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相关论文: SeCoNet: A Heterosexual Contact Network Growth Mod…

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Human papillomavirus (HPV) infection is the most common sexually transmitted infection in the world. Persistent oncogenic Human papillomavirus infection has been a leading threat to global health and can lead to serious complications such…

种群与进化 · 定量生物学 2024-11-13 Weiyi Wang , Shailendra Sawleshwarkar , Mahendra Piraveenan

This study examines how contact network topology influences the effectiveness of vaccination programs in the context of human papillomavirus (HPV) transmission. Using the SeCoNet sexual contact network growth model, we evaluate age based,…

社会与信息网络 · 计算机科学 2025-11-17 Weiyi Wang , Mahendra Piraveenan

In this article, we propose a network spread model for HIV epidemics, wherein each individual is represented by a node of the transmission network and the edges are the connections between individuals along which the infection may spread.…

生物物理 · 物理学 2007-08-29 Wen-Jie Bai , Tao Zhou , Bing-Hong Wang

A simple model to encapsulate the essential growth properties of \emph{the web of human sexual contacts} is presented. In the model only heterosexual connection is considered and represented by a random growing bipartite graph where both…

统计力学 · 物理学 2009-11-07 Guler Ergun

We develop a Bayesian statistical model and estimation methodology based on Forward Projection Adaptive Markov chain Monte Carlo in order to perform the calibration of a high-dimensional non-linear system of Ordinary Differential Equations…

应用统计 · 统计学 2011-08-17 Igor A. Korostil , Gareth W. Peters , Julien Cornebise , David G. Regan

The spread of sexually transmitted diseases (e.g. Chlamydia, Syphilis, Gonorrhea, HIV) across populations is a major concern for scientists and health agencies. In this context, both data collection on sexual contact networks and the…

物理与社会 · 物理学 2009-11-13 J. Gomez-Gardenes , V. Latora , Y. Moreno , E. V. Profumo

Capturing the structured mixing within a population is key to the reliable projection of infectious disease dynamics and hence informed control. Both heterogeneity in the number of contacts and age-structured mixing have been repeatedly…

社会与信息网络 · 计算机科学 2026-03-17 Luke Murray Kearney , Emma L Davis , Matt J Keeling

Real epidemic spreading networks often composed of several kinds of networks interconnected with each other, and the interrelated networks have the different topologies and epidemic dynamics. Moreover, most human diseases are derived from…

物理与社会 · 物理学 2017-06-21 Zhongpu Xu , Xinchu Fu

The study of transmission and progression of human papillomavirus (HPV) is crucial for understanding the incidence of cervical cancers, and has been identified as a priority worldwide. The complexity of the disease necessitates a detailed…

Cervical intraepithelial neoplasia (CIN) is the development of abnormal cells on the surface of the cervix, caused by a human papillomavirus (HPV) infection. Although in most of the cases it is resolved by the immune system, a small…

定量方法 · 定量生物学 2024-03-21 Vasiliki Bitsouni , Nikolaos Gialelis , Ioannis G. Stratis , Vasilis Tsilidis

The integration of empirical data in computational frameworks to model the spread of infectious diseases poses challenges that are becoming pressing with the increasing availability of high-resolution information on human mobility and…

种群与进化 · 定量生物学 2013-04-24 Anna Machens , Francesco Gesualdo , Caterina Rizzo , Alberto E Tozzi , Alain Barrat , Ciro Cattuto

Epidemiologic studies of infectious diseases often rely on models of contact networks to capture the complex interactions that govern disease spread, and ongoing projects aim to vastly increase the scale at which such data can be collected.…

密码学与安全 · 计算机科学 2026-04-10 Shlomi Hod , Debanuj Nayak , Jason R. Gantenberg , Iden Kalemaj , Thomas A. Trikalinos , Adam Smith

It is a fundamental question in epidemiology to estimate, model and predict the growth rate of a pandemic. Analogously, analysing the diffusion of innovation, (fake) news, memes, and rumours is of key importance in the social sciences. The…

Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models.…

统计方法学 · 统计学 2024-01-11 Octavious Smiley , Till Hoffmann , Jukka-Pekka Onnela

The spread of certain diseases can be promoted, in some cases substantially, by prior infection with another disease. One example is that of HIV, whose immunosuppressant effects significantly increase the chances of infection with other…

物理与社会 · 物理学 2014-08-04 M. E. J. Newman , C. R. Ferrario

The contact structure between hosts has a critical influence on disease spread. However, most networkbased models used in epidemiology tend to ignore heterogeneity in the weighting of contacts. This assumption is known to be at odds with…

种群与进化 · 定量生物学 2012-09-03 Christel Kamp , Mathieu Moslonka-Lefebvre , Samuel Alizon

We describe how to generate a heterosexual network with a prescribed joint-degree distribution that is embedded in a prescribed large-scale social contact network. The structure of a sexual network plays an important role in how sexually…

定量方法 · 定量生物学 2021-04-19 Asma Azizi , Zhuolin Qu , Bryan Lewis , James Mac Hyman

The most frequent infectious diseases in humans - and those with the highest potential for rapid pandemic spread - are usually transmitted via droplets during close proximity interactions (CPIs). Despite the importance of this transmission…

Disease spread in most biological populations requires the proximity of agents. In populations where the individuals have spatial mobility, the contact graph is generated by the "collision dynamics" of the agents, and thus the evolution of…

物理与社会 · 物理学 2007-06-07 Z. Toroczkai , H. Guclu

Human contact networks are constituted by a multitude of individuals and pairwise contacts among them. However, the dynamic nature, which generates the evolution of human contact networks, of contact patterns is not known yet. Here, we…

物理与社会 · 物理学 2019-05-22 Cong Li , Jing Li , Xiang Li
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