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Warming temperatures reduce lifespan and vectorial capacity of Anopheles mosquitoes in Ghana 变暖的气温减少了加纳按蚊的寿命和媒介能力
IF 2.5 3区 医学 Q1 Medicine Pub Date : 2025-12-15 DOI: 10.1016/j.idm.2025.12.011
Edmund I. Yamba , Kingsley Badu , Thomas A. Kyeimiah , Nathaniel O. Abrokwah , Stephen Asare , Mary J. Adjei , Joyce Ama Johnson , Leonard K. Amekudzi
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.
气候变化和变异正在改变疟疾病媒的生态,对撒哈拉以南非洲的疾病传播产生影响。在这项研究中,我们分析了加纳气气带的长期历史温度、降雨和相对湿度数据,以评估它们的趋势。然后,我们将这些数据纳入简单的气候驱动生物学模型,以评估它们如何影响按蚊的寿命、它们的媒介能力和疟原虫的外在潜伏期。这种方法使我们能够评估气候变化对该国疟疾传播动态的潜在影响。分析显示,在长期内,显著的温度变暖(超过1.5°C),相对湿度明显下降,所有气候带的降雨量没有明显的趋势。按蚊寿命(北方5 ~ 11天,南方9 ~ 14天)长期下降,外部潜伏期(北方6 ~ 11天,南方13天)发育时间缩短。尽管病媒能力没有显示出明确的长期趋势,但其值普遍低于10,表明全国范围内存在低至中度疟疾传播潜力。尽管区域和当地的小气候变化可能继续支持局部疟疾传播风险,但温度的长期上升和湿度的下降可能会减少加纳蚊子的寿命和疟疾传播的潜力。这些发现强调了在国家消除疟疾规划中,气候知情和针对特定区域的战略对于改进有针对性的干预措施和优化病媒控制工作的重要性。
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引用次数: 0
Study on the resurgence of pertussis based on a stage-structured dynamic model 基于阶段结构动态模型的百日咳复燃研究
IF 2.5 3区 医学 Q1 Medicine Pub Date : 2025-12-12 DOI: 10.1016/j.idm.2025.12.007
Yifei Qiao, Jijun Zhao
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.
虽然百日咳疫苗接种有效地降低了全球发病率和死亡率,但近年来在许多国家都观察到百日咳死灰复燃。本研究旨在梳理中国百日咳高发省份百日咳死灰复燃前后动态传播特征的变化,探讨其影响因素及可能的控制措施。选取山东、四川和浙江三省作为研究对象。基于2004 - 2022年的月度病例数据,构建了包含不同免疫减弱程度的动态模型,以确定最能反映这些省份疫情动态的模型。2004 - 2013年山东省、2004 - 2017年浙江省和四川省的易感-接种-感染-恢复模型(SVIR)最能反映三省的流行动态,估计疫苗保护期约为7-11年。重新流行后,易感-接种-感染-恢复-易感模型(SVIRS)更准确地代表了三省的流行动态,疫苗保护持续7-13年,自然免疫持续16-24年,表明不存在终身免疫。此外,我们发现百日咳的平均传播率、病例报告和疫苗有效性都有所增加。中国三省观察到的百日咳死灰复燃受多种因素影响,包括传播率上升、报告率提高和疫苗诱导免疫力下降。为减轻死灰复燃,应考虑针对青少年和成人的加强疫苗接种战略。
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引用次数: 0
Impact of the first booster vaccine against SARS-CoV-2 in Chile 智利首支SARS-CoV-2强化疫苗的效果
IF 2.5 3区 医学 Q1 Medicine Pub Date : 2025-12-12 DOI: 10.1016/j.idm.2025.12.009
Emilio Molina , Diego Olguín , Antoine Brault , Paula Uribe , Pedro Gajardo , Mauricio Canals , Héctor Ramírez
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.
本文提出了一种新的方法来评估预防传染病的疫苗接种计划的影响。该方法有两个不同的阶段。初始阶段包括描述疾病在人群中传播的区隔模型。该模型由代表疫苗接种状况的基本部分组成,在初始阶段用于估计疫苗接种计划避免的病例和死亡人数。在方法论的第二部分中,由分区模型生成的案例动态作为数据驱动模型的输入。第二个模型本质上是统计的,并提供了关于住院和ICU住院人数的额外信息。该方法应用于评估智利SARS-CoV-2强化疫苗的影响,结果估计在2021年8月16日至12月31日期间,84%(95%置信区间(CI): 74% - 92%)的病例、73%(95%置信区间(CI): 57%, 80%)的住院病例、77% (95% CI: 61%, 84%)的重症监护病房住院病例和78% (95% CI: 62% - 85%)的死亡病例被避免。
