Modeling and control of highly pathogenic avian influenza in poultry using network disease dynamics [0.03%]
利用网络疾病动力学模拟和控制家禽中高致病性禽流感
Hamed Karami,Sifur Safuka Chowdhury,Alexandra Smirnova
Hamed Karami
In light of the ongoing 2022-2025 HPAI outbreak in the U.S., which affects millions of commercial and backyard flocks, disrupts egg and meat production, and causes significant economic losses, it is important to develop biological models an...
When hosts gather: how extreme seasonal aggregation affects epidemiological outcomes [0.03%]
当宿主聚集时：极度季节性聚集如何影响流行病学结果
Daniel N R Longmuir,Simon Johnstone-Robertson,Andrew J Hoskins et al.
Daniel N R Longmuir et al.
Wildlife aggregate for many reasons (e.g. reproduction, feeding) and at times these aggregations can be extreme, with host densities increasing several orders of magnitude. While the impact of seasonality on infectious disease dynamics is w...
Predicting the spatiotemporal evolution of HIV/AIDS in Africa: A retrospective analysis of epidemiological trends [0.03%]
非洲艾滋病/AIDS时空演变预测：流行病学回顾分析
Francesco Branda,Olalekan John Okesanya,Mohamed Mustaf Ahmed et al.
Francesco Branda et al.
Background: Africa bears the highest global burden of HIV, with marked regional inequalities in prevalence, incidence and clinical outcomes. Mapping the spatial and temporal evolution of the epidemic is essential to guide...
Mina Khoshbazm,Kelsey Spence,Marzieh Soltani et al.
Mina Khoshbazm et al.
Avian influenza virus (AIV) continues to pose serious risks to animal and public health. Understanding its spread requires integrating ecological, agricultural, and human information. Quantitative models provide a practical way to represent...
Memory mechanisms for behavioural change in Bayesian individual-level spatial epidemic models [0.03%]
基于贝叶斯个体层面空间流行病模型的行为改变记忆机制
Yicheng Mao,Rob Deardon,Lorna E Deeth
Yicheng Mao
Accurate modelling of infectious disease transmission often requires capturing how individuals adjust their behaviours in response to evolving epidemic conditions. While recently developed behavioural change epidemic models attempt to ackno...
Modeling two-strain competition with reinfection: Mathematical analyses and epidemiological implications [0.03%]
具有重复感染的两种菌株竞争的数学模型及流行病学含义
Jie Bai,Jin Wang
Jie Bai
We propose a new epidemic model for the interactions between two different strains associated with the same pathogen. Our focus is how the competition between multiple strains and the impact of reinfection shape the transmission, spread, an...
Rapid assessment of local disease control measures against the Marburg virus outbreak in Ethiopia in late 2025 [0.03%]
埃塞俄比亚快速评估地方疾控措施应对2025年末马堡病毒暴发的影响
Qingcui Wu,Zihao Guo,Zihan Yuan et al.
Qingcui Wu et al.
In November 2025, Marburg virus caused the first outbreak of Marburg virus disease (MVD) in Ethiopia. By December 15, a total of 14 laboratory-confirmed cases were reported, including 9 deaths, corresponding to a case fatality ratio of 64.3...
Expanding optimization ensemble model methods for forecasting seasonal influenza in the U.S [0.03%]
基于美国季节性流感预测的优化集合模型方法及其扩展
Benjamin Benteke Longaou,Rhiannon Löster,Pengfei Yue et al.
Benjamin Benteke Longaou et al.
Each year, the seasonal influenza epidemic sees significant variability in its evolution. Accurate forecasts of future influenza cases are important for planning public health responses. The United States Centers for Disease Control and Pre...
Dynamics of infectious disease spread between transportation hubs and surrounding communities [0.03%]
交通枢纽及其周边社区间传染病传播的动力学研究
Rahele Mosleh,Mina Shafadeh,Bushra Majeed et al.
Rahele Mosleh et al.
Urban transit systems, particularly those in major metropolitan areas, are becoming increasingly interconnected, making it essential to better understand passenger mobility and its implications for the spread of infectious diseases. Respira...
Resolving parameter uncertainty in SIR models through population-level serological surveillance: A synthetic study [0.03%]
通过人口水平的血清学监测解决SIR模型中的参数不确定性：一项合成研究
Binod Pant,Matthew E Levine,Anjalika Nande et al.
Binod Pant et al.
Epidemic models face a critical challenge: surveillance systems capture only a fraction of infections (often