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相关论文: The Evolution of the Cuban HIV/AIDS Network

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The Cuban contact-tracing detection system set up in 1986 allowed the reconstruction and analysis of the sexual network underlying the epidemic (5,389 vertices and 4,073 edges, giant component of 2,386 nodes and 3,168 edges), shedding light…

应用统计 · 统计学 2015-05-25 Stéphan Clémençon , Hector De Arazoza , Fabrice Rossi , Viet Chi Tran

It is the main purpose of this paper to introduce a graph-valued stochastic process in order to model the spread of a communicable infectious disease. The major novelty of the SIR model we promote lies in the fact that the social network on…

应用统计 · 统计学 2014-02-06 Charanpal Dhanjal , Stéphan Clémençon

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

An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people. With this data, we can extract the knowledge potentially useful for many applications including the…

应用统计 · 统计学 2015-03-24 Sanja Brdar , Katarina Gavric , Dubravko Culibrk , Vladimir Crnojevic

A stochastic epidemic model accounting for the effect of contact-tracing on the spread of an infectious disease is studied. Precisely, individuals identified as infected may contribute to detecting other infectious individuals by providing…

概率论 · 数学 2009-03-28 Stéphan Clémençon , Viet Chi Tran , Hector De Arazoza

Molecular HIV Surveillance (MHS) has been described as key to enabling rapid responses to HIV outbreaks. It operates by linking individuals with genetically similar viral sequences, which forms a network. A major limitation of MHS is that…

应用统计 · 统计学 2024-07-24 Ravi Goyal , Kevin Nguyen , Victor De Gruttola , Susan J Little , Colby Cohen , Natasha K Martin

Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian Computation, an alternative to data imputation methods such as Markov Chain Monte Carlo integration, is proposed…

应用统计 · 统计学 2010-08-31 Michael G. B. Blum , Viet Chi Tran

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

Random mixing in host populations has been a convenient simplifying assumption in the study of epidemics, but neglects important differences in contact rates within and between population groups. For HIV/AIDS, the assumption of random…

定量方法 · 定量生物学 2016-02-24 Brian G. Williams , Christopher Dye

This paper presents a mathematical model to investigate co-infection with HIV/AIDS and zika virus (ZIKV) in Colombia and Brazil, where the first cases were reported in 2015-2016. The model considers the sexual transmission dynamics of both…

种群与进化 · 定量生物学 2024-04-17 Jhoana P. Romero-Leiton , Idriss Sekkak , Julien Arino , Bouchra Nasri

Understanding historical trends in the epidemic of HIV is important for assessing current and projecting future trends in prevalence, incidence and mortality and for evaluating the impact and cost-effectiveness of control measures. In…

定量方法 · 定量生物学 2014-12-10 Brian G. Williams

Background. Several studies have used phylogenetics to investigate Human Immunodeficiency Virus (HIV) transmission among Men who have Sex with Men (MSMs) in Montreal, Quebec, Canada, revealing many transmission clusters. The Quebec HIV…

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

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

Men who have sex with men (MSM) remain disproportionately affected by HIV, yet optimizing Pre-exposure Prophylaxis (PrEP) distribution remains a public health challenge. Current guidelines and most modelling studies do not incorporate…

物理与社会 · 物理学 2026-03-17 João Brázia , István Z. Kiss , Alexandre P. Francisco , Andreia Sofia Teixeira

Key populations at high risk of HIV infection are critical for understanding and monitoring HIV epidemics, but global estimation is hampered by sparse, uneven data. We analyze data from 199 countries for female sex workers (FSW), men who…

应用统计 · 统计学 2025-09-16 Jiahao Zhang , Keith Sabin , Le Bao

We present a comprehensive statistical methodological framework for estimating contextual exposure to HIV that includes local (grid-cell level) estimation of HIV prevalence and human activity space estimation based on GPS data. The…

Black men who have sex with men (MSM) in the U.S. are more likely to be HIV-positive than White MSM. Intentional and unintentional segregation of Black from non-Black MSM in sex partner meeting places may perpetuate this disparity, a fact…

应用统计 · 统计学 2021-05-13 Jonathan Larson , Jukka-Pekka Onnela

In this paper, we have identified and analyzed the emergence, structure and dynamics of the paradigmatic research fronts that established the fundamentals of the biomedical knowledge on HIV/AIDS. A search of papers with the identifiers…

One of the cornerstones in combating the HIV pandemic is being able to assess the current state and evolution of local HIV epidemics. This remains a complex problem, as many HIV infected individuals remain unaware of their infection status,…

种群与进化 · 定量生物学 2019-10-24 Pieter Libin , Nassim Versbraegen , Ana B. Abecasis , Perpetua Gomes , Tom Lenaerts , Ann Nowé
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