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引用次数: 0
Spatio-temporal forecasting of dengue in the Americas through hybrid mechanistic and data-driven models: Systematic review and meta-analysis 通过混合机制和数据驱动模型预测美洲登革热的时空:系统回顾和荟萃分析
IF 2.5 3区 医学 Q1 Medicine Pub Date : 2025-12-11 DOI: 10.1016/j.idm.2025.12.005
Jenniffer Alejandra Castellanos Garzón, Luis Fernando Plaza Gálvez, Kelly Fernanda Plaza Bastidas, Julián Eduardo Betancur Agudelo, Andrés Rey Piedrahita
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.
本系统综述和荟萃分析(PROSPERO: CRD420251130769)综合了2016年至2025年间在美洲开展的30项登革热模型研究,评估了机制和数据驱动方法的整合。我们通过应用标准化预测保真度指数(SPFI)量化了各种建模框架的可靠性。我们的综合研究显示,在所有方法中,温度与登革热风险之间存在显著正相关(合并相对风险(RR) = 1.26[95%置信区间(CI): 1.18-1.35])。然而,一个关键的性能二分法仍然存在:虽然机制模型表现出依赖于校准质量的高方差,但时间回归分析证实,自2023年以来,机器学习架构已经实现了统计上显著的向高预测保真度(中位数SPFI: 0.89)的收敛。尽管它们很精确,但数据驱动的模型仍然与干预模拟所需的因果逻辑脱节。为了解决这种方法上的碎片化问题,我们开发了一种功能性的“玻璃盒”混合架构,它由三种基于证据的途径定义:通过机器学习对机械核心进行动态参数化;生物约束对预测算法的强制执行;以及数据的持续同化。我们的结论是,从描述性科学过渡到这种可操作的、数据同化的混合框架,对于在美洲对不断升级的登革热危机作出精确的、特定地点的公共卫生反应至关重要。
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引用次数: 0
Dengue fever prediction based on meteorological features and deep learning models 基于气象特征和深度学习模型的登革热预测
IF 2.5 3区 医学 Q1 Medicine Pub Date : 2025-12-11 DOI: 10.1016/j.idm.2025.12.010
Yunyun Cheng , Rong Cheng , Ting Xu , Xiuhui Tan , Yanping Bai
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.
登革热流行是世界卫生组织(世卫组织)的卫生重点之一,准确预测其流行病学趋势至关重要。温度、湿度和降水等多源地理数据影响登革热的发生和流行。因此,本文提出了一种考虑多维气象特征影响的有效混合模型,以提高登革热的预测性能。首先,为了解决数据稀缺的问题,采用时间序列生成对抗网络(TimeGAN)算法扩展登革热数据集。其次,利用辛几何模态分解(SGMD)对气象序列进行分解,利用样本熵(SE)将子序列重构为高、中、低频信号;随后,将滑动窗口技术应用于信号带,以准确捕获影响登革热的关键时间段。最后,通过双向时间卷积网络(BiTCN)对登革热序列相关的双向时间特征进行提取和融合,并将融合后的特征输入到引入注意力模块的双向长短期记忆网络(BiLSTM)中进行预测,得到最终的预测结果。以广东省登革热病例数据为例,实验结果表明,该方法能较准确地预测登革热流行趋势,平均绝对误差(MAE)为192.98759,平均绝对百分比误差(MAPE)为2.492。
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引用次数: 0
Modeling and control of Chikungunya with chronic infection 基孔肯雅热慢性感染的建模与控制
IF 2.5 3区 医学 Q1 Medicine Pub Date : 2025-12-10 DOI: 10.1016/j.idm.2025.12.002
Yan Wang , Huan Ma , Qian Yan , Zhichun Yang
