Nina H. Fefferman

Bio: Nina H. Fefferman is an academic researcher from University of Tennessee. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 23, co-authored 107 publications receiving 2362 citations. Previous affiliations of Nina H. Fefferman include Princeton University & Center for Discrete Mathematics and Theoretical Computer Science.

Influenza seasonality: underlying causes and modeling theories.

Abstract: Influenza (or “flu”) leads to the hospitalization of more than 200,000 people yearly and results in 36,000 deaths from flu or flu-related complications in the United States ([15][1]), striking both the elderly and infant populations particularly hard ([24][2]). Two members of the

Genetic, individual, and group facilitation of disease resistance in insect societies.

Abstract: In this review, we provide a current reference on disease resistance in insect societies. We start with the genetics of immunity in the context of behavioral and physiological processes and scale up levels of biological organization until we reach populations. A significant component of this review focuses on Apis mellifera and its role as a model system for studies on social immunity. We additionally review the models that have been applied to disease transmission in social insects and elucidate areas for future study in the field of social immunity.

Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission

Abstract: Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10–15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector–pathogen systems.

The untapped potential of virtual game worlds to shed light on real world epidemics

Abstract: Summary Simulation models are of increasing importance within the field of applied epidemiology. However, very little can be done to validate such models or to tailor their use to incorporate important human behaviours. In a recent incident in the virtual world of online gaming, the accidental inclusion of a disease-like phenomenon provided an excellent example of the potential of such systems to alleviate these modelling constraints. We discuss this incident and how appropriate exploitation of these gaming systems could greatly advance the capabilities of applied simulation modelling in infectious disease research.

Linking models of human behaviour and climate alters projected climate change

Abstract: Although not considered in climate models, perceived risk stemming from extreme climate events may induce behavioural changes that alter greenhouse gas emissions. Here, we link the C-ROADS climate model to a social model of behavioural change to examine how interactions between perceived risk and emissions behaviour influence projected climate change. Our coupled climate and social model resulted in a global temperature change ranging from 3.4–6.2 °C by 2100 compared with 4.9 °C for the C-ROADS model alone, and led to behavioural uncertainty that was of a similar magnitude to physical uncertainty (2.8 °C versus 3.5 °C). Model components with the largest influence on temperature were the functional form of response to extreme events, interaction of perceived behavioural control with perceived social norms, and behaviours leading to sustained emissions reductions. Our results suggest that policies emphasizing the appropriate attribution of extreme events to climate change and infrastructural mitigation may reduce climate change the most. Human behaviour is an important driver of emissions. A system-dynamics model that couples a psychological model of behaviour with a model of emissions and climate change shows that behaviour can influence global temperature in the year 2100 by up to 1.5 °C.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.