Infectious Disease Modelling最新文献

{"title":"FluAttn: Antigenicity prediction of influenza A/H3N2 through attention-based feature mining","authors":"Li Geng ,&nbsp;Jun He ,&nbsp;Ping Liu","doi":"10.1016/j.idm.2025.11.005","DOIUrl":"10.1016/j.idm.2025.11.005","url":null,"abstract":"<div><div>The rapid antigenic drift of influenza A/H3N2 compromises the durability of vaccine-induced protection, underscoring the need for accurate antigenic assessment to evaluate vaccine efficacy and guide vaccine updates. Although the hemagglutination inhibition (HI) assay remains the gold standard for antigenic characterization, its labor-intensive and time-consuming procedures hinder large-scale application. Sequence-based computational approaches have therefore emerged as high-throughput and cost-effective complements to the HI assay. However, most existing methods insufficiently exploit differences in the intrinsic properties of amino acids across sequence positions, constraining advances in antigenicity prediction. To address this limitation, we propose FluAttn, an attention-based feature mining framework that automatically identifies and integrates antigenicity-relevant features from various amino acid property datasets. FluAttn not only allows for customizable feature scales but also simultaneously quantifies the differential contributions of these features during the mining process, thereby facilitating synergistic feature integration and enabling high-precision prediction of antigenic distances between A/H3N2 influenza viruses. Evaluation on datasets covering the periods 1963–2003 and 2003–2025 demonstrates that FluAttn significantly outperforms existing methods in both accuracy and robustness, providing a cost-effective and reliable framework for early antigenic characterization and vaccine candidate screening.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 428-437"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658883","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":"Urbanization influences hemorrhagic fever with renal syndrome transmission: 34-year evidence from China's national surveillance","authors":"Junyu He ,&nbsp;Yanding Wang ,&nbsp;Xiaopeng Xu ,&nbsp;George Christakos ,&nbsp;Danjie Zhang ,&nbsp;Yuanyong Xu ,&nbsp;Qiulan Chen ,&nbsp;Wenyi Zhang","doi":"10.1016/j.idm.2025.12.004","DOIUrl":"10.1016/j.idm.2025.12.004","url":null,"abstract":"<div><h3>Background</h3><div>Mainland China accounts for over 90 % of the global hemorrhagic fever with renal syndrome (HFRS) cases, yet quantitative relationships between climate, urbanization and transmission dynamics remain poorly understood across national scales.</div></div><div><h3>Methods</h3><div>We analyzed 34 years of HFRS surveillance data (1985–2018) from 31 provincial-level administrative divisions in China to examine the associations with climatic variables, socioeconomic indicators, and land use types using Bayesian nonlinear mixed-effects models. Dominance analysis was conducted to quantify the relative importance of each predictor. Additionally, linear mixed-effects and generalized additive models were implemented for comparative and validation purposes.</div></div><div><h3>Findings</h3><div>Annual HFRS incidence declined sharply from a peak of 10.99 cases/10⁵ in 1986 to fewer than 0.98 cases/10⁵ after 2010, with the top four highest annual averaged HFRS incidence cases reported at the provinces of Heilongjiang, Shandong, Shaanxi and Zhejiang. Bayesian models demonstrated excellent predictive performance (R² = 0.8722 and 0.8592 for early/late periods, i.e., 1985–2004 and 2005–2018, respectively). Before 2005, impervious surfaces, population and wetlands emerged as the top three dominant transmission predictors. After 2005, however, the key predictors shifted, with wetlands, the Palmer Drought Severity Index (PDSI), and impervious surfaces having the highest relative importance.</div></div><div><h3>Interpretation</h3><div>The quantification of urbanization is provided through impervious surface expansion and wetlands changes, which represent the primary predictors of HFRS transmission in China, likely operating through rodent habitat modification and altered human-wildlife contact patterns. The emerging wetland influence suggests that environmental policies are reshaping disease dynamics. Our findings support urbanization-targeted prevention strategies across the Western Pacific region and highlight integrating land use surveillance into regional infectious disease monitoring systems.