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 R0 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 (β, ρ1, ρ2), the total mosquito population (Tv), and the initial number of infected mosquitoes (Iv(0)). Sensitivity analysis identifies that the primary positive and negative parameters on disease transmission are mosquito biting rate (β) and mosquito mortality rate (ϵv), 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 R0 < 1 and that of the endemic equilibrium when R0 > 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.
基孔肯雅热被全球公认为热带和亚热带地区的一个主要公共卫生问题,在中国广东省等存在适宜蚊子媒介栖息地的地区也构成潜在的流行风险。本研究以佛山市顺德区一起基孔肯雅热暴发为例,建立了一个包含慢性感染阶段的动力学模型。我们推导了R0,并对所有均衡进行了彻底的稳定性分析。利用顺德区每日报告病例数据,模型拟合3个关键传播参数(β、ρ1、ρ2)、总蚊群(Tv)和初始感染蚊数(Iv(0))的产出量估计值。敏感性分析结果显示,蚊虫叮咬率(β)和蚊虫死亡率(ϵv)分别为疾病传播的主要阳性和阴性参数。据此,设计了五种干预措施:个人保护、筛查和检测、急性患者治疗、慢性病例管理和蚊虫媒介控制措施。基于这些发现,我们制定了一个控制框架来优化干预策略。数值模拟不仅验证了R0 <; 1时无病平衡点的全局渐近稳定性和R0 >; 1时地方病平衡点的全局渐近稳定性,而且评估了不同控制策略的有效性。在成本效益分析中,强调个人保护的策略A是最经济有效的选择。它不仅有效地阻断了病毒传播,而且最大限度地减轻了慢性病例的负担,从而为公共卫生资源配置提供了一种科学合理、经济可行的方法。
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引用次数: 0
Integrating Kolmogorov-Arnold networks with ordinary differential equations for efficient, interpretable, and robust deep learning: Epidemiology of infectious diseases as a case study 将Kolmogorov-Arnold网络与常微分方程集成以实现高效、可解释和鲁棒的深度学习:传染病流行病学案例研究
IF 2.5 3区 医学 Q1 Medicine Pub Date : 2025-12-10 DOI: 10.1016/j.idm.2025.12.006
Kexin Ma , Xu Lu , Nicola Luigi Bragazzi , Dominik Selzer , Thorsten Lehr , Biao Tang
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.
本研究通过将Kolmogorov-Arnold网络(KAN)与常微分方程(称为KAN-UDE)集成来扩展通用微分方程(UDE)框架,以实现高效和可解释的深度学习。我们的案例研究集中在新发传染病的流行病学上。与基于多层感知器的UDEs相比,训练KAN-UDE模型的拟合性能得到了显著提高,损失迅速而显著地减少。KAN-UDE模型能够在部分时间序列训练数据下精确地重建非线性函数,并保持对数据稀疏性的鲁棒性。这种方法可以实现可解释的学习过程,因为KAN-UDE模型被重构为全机制模型(RMMs)。当考虑到真实世界数据的随机性时,KAN- ude模型表现出较低的稳健性和准确性,而RMMs在更长的时间窗口内稳健而准确地预测了流行病趋势,因为KAN在数据随机性的情况下精确地重建了机制函数。
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引用次数: 0
Urbanization influences hemorrhagic fever with renal syndrome transmission: 34-year evidence from China's national surveillance 城市化影响肾综合征出血热传播:来自中国34年国家监测的证据
IF 2.5 3区 医学 Q1 Medicine Pub Date : 2025-12-09 DOI: 10.1016/j.idm.2025.12.004
Junyu He , Yanding Wang , Xiaopeng Xu , George Christakos , Danjie Zhang , Yuanyong Xu , Qiulan Chen , Wenyi Zhang

