Infectious Disease Modelling最新文献

{"title":"Modeling the seasonal and climate-dependent dynamics of visceral leishmaniasis in Brazil: Implications for transmission and Control","authors":"Quinn H. Adams ,&nbsp;Davidson H. Hamer ,&nbsp;Lucy R. Hutyra ,&nbsp;Gregory A. Wellenius ,&nbsp;Kayoko Shioda","doi":"10.1016/j.idm.2025.11.009","DOIUrl":"10.1016/j.idm.2025.11.009","url":null,"abstract":"<div><h3>Background</h3><div>Visceral leishmaniasis (VL) is a parasitic, zoonotic neglected tropical disease that remains a persistent public health challenge in endemic regions of Brazil, including the state of Maranhão. Transmission dynamics are complex, involving interactions between <em>Lutzomyia longipalpis</em> sandflies, canine reservoirs, and human hosts, and are influenced by environmental and climatic variability. Mathematical models are critical tools for understanding these dynamics and identifying opportunities to effectively disrupt transmission.</div></div><div><h3>Methods</h3><div>Our objective was to develop and calibrate a climate-informed mechanistic model of VL transmission in Maranhão, Brazil, and to evaluate the potential impacts of vector, environmental, and reservoir-targeted interventions. The model incorporates seasonally varying sandfly biting rates and vector recruitment and explicitly accounts for climate variability through the El Niño-Southern Oscillation (ENSO). Transmission rates between populations (human, canine reservoir, and sandfly vector) were calibrated using monthly reported human VL cases from 2007 to 2019 in Maranhão. We simulated the impact of four potential interventions on VL incidence: increased vector mortality, environmental sanitation (reducing vector maturation), expanded canine treatment, and increased canine culling.</div></div><div><h3>Results</h3><div>The model accurately reproduced the observed temporal trends in monthly human VL cases in Maranhão and quantified the nonlinear effects of potential interventions. Vector control was the most effective standalone strategy, with a 10 % increase in sandfly mortality reducing human cases by 43 %, and a 90 % increase leading to a 96 % decline. Environmental sanitation was similarly impactful, with a 50 % reduction in sandfly maturation lowering cases by 72 %, and a 90 % reduction leading to a 97 % decline. Canine-focused strategies were less effective: expanded canine treatment reduced human cases only up to 69 %, while increased euthanasia had only modest effects. A combined intervention strategy was more effective than any individual measure, reducing cases by 61 % at just a 10 % increase in coverage and achieving substantially greater declines at higher levels.</div></div><div><h3>Conclusions</h3><div>Climate variability and seasonal dynamics were key drivers of VL transmission in this setting. Our findings highlight the importance of integrating vector control and environmental management as core components of VL mitigation strategies. While canine-focused interventions may contribute incremental benefits, they are less effective than other interventions and are insufficient when implemented in isolation.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 549-559"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738325","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":"Dynamical analysis of the SVEIR-M epidemic model with age structure under media coverage","authors":"Jianrong Wang ,&nbsp;Xue Yan ,&nbsp;Xinghua Chang ,&nbsp;Maoxing Liu","doi":"10.1016/j.idm.2025.11.004","DOIUrl":"10.1016/j.idm.2025.11.004","url":null,"abstract":"<div><div>With the frequent emergence and spread of new infectious diseases, poses severe threats to public health, and the government often relies on non-pharmaceutical interventions to cope. Meanwhile, the impact of media information on public behavior and health awareness is increasingly significant, becoming an indispensable factor in epidemic prevention and control. This paper constructs an SVEIR-M infectious disease model integrating age structure and media coverage mechanisms, depicting the differences in individuals’ acceptance of media information and the effectiveness of vaccination at different age stages. The model introduces complex factors such as immune waning, latent development age, and media information dissemination, and systematically analyzes the existence and stability of disease-free and endemic equilibrium points using partial differential equations and Volterra integral tools. It is proved that the basic reproduction number <em>R</em>₀ plays a threshold role in characterizing the dynamical properties of the system, and the global stability of equilibrium points under different conditions is demonstrated by constructing Lyapunov functions. In addition, the uniform persistence of the system is analyzed, and the correctness of the theoretical analysis is verified through numerical simulations, discussing the impact of different intervention measures on epidemic development. The research results show that media publicity and vaccination can significantly reduce the infection and mortality rates, and their combination can more effectively control the spread of the epidemic.