Infectious Disease Modelling最新文献

{"title":"From qualitative prediction to quantitative insight: combined meteorological patterns and regional dynamics of severe fever with thrombocytopenia syndrome in Liaoning Province, China, 2010–2024","authors":"Ning Yu ,&nbsp;Baocheng Deng ,&nbsp;Xue Zhang","doi":"10.1016/j.idm.2026.01.001","DOIUrl":"10.1016/j.idm.2026.01.001","url":null,"abstract":"<div><h3>Background</h3><div>Severe fever with thrombocytopenia syndrome(SFTS) is an emerging tick-borne disease with an expanding range and increasing public health burden. Meteorology-driven frameworks that integrate qualitative prediction with quantitative risk estimation while accommodating lag, regional heterogeneity, autoregressive case count effects, and zero-inflated counts remain scarce.</div></div><div><h3>Methods</h3><div>Monthly SFTS case counts and meteorological data from thirteen prefecture-level cities in Liaoning Province, China, from 2010 to 2024 were analyzed. Fushun was excluded because all counts were zero. Predictors were screened by correlation and variance inflation factor (VIF), and Boruta plus conditional permutation importance selected nine variables. Cities were grouped by k-means clustering. Four algorithms, including random forest (RF), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), and light gradient boosting machine (LightGBM), classified case presence using 2010–2022 training with ten-fold cross-validation and 2023–2024 testing. Shapley additive explanations (SHAP) interpreted variable importance and lagged associations in Dalian and Dandong. A mixed generalized additive model (MGAM) with distributed lag nonlinear modeling (DLNM) estimated exposure-lag effects of each meteorological main exposure.</div></div><div><h3>Results</h3><div>Nine meteorological variables were retained: wind speed (WS), relative humidity (RH), precipitation (PRCP), air pressure(AP), sunshine duration (SD), diurnal temperature range (DTR), surface air temperature difference (STD), standardized precipitation evapotranspiration at one month (SPEI1), and six months (SPEI6). K-means clustering grouped the thirteen Liaoning cities into three climatic groups. Across four classifiers, RF performed best in high-incidence areas, XGBoost was most stable; SHAP revealed opposite lag effects for some variables, indicating nonlinear delayed influences. Quantitative risk estimation selected the optimal covariates for each main exposure, characterized exposure response shapes: inverted U for WS, AP, PRCP, DTR, and SPEI6; monotonic increase for RH and SD; monotonic decrease for STD; bimodal for SPEI1.</div></div><div><h3>Conclusions</h3><div>This study identifies meteorological heterogeneity in high-incidence regions while quantifying province-wide risk windows for each meteorological exposure, thereby informing regional and provincial prevention and early warning strategies.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 3","pages":"Pages 807-822"},"PeriodicalIF":2.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116516","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":"Simulating treatment effects for gonorrhoea using a within-host mathematical model","authors":"Pavithra Jayasundara ,&nbsp;David G. Regan ,&nbsp;Philip Kuchel ,&nbsp;James G. Wood","doi":"10.1016/j.idm.2026.01.002","DOIUrl":"10.1016/j.idm.2026.01.002","url":null,"abstract":"<div><div><em>Neisseria gonorrhoeae</em> (NG) bacteria have evolved resistance to many of the antibiotics used to treat gonorrhoea infection. To explore potential treatment options for gonorrhoea, we extend a previously developed within-host mathematical model to integrate treatment dynamics by accounting for key pharmacokinetic (PK) and pharmacodynamic (PD) features. This extended model was used to investigate different treatment regimens for two potential drugs: monotreatment with gepotidacin, and dual treatment with gentamicin and azithromycin. The simulated treatment success rates aligned well with the limited clinical trial data available. The simulation results indicated that antibiotic treatment failure is associated with failure to successfully clear intracellular NG (NG residing within epithelial cells and neutrophils), and extracellular PK indices alone cannot differentiate between treatment success/failure. Also, the index defined by the ratio of area under the curve to minimum inhibitory concentration (AUC/MIC) index &gt;150, evaluated using intracellular gepotidacin concentration, successfully distinguished between treatment success and failure. For the dual treatment regimen, AUC/MIC index &gt;140 evaluated using the simulated single drug concentration, representing the combined effect of gentamicin and azithromycin with the Loewe additivity concept, successfully differentiated between treatment success and failure. However, we found this PK threshold associated with dual treatment to be less informative than that of gepotidacin, as a majority of samples below this threshold still resulted in infection clearance. Although previous experimental results on antibiotic killing of intracellular NG are scarce, our findings highlight the need for further studies on this. This will be useful for testing putative new anti-gonorrhoea antibiotics.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 3","pages":"Pages 840-853"},"PeriodicalIF":2.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116517","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 coupled disease-misinformation model of measles transmission in the Canadian context","authors":"Callandra Moore,&nbsp;David Fisman","doi":"10.1016/j.idm.2025.12.014","DOIUrl":"10.1016/j.idm.2025.12.014","url":null,"abstract":"<div><div>Infectious disease dynamics are increasingly shaped not only by biological processes but also by the spread of misinformation. This study presents a coupled disease-misinformation model, SMIRK, to evaluate the impact of misinformation on a recent measles outbreak in Canada. The novel model combines a standard SIR framework for measles transmission with an SIS-like KMK model for misinformation spread. Two misinformation recovery paradigms are explored: constant-rate recovery and recovery induced by contact with infected individuals. Parameters were estimated using least-squares fitting to epidemiological data from Health Canada and Public Health Ontario (December 2024 to March 2025). Model calibration suggests that misinformation rapidly reaches a stable equilibrium, effectively saturating the population regardless of recovery paradigm. Due to the rapid information homogenization of the population, measles is predicted to behave as a standard low-<em>R</em>₀ infection under most calibrations. Despite its limitations, the SMIRK model offers a proof of concept for integrating misinformation into epidemiological models, underscoring the need for more nuanced modeling of informational dynamics in public health forecasting.