Background

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.

Methods

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.

Findings

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.

Interpretation

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.
中国大陆占全球肾综合征出血热(HFRS)病例的90%以上,但在全国范围内,气候、城市化和传播动态之间的定量关系仍然知之甚少。方法采用贝叶斯非线性混合效应模型,分析中国31个省级行政区1985-2018年34年HFRS监测数据,探讨其与气候变量、社会经济指标和土地利用类型的关系。进行优势分析以量化每个预测因子的相对重要性。此外,为了比较和验证目的,实现了线性混合效应和广义加性模型。结果HFRS年平均发病率由1986年的10.99例/105下降到2010年后的0.98例/105以下,年均发病率最高的4个省为黑龙江、山东、陕西和浙江。贝叶斯模型表现出良好的预测性能(R2分别为0.8722和0.8592,分别为1985-2004年和2005-2018年的前期和后期)。在2005年之前,不透水表面、人口和湿地成为前三大主要传播预测因子。然而,2005年之后,关键的预测因子发生了变化,湿地、帕尔默干旱严重指数(PDSI)和不透水地表的相对重要性最高。城市化的量化是通过不透水地表扩张和湿地变化提供的,这是中国HFRS传播的主要预测因子,可能通过啮齿动物栖息地的改变和人类与野生动物接触模式的改变来实现。新兴的湿地影响表明,环境政策正在重塑疾病动态。我们的研究结果支持整个西太平洋地区以城市化为目标的预防战略,并强调将土地利用监测纳入区域传染病监测系统。
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引用次数: 0
Age-structured next generation matrix and R0 calculation for Mycobacterium avium subsp. paratuberculosis (MAP) 鸟分枝杆菌亚种年龄结构下一代基质及R0计算。副结核(地图)
IF 2.5 3区 医学 Q1 Medicine Pub Date : 2025-12-08 DOI: 10.1016/j.idm.2025.12.003
Yuqi Gao , Nienke Hartemink , Piter Bijma , Mart C.M. de Jong
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, 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.
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 (>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.
副结核(约翰氏病)是由鸟分枝杆菌亚种副结核(MAP)引起的,以其年龄特异性易感性、终生感染、多种脱落模式、诊断敏感性低、潜伏期长和环境持久性长而闻名,所有这些都使根除具有挑战性。在这里,我们提出了一个年龄结构的下一代矩阵(NGM),用于一个封闭的、全年产犊的、自由的畜群,以量化MAP的传播,并评估可以通过遗传选择和三种畜群管理干预措施来降低基本繁殖数(R0)的五种动物性状的潜力。NGM基于一个隔间模型,捕捉了大坝到小牛和环境的传播。宿主以离散的一周年龄组表示,每个年龄组都有其年龄依赖性易感性和位置(产犊圈针对新生儿,小牛圈针对小牛,成年圈针对母牛/成年),以反映宿主人口统计和年龄特异性暴露。我们的基线NGM产率R0 = 2.90,其中91.1%的新感染发生在小牛(1-52周),5.01%发生在新生儿(0-1周),3.91%发生在小母牛/成年(>;52周)。单变量分析发现,两个性状(当成功选择时)可以使R0低于1:将初始易感性水平从1降低到0.303(或更低),或将年龄特异性易感性降低率从0.0629提高到0.214(或更高)。我们进一步推导了ΔR0的线性近似,它可以在不重建完整矩阵的情况下进行快速场景测试。该研究为量化农场层面的MAP传播动态以及确定关键特征和针对性干预措施提供了方法。
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引用次数: 0
Absolute humidity drives seasonal influenza A transmission in Hong Kong through social contact modulation: Evidence from compartmental modeling 绝对湿度通过社会接触调节驱动香港的季节性甲型流感传播:来自区隔模型的证据
IF 2.5 3区 医学 Q1 Medicine Pub Date : 2025-12-07 DOI: 10.1016/j.idm.2025.12.001
Guanlin Ou , Wenjun Ma , Yanying Mo , Jianxiong Hu , Tian Tang

Background

Prior studies propose a U-shaped humidity-influenza relationship, yet the interplay between humidity-driven contact behaviors and transmission dynamics remains unclear.

Objective

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.

Methods

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.

Results

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.

Conclusions

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.
先前的研究提出了湿度与流感的u型关系,但湿度驱动的接触行为与传播动力学之间的相互作用尚不清楚。目的研究绝对湿度(AH)如何调节社会接触(SC)驱动甲型流感传播,量化绝对湿度介导的接触行为与病毒生存能力的相对贡献,并确定控制疫情的最佳接触减少策略。方法将who FluNet数据(2016-2024年)、香港接触调查和气象记录整合到遗传算法优化的SEIR模型中。该框架动态模拟了ah依赖的双重传播机制(行为和环境),通过发生率最小化来评估最佳的减少接触策略,并采用LHS/PRCC敏感性分析来确定关键驱动因素。结果AH的季节性变化引起了社会接触的周期性波动,从而调节了甲型流感的传播动态。ah驱动的SC模式对甲型流感的潜在影响已逐渐减弱。ga优化的SEIR动态显示了控制策略的季节性异质性需求。暴发暴发的最高风险出现在冬季。接触干预在冬季(干预强度达到62%)和夏季(干预强度在16% - 23%之间)达到高峰。敏感性分析显示绝对湿度调节感染效果和恢复率是主要驱动因素。结论绝对湿度与流感传播之间的关系可归因于湿度驱动的社会接触转变。这就需要根据季节调整干预措施:冬季战略应优先考虑严格的接触限制,而温暖的季节则允许放松措施。未来的模型应整合多气候带验证和动态行为感知,以改进疫情预测。
{"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":"2025-12-07","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}
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Infectious Disease Modelling
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