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 575-585"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738324","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":"Spatio-temporal forecasting of dengue in the Americas through hybrid mechanistic and data-driven models: Systematic review and meta-analysis","authors":"Jenniffer Alejandra Castellanos Garzón,&nbsp;Luis Fernando Plaza Gálvez,&nbsp;Kelly Fernanda Plaza Bastidas,&nbsp;Julián Eduardo Betancur Agudelo,&nbsp;Andrés Rey Piedrahita","doi":"10.1016/j.idm.2025.12.005","DOIUrl":"10.1016/j.idm.2025.12.005","url":null,"abstract":"<div><div>This systematic review and meta-analysis (PROSPERO: CRD420251130769) synthesises 30 dengue modelling studies conducted in the Americas between 2016 and 2025, evaluating the integration of mechanistic and data-driven approaches. We quantified the reliability of diverse modelling frameworks by applying a Standardised Predictive Fidelity Index (SPFI). Our synthesis reveals a robust positive association between temperature and dengue risk across all methodologies (pooled relative risk (RR) = 1.26 [95 % confidence interval (CI): 1.18–1.35]). However, a critical performance dichotomy remains: while mechanistic models exhibit high variance dependent on calibration quality, temporal regression analysis confirms that machine learning architectures have achieved statistically significant convergence towards high predictive fidelity (median SPFI: 0.89) since 2023. Despite their precision, data-driven models remain disconnected from the causal logic necessary for intervention simulation. To address this methodological fragmentation, we have developed a functional “glass-box” hybrid architecture, which is defined by three evidence-based pathways: the dynamic parameterisation of mechanistic cores via machine learning; the enforcement of biological constraints on predictive algorithms; and the continuous assimilation of data. We conclude that transitioning from descriptive science to this operational, data-assimilating hybrid framework is essential for enabling precise, location-specific public health responses to the escalating dengue crisis in the Americas.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 586-602"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791245","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 frequentist and Bayesian methods to identify drivers of pathogen strain invasion: A temporal case study of pertussis in the United States","authors":"Florian Lecorvaisier,&nbsp;Dominique Pontier,&nbsp;Frank Sauvage,&nbsp;David Fouchet","doi":"10.1016/j.idm.2025.09.007","DOIUrl":"10.1016/j.idm.2025.09.007","url":null,"abstract":"<div><div>Since the 20th century, it has been widely recognized that the emergence of new pathogens is closely linked to human activities such as global travel and environmental exploitation. In addition, the widespread use of antibiotics and vaccines has contributed to the evolution and dissemination of new pathogen variants. However, the role of environmental and socio-demographic cofactors on the dynamics of pathogen spread remains insufficiently explored. In this study, we argue that such influences are best captured using mixed logistic regression models that incorporate temporally autocorrelated random effects, in order to reflect the complex and time-dependent nature of strain invasion processes. To address the statistical challenges of this framework, we compared two approaches: (i) a simplified model with independent random effects and frequentist inference, and (ii) a full model accounting for temporal autocorrelation, estimated using Bayesian inference. Our results show that the simplified model, although commonly used in longitudinal analyses, substantially underestimates the probability of detecting false-positive associations (i.e., it underestimates the Type I error rate), leading to potentially misleading conclusions. In contrast, the full Bayesian model avoids this bias and offers a more robust alternative. We applied this approach to a dataset monitoring the emergence of vaccine-escape <em>Bordetella pertussis</em> strains in the United States between 2007 and 2017. Among the eight cofactors tested, only temperature was significantly associated with the rate of strain invasion. Further simulation-based analyses revealed that the current dataset has limited statistical power to detect such associations. However, our results suggest that increasing the temporal resolution of data collection could substantially improve the model's ability to detect meaningful associations – without increasing surveillance costs.