Manting Wang,P van den Driessche,Laura L E Cowen et al.
Manting Wang et al.
Accurate estimation of the initial growth rate of an epidemic is critical for assessing transmissibility and guiding early interventions. Standard regression-based methods, such as negative binomial regression, often rely on independence as...
From qualitative prediction to quantitative insight: combined meteorological patterns and regional dynamics of severe fever with thrombocytopenia syndrome in Liaoning Province, China, 2010-2024 [0.03%]
从定性预测到定量洞察：中国辽宁省严重发热伴血小板减少综合征的气象模式与区域动态（2010-2024年）
Ning Yu,Baocheng Deng,Xue Zhang
Ning Yu
Background: 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 predict...
Ensemble-labeling of infectious disease time series to evaluate early warning systems [0.03%]
传染病时间序列的群体标签化以评估早期预警系统
Andreas Hicketier,Moritz Bach,Philip Oedi et al.
Andreas Hicketier et al.
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 s...
Simulating treatment effects for gonorrhoea using a within-host mathematical model [0.03%]
利用宿主体内数学模型模拟淋病的治疗效果
Pavithra Jayasundara,David G Regan,Philip Kuchel et al.
Pavithra Jayasundara et al.
Neisseria gonorrhoeae (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 mode...
Within host dynamics of HPV infection with cellular immunity and HPV-infected dormant cells reactivation [0.03%]
带有细胞免疫和人乳头瘤病毒潜伏感染细胞再激活的HPV感染的体内动力学
Michael Chapwanya,Adèle Claire Fouape,Berge Tsanou
Michael Chapwanya
Like other viruses, human papillomavirus genotypes can remain dormant for years or decades and later reactivate due to some well-known factors. The activation of such a dormant infection years later can cause many health and behavioural pro...
Age-structured next generation matrix and R0 calculation for Mycobacterium avium subsp. paratuberculosis (MAP) [0.03%]
MAP的年龄分层下一代矩阵和R0计算公式
Yuqi Gao,Nienke Hartemink,Piter Bijma et al.
Yuqi Gao et al.
Paratuberculosis (Johne's disease), caused by Mycobacterium avium subspecies paratuberculosis (MAP), is known for its age-specific susceptibility, lifelong infection, varied shedding patterns, low diagnostic sensitivity, long latent period,...
A coupled disease-misinformation model of measles transmission in the Canadian context [0.03%]
加拿大麻疹传播的疾病与虚假信息耦合模型
Callandra Moore,David Fisman
Callandra Moore
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 ...
A spatio-temporal causal network for multi-scale analysis of infectious respiratory diseases transmission [0.03%]
一种时空因果网络：用于呼吸道传染病传播的多尺度分析
Xincao Zheng,Wenjing Yu,Lu Wang et al.
Xincao Zheng et al.
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 dir...
Multi-event dynamic capture-recapture model for big data: Estimating undetected COVID-19 cases in British Columbia, Canada [0.03%]
基于大型数据的多事件动态捕获-标记-回收模型在加拿大不列颠哥伦比亚省估计未检测到的COVID-19病例数
Kehinde Olobatuyi,Junling Ma,Patrick Brown et al.
Kehinde Olobatuyi et al.
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, ...
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] [0.03%]
关于“巴西严重急性呼吸系统综合征的贝叶斯时空模型：与COVID-19前、期间和后的比较分析”的勘误表 [感染病建模，10(2)(2025)466-476]
Rodrigo de Souza Bulhões,Jonatha Sousa Pimentel,Paulo Canas Rodrigues
Rodrigo de Souza Bulhões
[This corrects the article DOI: 10.1016/j.idm.2024.12.010.]. © 2024 The Authors.
Published Erratum
Infectious Disease Modelling. 2025 Dec 30;11(2):751. DOI:10.1016/j.idm.2025.12.017 2025