Bayesian inference for partially observed stochastic epidemics

The analysis of infectious disease data is usually complicated by the fact that real life epidemics are only partially observed. In particular, data concerning the process of infection are seldom available. Consequently, standard statistical techniques can become too complicated to implement effectively. In this paper Markov chain Monte Carlo methods are used to make inferences about the missing data as well as the unknown parameters of interest in a Bayesian framework. The methods are applied to real life data from disease outbreaks.     

Testing change in the variance with epidemic alternatives

ABSTRACT

In this paper we consider the dyadic increments statistics (of type DI) based on independent not identically distributed or α-mixing random variables. We obtain their limit distributions under the null hypothesis and we present application for testing epidemic change in the variance in each case. Finally, numerical simulations are done to illustrate these results.     

SARS疫情的状态评估和预测建模研究

从定义SARS疫情发展状态的标志参数出发,给出对疫情发展过程进行阶段划分的基本方法。按照这些标志参数在各个阶段的取值,可以分析SARS疫情从爆发到衰减过程中的关键因素,讨论新增病例数量和留院治疗人数进入收敛状态的基本条件,并给出预报有关地区达到WHO解除疫情警报的时间的方法。论文以香港和北京的数据为例,验证了所研究模型的合理性和有效性。同时,对北京解除WHO旅游警告的时间进行推测。     

All the News that’s Fat to Print: The American “Obesity Epidemic” and the Media

In the last twenty years scientific, medical, and public health interest in obesity has skyrocketed. Increasingly the term “epidemic” is being used in the media, medical journals, and public health policy literature to describe the current prevalence of fatness in the U.S. Using social scientific literature on epidemics, social problems, and feminist theories of the body, this paper traces the historical emergence of the “obesity epidemic” through an analysis of 751 articles on obesity published in The New York Times between 1990 and 2001. Through the identification and analysis of three discursive pairings I argue that the “obesity epidemic” is a part of a new breed of what I call “post-modern epidemics,” epidemics in which unevenly medicalized phenomena lacking a clear pathological basis get cast in the language and moral panic of “traditional” epidemics. I show how this moral panic together with the location of the problem within the individual precludes a more macro level approach to health and health care delivery at a time when health care services are being dismantled or severely cut back.

Evaluation of Response Scenarios to Potential Polio Outbreaks Using Mathematical Models

Appropriate response to polio outbreaks represents an important prerequisite for achieving and maintaining global polio eradication. We use an existing dynamic disease transmission model to evaluate the impact of different aspects of immunization campaigns in response to polio outbreaks occurring in previously polio-free areas. This analysis yields several important insights about response strategies. We find that delay in response represents a crucial risk factor for occurrence of large outbreaks and we characterize the tradeoffs associated with delaying the initial response to achieve better population coverage. We also demonstrate that controlling most potential outbreaks will likely require at least three immunization rounds, although the impact of the optimal interval between rounds varies. Finally, long after oral poliovirus vaccine cessation the choice of target age groups during a response represents an important consideration.     

Bayesian inference for nonlinear stochastic SIR epidemic model

ABSTRACT

Inference for epidemic parameters can be challenging, in part due to data that are intrinsically stochastic and tend to be observed by means of discrete-time sampling, which are limited in their completeness. The problem is particularly acute when the likelihood of the data is computationally intractable. Consequently, standard statistical techniques can become too complicated to implement effectively. In this work, we develop a powerful method for Bayesian paradigm for susceptible–infected–removed stochastic epidemic models via data-augmented Markov Chain Monte Carlo. This technique samples all missing values as well as the model parameters, where the missing values and parameters are treated as random variables. These routines are based on the approximation of the discrete-time epidemic by diffusion process. We illustrate our techniques using simulated epidemics and finally we apply them to the real data of Eyam plague.     

Analyses of infectious disease data from household outbreaks by Markov chain Monte Carlo methods

The analysis of infectious disease data presents challenges arising from the dependence in the data and the fact that only part of the transmission process is observable. These difficulties are usually overcome by making simplifying assumptions. The paper explores the use of Markov chain Monte Carlo (MCMC) methods for the analysis of infectious disease data, with the hope that they will permit analyses to be made under more realistic assumptions. Two important kinds of data sets are considered, containing temporal and non-temporal information, from outbreaks of measles and influenza. Stochastic epidemic models are used to describe the processes that generate the data. MCMC methods are then employed to perform inference in a Bayesian context for the model parameters. The MCMC methods used include standard algorithms, such as the Metropolis–Hastings algorithm and the Gibbs sampler, as well as a new method that involves likelihood approximation. It is found that standard algorithms perform well in some situations but can exhibit serious convergence difficulties in others. The inferences that we obtain are in broad agreement with estimates obtained by other methods where they are available. However, we can also provide inferences for parameters which have not been reported in previous analyses.     

哲里木盟地区猪病发生和流行的基本趋势及其对策

哲里木盟地区自1980年到1998年猪病的发生和传播出现如下特征：新的传染病（如猪呼吸—综合症）不断增多,旧的传染病以并发、非典型性为主。细菌性传染病、遗传性疾病及代谢性疾病是增多趋势。因此,猪病的防治是一个系统工程,应统筹规划,重点防治,制定科学的免疫程序,科学地使用抗菌药物,并深入研究和利用中草药及各类生物制剂,为防治猪病提供新的手段。     

Optimal design of experimental epidemics

We consider the optimal design of controlled experimental epidemics or transmission experiments, whose purpose is to inform the practitioner about disease transmission and recovery rates. Our methodology employs Gaussian diffusion approximations, applicable to epidemics that can be modeled as density-dependent Markov processes and involving relatively large numbers of organisms. We focus on finding (i) the optimal times at which to collect data about the state of the system for a small number of discrete observations, (ii) the optimal numbers of susceptible and infective individuals to begin an experiment with, and (iii) the optimal number of replicate epidemics to use. We adopt the popular D-optimality criterion as providing an appropriate objective function for designing our experiments, since this leads to estimates with maximum precision, subject to valid assumptions about parameter values. We demonstrate the broad applicability of our methodology using a diverse array of compartmental epidemic models: a time-homogeneous SIS epidemic, a time-inhomogeneous SI epidemic with exponentially decreasing transmission rates and a partially observed SIR epidemic where the infectious period for an individual has a gamma distribution.     

生殖健康面临的严峻挑战：全球艾滋病形势综述

严重影响人类生殖健康的艾滋病正以前所未有的速度在全世界流行．从发现第一例病人至今不足20年时间里,绝大多数国家都已经报告发现了艾滋病病毒感染者．怎样进一步防治艾滋病已是各国政府、民间组织和科学工作者共同关注的焦点．