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 389-406"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658881","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":"Stochastic dynamics of Chikungunya virus infection model incorporating general incidence rate and immune responses","authors":"Jingze Ma,&nbsp;Yan Wang","doi":"10.1016/j.idm.2025.11.007","DOIUrl":"10.1016/j.idm.2025.11.007","url":null,"abstract":"<div><div>This study investigates a stochastic model of Chikungunya virus (CHIKV) infection that incorporates a general incidence rate along with B-cell and CTL immune responses. Stochasticity is modeled through a log-normal Ornstein-Uhlenbeck process. We first establish the existence of a unique and globally positive solution. Then, the solution's dynamic behavior around the two steady states is examined, and it is shown that the stochastic model's dynamics at the steady state generalizes the global asymptotic stability of the deterministic model. We prove the existence of the stationary distribution by constructing suitable Lyapunov functions when the stochastic reproduction number is greater than one. The probability density function near the quasi-steady state is subsequently derived. Sufficient conditions for CHIKV extinction are provided by spectral radius analysis. Furthermore, we conduct uncertainty and sensitivity analyses to investigate the effects of key parameters on each population and the value of the stochastic reproduction number. Finally, numerical simulations are carried out to explore the impact of noise intensity and the average incidence rate on the dynamic behavior of the model.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 438-476"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658880","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 spatio-temporal causal network for multi-scale analysis of infectious respiratory diseases transmission","authors":"Xincao Zheng ,&nbsp;Wenjing Yu ,&nbsp;Lu Wang ,&nbsp;Jiaoe Wang ,&nbsp;Yilan Liao ,&nbsp;LingCai Kong","doi":"10.1016/j.idm.2025.12.018","DOIUrl":"10.1016/j.idm.2025.12.018","url":null,"abstract":"<div><div>Understanding the spatio-temporal transmission characteristics of infectious respiratory diseases is crucial for effective control. However, most existing studies rely on correlation analysis, which obscures the true causal pathways and directionality of infectious respiratory disease transmission, preventing accurate identification of epidemic sources and sinks. To address these challenges, we proposed a novel spatio-temporal causal analysis framework. First, a spatio-temporal causal network is constructed using the Convergent Cross Mapping (CCM) model. This method effectively overcomes the limitations of traditional correlation analysis in identifying spurious correlations and determining causal direction. Subsequently, the weighted k-shell decomposition and Louvain algorithm are applied to analyze the multi-scale structural characteristics of the network, including critical paths, core nodes, and community structures, revealing the multi-scale transmission patterns of the system. We conducted a case study using influenza data from 30 provinces in mainland China from 2010 to 2018. A total of 120 directional transmission pathways were identified, primarily driven by interprovincial population mobility, showing an 83.9 % concordance with the results of the Bayesian phylogenetic analysis. Moreover, provincial importance in transmission was found to be highly correlated with the Hu Huanyong Line. This study provided new insights into the causal relationships and multi-scale structure of infectious disease transmission, offering an important reference for formulating targeted regional prevention and control strategies.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 796-805"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022636","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":"Study on the resurgence of pertussis based on a stage-structured dynamic model","authors":"Yifei Qiao,&nbsp;Jijun Zhao","doi":"10.1016/j.idm.2025.12.007","DOIUrl":"10.1016/j.idm.2025.12.007","url":null,"abstract":"<div><div>Although pertussis vaccination has effectively reduced the global incidence rate and mortality, pertussis resurgence has been observed in many countries in recent years. This study aims to untangle the changes in dynamic transmission characteristics before and after pertussis resurgence in high-incidence provinces of China and to explore the contributing factors and potential control measures. Shandong, Sichuan, and Zhejiang provinces were selected as study subjects. Based on monthly cases data from 2004 to 2022, dynamic models incorporating different waning immunity were constructed to identify the model that most appropriately reflected the epidemic dynamics in these provinces. From 2004 to 2013 in Shandong Province, and from 2004 to 2017 in Zhejiang and Sichuan Province, the Susceptible-Vaccinated-Infected-Recovered model (SVIR) best captured the epidemic dynamics in the three provinces, with an estimated vaccine protection duration of approximately 7–11 years. After the resurgence, the Susceptible-Vaccinated-Infected-Recovered-Susceptible model (SVIRS) more accurately represented the epidemic dynamics across the three provinces, with vaccine protection lasting 7–13 years and natural immunity persisting for 16–24 years, indicating the absence of lifelong immunity. Moreover, we found increases in the average transmission rate, case reporting, and vaccine effectiveness for pertussis. The observed resurgence of pertussis in the three provinces in China is affected by multiple factors, including elevated transmission rates, improved reporting rate, and vaccine-induced immunity waning. To mitigate resurgence, booster vaccination strategies targeting adolescents and adults should be considered.