic and deep learning methods on real-world infectious diseases datasets and related derived datasets. Our framework has a better performance and requires less data.</div></div><div><h3>Conclusion</h3><div>We propose a readily deployable early warning framework characterized by strong generalization ability and exceptional performance, which would enlighten the epidemic modeling researchers.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 107-120"},"PeriodicalIF":2.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061267","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}

{"title":"Impact of high-order time-delayed information on epidemic propagation in multiplex networks","authors":"Zehui Zhang ,&nbsp;Fang Wang ,&nbsp;Lilin Liu ,&nbsp;Lin Wang","doi":"10.1016/j.idm.2025.08.007","DOIUrl":"10.1016/j.idm.2025.08.007","url":null,"abstract":"<div><div>Traditional epidemic models often overlook disease incubation periods and high-order social interactions, limiting their ability to capture real-world transmission dynamics. To address these gaps, we develop a stochastic model that integrates both factors, investigating their combined effects on information diffusion and disease spread. Our framework consists of a two-layer network: an awareness layer, where disease-related information propagates through high-order delayed interactions, and an epidemic layer, where disease transmission follows an SIS model with incubation delays. Using a Markov chain approach, we derive outbreak thresholds and perform numerical simulations to assess the impact of delayed awareness adoption on epidemic outcomes. High-order delayed interactions accelerate information spread compared to traditional pairwise models. Interestingly, while incubation periods increase the risk of hidden transmission, they also provide a crucial window for awareness diffusion, potentially mitigating outbreaks. This dual role of incubation prolonging undetected transmission while enabling proactive awareness dissemination underscores the importance of synchronizing public health interventions with disease incubation phases.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 72-86"},"PeriodicalIF":2.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047953","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}