Infectious Disease Modelling最新文献

{"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":"2026-09-01","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":"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":"2026-09-01","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":"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":"2026-09-01","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":"Dynamics and forecasting of an age-structured stochastic SIR model with Lévy perturbations via physics-informed neural networks","authors":"Ge Zhang ,&nbsp;Zhihao Wang ,&nbsp;Zhiming Li ,&nbsp;Shenglong Chen ,&nbsp;Qiaoling Chen","doi":"10.1016/j.idm.2025.11.003","DOIUrl":"10.1016/j.idm.2025.11.003","url":null,"abstract":"<div><div>Understanding and predicting real-world epidemic dynamics has consistently posed a formidable challenge. This study addresses an age-structured stochastic SIR model incorporating a general incidence rate, high-order white noise, and Lévy jump perturbations. By employing Lyapunov function method, we establish the existence and uniqueness of a global positive solution. Furthermore, we derive a stochastic threshold that delineates the conditions for disease persistence and extinction. Moreover, the existence and uniqueness of a stationary distribution are proven by applying an improved version of Hasminskii's theory. Numerical simulations based on the positivity- and boundedness-preserving Euler–Maruyama scheme corroborate the theoretical results, showing that reducing the amplitude of higher-order noise amplifies the infection burden, whereas increasing the age-structure parameters <em>ϑ</em> and <em>ς</em> markedly suppresses transmission. Finally, the efficacy of physics-informed neural network based on stochastic SIR model (PINN-SIR), is demonstrated through its application to the fitting and forecasting of COVID-19 case in Zhejiang, China. The method shows promise for extension to more complex dynamical systems and diseases.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 407-427"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658882","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":"Absolute humidity drives seasonal influenza A transmission in Hong Kong through social contact modulation: Evidence from compartmental modeling","authors":"Guanlin Ou ,&nbsp;Wenjun Ma ,&nbsp;Yanying Mo ,&nbsp;Jianxiong Hu ,&nbsp;Tian Tang","doi":"10.1016/j.idm.2025.12.001","DOIUrl":"10.1016/j.idm.2025.12.001","url":null,"abstract":"<div><h3>Background</h3><div>Prior studies propose a U-shaped humidity-influenza relationship, yet the interplay between humidity-driven contact behaviors and transmission dynamics remains unclear.</div></div><div><h3>Objective</h3><div>The study investigates how absolute humidity (AH) modulates social contact (SC) to drive influenza A transmission, quantifies the relative contributions of AH-mediated contact behavior versus viral survivability, and identifies optimal contact-reduction strategies for outbreak control.</div></div><div><h3>Methods</h3><div>WHO FluNet data (2016–2024), Hong Kong contact surveys, and meteorological records into a genetic algorithm-optimized SEIR model were integrated. The framework dynamically simulates dual AH-dependent transmission mechanisms (behavioral and environmental), evaluates optimal contact-reduction strategies via incidence minimization, and employs LHS/PRCC sensitivity analysis to identify key drivers.</div></div><div><h3>Results</h3><div>Seasonal changes in AH induce cyclical fluctuations in social contact, thereby modulating the influenza A transmission dynamics. The potential effect of AH-driven SC patterns on influenza A has gradually diminished. The GA-optimized SEIR dynamic reveals seasonally heterogeneous requirements for control strategies. The highest risk for outbreak initiation is posed in winter. Contact intervention can reach its peak in winter (intervention intensity reaches 62 %) and summer (intervention intensity is between 16 % and 23 %). Sensitivity analysis highlighted Absolute humidity-modulated infection effect and recovery rate as dominant drivers.</div></div><div><h3>Conclusions</h3><div>The association between absolute humidity and influenza transmission can be attributed to humidity-driven shifts in social contact. This necessitates seasonally tailored interventions: winter strategies should prioritize stringent contact restrictions, while warmer seasons permit relaxed measures. Future models should integrate multi-climate zone validation and dynamic behavioral sensing to improve outbreak predictions.