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 787-795"},"PeriodicalIF":2.5,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022635","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":"Multi-event dynamic capture-recapture model for big data: Estimating undetected COVID-19 cases in British Columbia, Canada","authors":"Kehinde Olobatuyi ,&nbsp;Junling Ma ,&nbsp;Patrick Brown ,&nbsp;Laura L.E. Cowen","doi":"10.1016/j.idm.2025.12.016","DOIUrl":"10.1016/j.idm.2025.12.016","url":null,"abstract":"<div><div>The accurate quantification of the impact of COVID-19 pandemic on both public health and the economy is essential for informed policy-making. However, the true scope of the pandemic remains challenging to ascertain due to undetected cases, particularly when relying on reported cases, which rely heavily on test availability and strategies. To accurately quantify COVID-19 cases in British Columbia (BC), we develop a Susceptible-Infectious-Recovered multi-event capture-recapture (SIRMECR) model to capture the dynamics of COVID-19. Specifically, we present a time-varying Markov model to estimate the number of undetected COVID-19 cases in five Health Authority Regions in BC, Canada, during the year 2020. We utilize individual-level information available from Population Data BC database to estimate the case detection probability, infection probability, survival probability, and recovery probability by incorporating testing volumes as covariates that improve the estimate of our parameters. We develop a Markov chain Monte Carlo (MCMC) algorithm to estimate SIRMECR model parameters. However, analyzing this big COVID-19 data set prompts a discussion on the computational challenges encountered. Therefore, we developed divide-and-conquer strategies to address the challenges. Our application provides an estimate of the total COVID-19 burden in year 2020 and found the percentage of undetected varying from 77.4 % to 84.0 %. More specifically, we validate our results through a simulation study and N-mixture model for Northern Health Authority Region of BC.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 764-786"},"PeriodicalIF":2.5,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925760","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":"2026-01-02","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":"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":"2025-12-30","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":"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":"2025-12-30","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":"Ensemble-labeling of infectious disease time series to evaluate early warning systems","authors":"Andreas Hicketier ,&nbsp;Moritz Bach ,&nbsp;Philip Oedi ,&nbsp;Alexander Ullrich ,&nbsp;Auss Abbood","doi":"10.1016/j.idm.2025.12.013","DOIUrl":"10.1016/j.idm.2025.12.013","url":null,"abstract":"<div><div>Early warning systems (EWSs) for detecting disease outbreaks can help make informed public health decisions and organize necessary responses. During the COVID-19 pandemic, several EWSs were proposed that use covariates such as mobility or social media data for improved timeliness and precision. Evaluating these EWSs is not trivial, since we do not have the ground truth knowledge about outbreaks of COVID-19. Workarounds for missing labels are to simulate them or produce them post hoc. Simulating COVID-19 outbreaks for evaluation is not feasible with highly complex covariates such as mobility. Furthermore, existing post hoc labeling methods do not perform well on heterogeneous COVID-19 time series. To address this evaluation gap, we propose an adaptive labeling method that produces useful labels (time-indexed annotations marking outbreak-like periods) for highly heterogeneous, nonstationary COVID-19 time series. To this end, we develop a customized ensemble of labeling methods. We find that our method consistently produces useful labels for various outbreak types, such as waves and short peaks occurring at different spatial resolutions. Lastly, we use our self-produced labels to train machine learning models and compare their performance with traditional outbreak detection methods. We find that models trained with our labels outperform classical, unsupervised outbreak detection algorithms.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 3","pages":"Pages 823-839"},"PeriodicalIF":2.5,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116543","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":"Epidemiological model calibration via graybox Bayesian optimization","authors":"Puhua Niu ,&nbsp;Byung-Jun Yoon ,&nbsp;Xiaoning Qian","doi":"10.1016/j.idm.2025.12.012","DOIUrl":"10.1016/j.idm.2025.12.012","url":null,"abstract":"<div><div>In this study, we focus on developing efficient calibration methods via Bayesian decision-making for the family of compartmental epidemiological models. The existing calibration methods usually assume that the compartmental model is <em>cheap</em> in terms of its output and gradient evaluation, which may not hold in practice when extending them to more general settings. Therefore, we introduce model calibration methods based on a “graybox” Bayesian optimization (BO) scheme, to enable more efficient calibration for general epidemiological models. This approach uses Gaussian processes as a surrogate to the expensive model, and leverages the functional structure of the compartmental model to enhance calibration performance. Additionally, we develop model calibration methods via a decoupled decision-making strategy for BO, which further exploits the decomposable nature of the functional structure. The calibration efficiencies of the multiple proposed schemes are evaluated based on various data generated by a compartmental model mimicking real-world epidemic processes and COVID-19 datasets. Experimental results demonstrate that our proposed graybox variants of BO schemes can efficiently calibrate computationally <em>expensive</em> models and further improve the calibration performance measured by the logarithm of mean squared errors and achieve faster performance convergence in terms of BO iterations. We anticipate that the proposed calibration methods can be extended to enable fast calibration of more complex epidemiological models, such as the agent-based models.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 737-750"},"PeriodicalIF":2.5,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884007","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":"2025-12-18","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}