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 477-498"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683589","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":"Corrigendum to: “Bayesian spatio-temporal modeling of severe acute respiratory syndrome in Brazil: A comparative analysis across pre-, during, and post-COVID-19 eras” [Infectious Disease Modelling, 10 (2) (2025) 466-476]","authors":"Rodrigo de Souza Bulhões ,&nbsp;Jonatha Sousa Pimentel ,&nbsp;Paulo Canas Rodrigues","doi":"10.1016/j.idm.2025.12.017","DOIUrl":"10.1016/j.idm.2025.12.017","url":null,"abstract":"","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Page 751"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884009","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":"Age-structured next generation matrix and R0 calculation for Mycobacterium avium subsp. paratuberculosis (MAP)","authors":"Yuqi Gao ,&nbsp;Nienke Hartemink ,&nbsp;Piter Bijma ,&nbsp;Mart C.M. de Jong","doi":"10.1016/j.idm.2025.12.003","DOIUrl":"10.1016/j.idm.2025.12.003","url":null,"abstract":"<div><div>Paratuberculosis (Johne's disease), caused by <em>Mycobacterium avium</em> subspecies <em>paratuberculosis</em> (MAP), is known for its age-specific susceptibility, lifelong infection, varied shedding patterns, low diagnostic sensitivity, long latent period, and prolonged environmental persistence, all of which make eradication challenging. Here, we present an age-structured next-generation matrix (NGM) for a closed, year-round calving, free stall dairy herd to quantify MAP transmission and evaluate the potential of five animal traits that could be targeted by genetic selection and three herd management interventions to reduce the basic reproduction number (R₀). The NGM is based on a compartmental model, that captures dam-to-calf and environmental transmission. Hosts are represented in discrete, one-week age groups, each with its age-dependent susceptibility and location (calving pen for newborns, calf pen for calves, and adult pen for heifer/adults), to reflect both host demography and age-specific exposure.</div><div>Our baseline NGM yields R₀ = 2.90, with 91.1 % of new infections occurring in calves (1–52 weeks), 5.01 % in newborns (0–1 week), and 3.91 % in heifers/adults (&gt;52 weeks). Univariate analysis identifies two traits that (when successfully selected for) could drive R₀ below one: reducing the initial susceptibility level from 1 to 0.303 (or lower) or increasing the age-specific susceptibility decrease rate from 0.0629 to 0.214 wk⁻¹ (or higher). We further derive a linear approximation for ΔR₀ that enables rapid scenario testing without reconstructing the full matrix. This study provides methods for quantifying farm-level MAP transmission dynamics and identifying key traits and measures for targeted interventions.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 560-574"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738408","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":"Systematic prediction of spatiotemporal transmission of potential respiratory pandemics in China","authors":"Xiao Liu ,&nbsp;Yanxia Sun ,&nbsp;Rui Shen ,&nbsp;Qing Wang ,&nbsp;Mingyue Jiang ,&nbsp;Weizhong Yang ,&nbsp;Luzhao Feng","doi":"10.1016/j.idm.2025.12.019","DOIUrl":"10.1016/j.idm.2025.12.019","url":null,"abstract":"<div><div>Population movement significantly influences respiratory disease transmission; however, movement restrictions can impose substantial societal burdens. To understand spatiotemporal characteristic of a potential respiratory pandemic in Chinese mainland which could help in precise control, a spatiotemporal transmission model incorporating population movement dynamics was developed. The model was calibrated using Corona Virus Disease of 2019 data collected from an online survey of 3 million respondents conducted in December 2022. With the model, simulated hypothetical respiratory pandemics originating from each province and successfully predicted province-level transmission paths, peaking times and peaking infections. Beijing was identified as the most important location within the transmission network under various scenarios due to its strong mobility and socioeconomic connectivity. A global sensitivity analysis was conducted using the Partial Rank Correlation Coefficient (PRCC) method to evaluate the influence of key disease parameters on transmission dynamics. Transmission rate, progression rate and recovery rate were identified as key parameters influencing transmission characteristics. Additionally, the model provides a valuable predictive tool for understanding the transmission patterns of respiratory pandemics, which enables policymakers to gain insights into epidemic progression, facilitating the development of more targeted and effective control measures. Furthermore, our research offers a methodological framework for predicting epidemic transmission characteristics in advance, contributing to the field of public health.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 752-763"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925759","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":"Dynamic analysis of syphilis model with the saturated incidence and early latent stage","authors":"Anli Yao ,&nbsp;Fengying Wei ,&nbsp;Jianfeng Xie","doi":"10.1016/j.idm.2025.11.008","DOIUrl":"10.1016/j.idm.2025.11.008","url":null,"abstract":"<div><div>Syphilis is a blood-borne disease with multiple hidden-transmission stages caused by <em>Treponema pallidum</em>, and most infected individuals are asymptomatic as reported by the World Health Organization. This study establishes a Susceptible-Exposed-Infected-Latent-Recovered-Susceptible syphilis model with the nonlinear incidence and early latent stage incorporating the psychological effect. Firstly, the basic reproduction number of the SEILRS syphilis model is derived using the next generation matrix method. Then, the local asymptotic stability of the syphilis-free equilibrium point is proved. The global asymptotic stabilities of the syphilis-free equilibrium point and an endemic equilibrium point are shown by LaSalle's invariance principle. Further, the key parameters of the SEILRS syphilis model are estimated by the least squares method against the surveillance data of Fujian Province, China. The numerical simulation demonstrates that the changes of transmission rate in the early latent stage, treatment rate in the secondary stage and psychological effect in the early latent stage present the significant influences on the infection scale of syphilis. The 2023–2030 tendency predictions of the infection scale with scenarios indicate that the transmission rates are most critical for the prevalence of syphilis. As a consequence, to more effectively reduce the transmission rates of syphilis, it is recommended to enhance testing and screening for high-risk groups, to ensure the effective and complete treatment for infected individuals in the secondary stage, and to vigorously publicize the asymptomatic but infectious characteristics of infected individuals in the early latent stage.