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 671-682"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791226","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":"2026-06-01","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":"Modeling and control of Chikungunya with chronic infection","authors":"Yan Wang ,&nbsp;Huan Ma ,&nbsp;Qian Yan ,&nbsp;Zhichun Yang","doi":"10.1016/j.idm.2025.12.002","DOIUrl":"10.1016/j.idm.2025.12.002","url":null,"abstract":"<div><div>Recognized globally as a major public health concern in the tropics and subtropics, Chikungunya fever also poses a potential epidemic risk in areas of China such as Guangdong Province, where suitable mosquito vector habitats exist. Based on a Chikungunya fever outbreak in Shunde District, Foshan City, this study develops a dynamical model incorporating a chronic infection stage. We derive <em>R</em>₀ and perform a thorough stability analysis of all equilibria. Using daily reported case data from Shunde District, model fitting yields estimates for three key transmission parameters (<em>β</em>, <em>ρ</em>₁, <em>ρ</em>₂), the total mosquito population (<em>T</em>_<em>v</em>), and the initial number of infected mosquitoes (<em>I</em>_<em>v</em>(0)). Sensitivity analysis identifies that the primary positive and negative parameters on disease transmission are mosquito biting rate (<em>β</em>) and mosquito mortality rate (<em>ϵ</em>_<em>v</em>), respectively. Accordingly, five types of intervention measures are designed: personal protection, screening and detection, treatment of acute patients, management of chronic cases, and mosquito vector control measures. Based on these findings, we formulate a control framework to optimize intervention strategies. Numerical simulations not only validate the global asymptotic stability of the disease-free equilibrium when <em>R</em>₀ &lt; 1 and that of the endemic equilibrium when <em>R</em>₀ &gt; 1, but also assess the effectiveness of different control strategies. Strategy A, which emphasizes personal protection, emerges as the most economically efficient option in the cost-effectiveness analysis. It not only effectively interrupts virus transmission but also optimally reduces the burden of chronic cases, thereby offering a scientifically sound and economically feasible approach for public health resource allocation.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 619-642"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791225","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":"Warming temperatures reduce lifespan and vectorial capacity of Anopheles mosquitoes in Ghana","authors":"Edmund I. Yamba ,&nbsp;Kingsley Badu ,&nbsp;Thomas A. Kyeimiah ,&nbsp;Nathaniel O. Abrokwah ,&nbsp;Stephen Asare ,&nbsp;Mary J. Adjei ,&nbsp;Joyce Ama Johnson ,&nbsp;Leonard K. Amekudzi","doi":"10.1016/j.idm.2025.12.011","DOIUrl":"10.1016/j.idm.2025.12.011","url":null,"abstract":"<div><div>Climate change and variability are altering the ecology of malaria vectors, with implications for disease transmission in sub-Saharan Africa. In this study, we analysed long-term historical temperature, rainfall and relative humidity data across Ghana's climatic zones to evaluate their trends. We then incorporated these data into simple climate-driven biological models to assess how they impacted Anopheles mosquito lifespan, their Vectorial Capacity and Extrinsic Incubation Period of malaria parasites. This approach allowed us to assess the potential impacts of climate change on malaria transmission dynamics in the country. The analysis revealed, on the long-term, significant temperature warming (over 1.5°C), marked decline in relative humidity, and no clear trends in rainfall across all climatic zones. Similarly, Anopheles mosquito lifespan (with seasonal variations of 5–11 days in the north and 9–14 days in the south) showed long-term decline while Extrinsic Incubation Period (with seasonal average range of 6–11 days in the north and up to 13 days in the south) showed shortened development time. Even though Vectorial Capacity showed no clear long-term trends, its values were generally below 10, indicating low-to-moderate malaria transmission potential nationwide. Although regional and local microclimatic variations may continue to support localized malaria transmission risk, the long-term rise in temperatures and decline in humidity are likely reducing mosquito longevity and malaria transmission potential in Ghana. These findings underscore the importance of climate-informed and region-specific strategies in the National Malaria Elimination Program to improve targeted interventions and optimize vector control efforts.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 2","pages":"Pages 652-670"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791227","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":"2026-06-01","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":"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}