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 643-651"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791223","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":"Determining disease attributes from epidemic trajectories","authors":"Mark P. Rast,&nbsp;Luke I. Rast","doi":"10.1016/j.idm.2025.12.008","DOIUrl":"10.1016/j.idm.2025.12.008","url":null,"abstract":"<div><div>Effective public health decisions require early reliable inference of infectious disease properties. In this paper we assess the ability to infer infectious disease attributes from population-level stochastic epidemic trajectories. In particular, we construct stochastic Kermack-McKendrick model trajectories, sample them with and without observational error, and evaluate inversions for the population mean infectiousness as a function of time since infection, the infection duration distribution, and its complementary cumulative distribution, the infection survival distribution. Based on the integro-differential equation formulation for a well-mixed closed population we employ Poisson GLM regression to find the corresponding integral kernels, and show that these disease attributes are recoverable from both multi-trajectory and regularized single trajectory inversions. Moreover, we demonstrate that the infection duration distribution (or alternatively the infection survival distribution) and population mean infectiousness kernel recovered can be used to solve for the individual infectiousness profile, the infectiousness of an individual over the duration of their infection, assuming that individual infectiousness profiles are self-similar across individuals over the infection duration period. The work suggests that aggressive monitoring of the stochastic evolution of a novel infectious disease outbreak in a single local well-mixed population can allow determination of the underlying disease attributes that characterize its spread.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 719-736"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884010","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":"Dengue fever prediction based on meteorological features and deep learning models","authors":"Yunyun Cheng ,&nbsp;Rong Cheng ,&nbsp;Ting Xu ,&nbsp;Xiuhui Tan ,&nbsp;Yanping Bai","doi":"10.1016/j.idm.2025.12.010","DOIUrl":"10.1016/j.idm.2025.12.010","url":null,"abstract":"<div><div>The dengue fever epidemic is one of the health priorities of the World Health Organization (WHO), and accurately predicting its epidemiological trends is crucial. Multi source geographic data such as temperature, humidity, and precipitation affect the occurrence and prevalence of dengue fever. Therefore, an effective hybrid model to improve the prediction performance of dengue fever is proposed considering the effects of multidimensional meteorological features. Initially, to address the issue of data scarcity, the Time-series Generative Adversarial Networks (TimeGAN) algorithm is employed to expand the dengue dataset. Second, the meteorological series are decomposed by Symplectic Geometry Mode Decomposition (SGMD), and then the sub-sequences are reconstructed into high, mid and low frequency signals by utilizing Sample Entropy (SE). Subsequently, a sliding window technique is applied to the signal band to accurately capture the key time periods affecting dengue fever. Finally, bidirectional temporal features related to the dengue sequence are extracted and fused by a bidirectional temporal convolutional network (BiTCN), and the fused features are inputted into a bidirectional long and short-term memory network (BiLSTM) introduced the attention module for prediction, and to obtain the final prediction results. Using the data of dengue fever cases in Guangdong Province, China, as an example, the experimental results show that the developed method can accurately predict the trend of dengue fever epidemic, with mean absolute error (MAE) and mean absolute percentage error (MAPE) of 192.98759 and 2.492, respectively.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 683-700"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884012","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}