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 527-548"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738407","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 the first booster vaccine against SARS-CoV-2 in Chile","authors":"Emilio Molina ,&nbsp;Diego Olguín ,&nbsp;Antoine Brault ,&nbsp;Paula Uribe ,&nbsp;Pedro Gajardo ,&nbsp;Mauricio Canals ,&nbsp;Héctor Ramírez","doi":"10.1016/j.idm.2025.12.009","DOIUrl":"10.1016/j.idm.2025.12.009","url":null,"abstract":"<div><div>The present paper proposes a novel methodology for evaluating the impact of a vaccination plan against a transmissible disease. The methodology has two distinct stages. The initial stage comprises a compartmental model that describes the transmission of the disease within the population. This model is composed of fundamental parts representing the vaccination status and is used in this initial stage to estimate the number of cases and deaths averted by the vaccination plan. The case dynamics generated by the compartmental model serve as the input for a data-driven model in the second part of the methodology. The second model is statistical in nature and provides additional information regarding the number of hospitalizations and ICU admissions averted. This methodology is applied to assess the impact of booster SARS-CoV-2 vaccines in Chile, resulting in an estimation of 84 % (95 % confidence interval (CI): 74 %–92 %) of cases, 73 % (95 % CI: 57 %, 80 %) of hospital admissions, 77 % (95 % CI: 61 %, 84 %) of intensive care unit admissions, and 78 % (95 % CI: 62 %–85 %) of deaths averted between August 16 and December 31, 2021.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 701-718"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884011","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":"Integrating Kolmogorov-Arnold networks with ordinary differential equations for efficient, interpretable, and robust deep learning: Epidemiology of infectious diseases as a case study","authors":"Kexin Ma ,&nbsp;Xu Lu ,&nbsp;Nicola Luigi Bragazzi ,&nbsp;Dominik Selzer ,&nbsp;Thorsten Lehr ,&nbsp;Biao Tang","doi":"10.1016/j.idm.2025.12.006","DOIUrl":"10.1016/j.idm.2025.12.006","url":null,"abstract":"<div><div>This study extends universal differential equation (UDE) frameworks by integrating the Kolmogorov-Arnold Network (KAN) with ordinary differential equations, referred to as KAN-UDE, to achieve efficient and interpretable deep learning. Our case study centers on the epidemiology of emerging infectious diseases. Compared to UDEs based on multi-layer perceptrons, training KAN-UDE models shows significantly improved fitting performance, as evidenced by a rapid and substantial reduction in loss. KAN-UDE models demonstrate accurate reconstruction of nonlinear functions under partial time-series training data, maintaining robustness to data sparsity. This approach enables an interpretable learning process, as KAN-UDE models were reconstructed as fully mechanistic models (RMMs). While KAN-UDE models exhibit lower robustness and accuracy when real-world data randomness is considered, RMMs predict epidemic trends robustly and accurately over much longer time windows, as KAN precisely reconstructs the mechanistic functions despite data randomness.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 603-618"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791246","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":"Comparing virus incubation time in SIRC models: Deterministic versus stochastic approaches","authors":"Abdelmalik Moujahid ,&nbsp;Fernando Vadillo","doi":"10.1016/j.idm.2025.08.002","DOIUrl":"10.1016/j.idm.2025.08.002","url":null,"abstract":"<div><div>Time delays are a fundamental feature in modeling stochastic epidemic systems, as they capture the incubation period and other physiological lags inherent in disease transmission. In this work, we investigate a stochastic SIRC (Susceptible-Infectious-Recovered-Cross-immune) epidemic model where the delay is incorporated into the transmission term to reflect the incubation period. To account for environmental variability, we examine two stochastic formulations: the classical approach, which adds independent white noise to each compartment, and a probabilistic, event-driven model in which stochasticity arises directly from transition probabilities.</div><div>A key focus of our study is the comparison between different delay formulations in the transmission term, specifically contrasting the standard approach—where the delay acts only on the infected compartment—with alternative formulations that distribute the delay across both susceptible and infected populations. Through systematic numerical simulations, we find that the choice of delay formulation strongly influences the timing and magnitude of the initial epidemic peak, while the long-term (asymptotic) behavior is more robust but remains sensitive to the underlying stochastic framework. The probabilistic model, in particular, offers a more faithful depiction of correlated fluctuations and extinction phenomena, capturing the biological complexity of epidemic processes more accurately than the classical approach. These results underscore the importance of both the delay representation and the stochastic modeling strategy in shaping the qualitative and quantitative features of epidemic dynamics.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 16-28"},"PeriodicalIF":2.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019349","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":"Understanding human mobility patterns under a public health emergency","authors":"Cheng Peng ,&nbsp;Nana Chen ,&nbsp;Bo-Wen Ming ,&nbsp;Anqi Zhang ,&nbsp;Yao Zuo ,&nbsp;Paulo C. Ventura ,&nbsp;Hongjie Yu ,&nbsp;Marco Ajelli ,&nbsp;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":"2026-03-01","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}