A stochastic graph process for epidemic modelling

A stochastic graph process with a Markov property is introduced to model the flow of an infectious disease over a known contact network. The model provides a probability distribution over unobserved infectious pathways. The basic reproductive number in compartmental models is generalized to a dynamic reproductive number based on the sequence of outdegrees in the graph process. The cumulative resistance and threat associated with each individual is also measured based on the cumulative indegree and outdegree of the graph process. The model is applied to the outbreak data from the 2001 foot‐and‐mouth (FMD) outbreak in the United Kingdom. The Canadian Journal of Statistics 40: 55–67; 2012 © 2012 Statistical Society of Canada     

艾滋病疫情发展及防控中的伦理问题

在全球范围内,艾滋病已经发展成为一个严重的公共卫生问题和社会问题;我国目前形势严峻,而且还存在着一些高发的隐患因素;艾滋病给个人及其家庭、社区和社会造成一系列的破坏作用;在艾滋病的防控工作中,存在着诸多伦理问题。对这些伦理问题进行梳理和分别研究,是制订防治艾滋病的公共政策、法律法规、管理办法的重要基础,也是公众正确对待艾滋病问题和艾滋病患者、感染者的认识基础。     

Inference in disease transmission experiments by using stochastic epidemic models

Summary.  The paper extends the susceptible–exposed–infective–removed model to handle heterogeneity introduced by spatially arranged populations, biologically plausible distributional assumptions and incorporation of observations from additional diagnostic tests. These extensions are motivated by a desire to analyse disease transmission experiments in a more detailed fashion than before. Such experiments are performed by veterinarians to gain knowledge about the dynamics of an infectious disease. By fitting our spatial susceptible–exposed–infective–removed with diagnostic testing model to data for a specific disease and production environment a valuable decision support tool is obtained, e.g. when evaluating on-farm control measures. Partial observability of the epidemic process is an inherent problem when trying to estimate model parameters from experimental data. We therefore extend existing work on Markov chain Monte Carlo estimation in partially observable epidemics to the multitype epidemic set-up of our model. Throughout the paper, data from a Belgian classical swine fever virus transmission experiment are used as a motivating example.     

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.     

论疫情防控中个人信息保护——以CoviD-19突发公共卫生事件应急为视角

CoviD-19疫情引发的特别重大突发公共卫生事件中,个人信息在有效预防和及时防控疫情中发挥了非常重大的作用,但是也出现了一些个人信息泄露和侵权的问题。因此,需要平衡个人信息收集处理与个人信息保护。第一,在疫情防控中个人信息收集,应当明确与个人数据的不同,不能简单用保护网络数据规则保护个人信息;与《民法总则》私法规范基于知情合意收集个人信息不同,无需个人同意,且个人必须如实提供个人信息。同时,个人信息收集主体对涉及个人隐私等敏感信息得保密或去敏化处理。第二,在疫情防控中个人信息处理,遵从统一领导原则和奉行属地原则,以维护公共健康利益为目的对个人信息进行处理利用,分离敏感信息,由专门职能部门对个人信息使用及时公布;严格遵守最小比例原则,将使用范围控制在使用目的所必需的范围内。第三,《传染病防治法》《突发事件应对法》《突发公共卫生事件应急条例》等法律法规对疫情防控中个人信息收集处理规定的内容过于抽象、操作性差,《网络安全法》不能代替个人信息保护的立法,需要制定统一的《个人信息保护法》,根据公开透明原则、比例原则、个人信息安全管理原则、以私权为中心兼顾公共利益原则,建立完善个人信息保护法律制度。     

Assessing Interventions to Reduce the Risk of Campylobacter Prevalence in Broilers

As part of a comprehensive risk assessment on the Campylobacter prevalence in the chicken production chain (from young born chicken till chicken fillet) in the Netherlands, we formulated a quantitative model on the transmission dynamics of Campylobacter at Dutch broiler farms. This model is used to quantify the risk of Campylobacter prevalence in broilers at the time that flocks leave the farm for processing. To this end, we assumed that the Campylobacter prevalence is primarily determined by two parameters, that is, the within- and between-flock transmission. The within-flock transmission was assessed fitting experimental data to a logistic growth model and the between-flock transmission was assessed fitting field data to a generalized linear model (GLM), which included three possible infection routes: (1) via an infected flock in the previous cycle, (2) via other infected flocks present on the farm, and (3) from other sources. This model was applied to assess the efficacy of three control scenarios; (1) a ban on other livestock on broiler farms, (2) a ban on thinning, and (3) a reduction of the between-flock transmission. In contrast to the other scenarios, the third one was shown to be most effective. Theoretically, this is accomplished by improved biosecurity. However, the impact of improved biosecurity cannot be specified into specific control measures, and therefore it is not clear what investments are needed. Finally, we also assessed the efficacy of scheduled treatment, that is, fresh meat production solely from test-negative flocks. We found that the reliability of negative test results, which is crucial, strongly depends on the length of time between testing and slaughter. The sensitivity and specificity of the test appeared to be of minor importance.     

Estimation in Epidemics with Incomplete Observations

The construction of estimating equations by martingale methods is generalized to yield estimators with explicit expressions for the parameters of the birth-and-death and the general epidemic processes when only partial observations are available. (For the birth-and-death process the death process is observed but the number of births is observed only at the end and for the general epidemic process only the removal process is observed.) For large populations, the use of the martingale central limit theorem yields asymptotic confidence regions for the parameters. Explicit expressions are derived for estimators of the variances of the large sample distributions. The range of validity and usefulness of the new estimators is determined by simulation.     

Exact filtering for partially observed continuous time models

Summary.  The forward–backward algorithm is an exact filtering algorithm which can efficiently calculate likelihoods, and which can be used to simulate from posterior distributions. Using a simple result which relates gamma random variables with different rates, we show how the forward–backward algorithm can be used to calculate the distribution of a sum of gamma random variables, and to simulate from their joint distribution given their sum. One application is to calculating the density of the time of a specific event in a Markov process, as this time is the sum of exponentially distributed interevent times. This enables us to apply the forward–backward algorithm to a range of new problems. We demonstrate our method on three problems: calculating likelihoods and simulating allele frequencies under a non-neutral population genetic model, analysing a stochastic epidemic model and simulating speciation times in phylogenetics.     

仪式与象征——年都乎岁末傩疫活动的人类学研究

本文在人类学田野工作的基础上 ,运用人类学的知识和社会记忆理论 ,对年都乎虎傩活动的仪式结构和象征意义作了讨论。     

考虑双重传播路径的关联信用风险传染机理研究

关联信用风险及其传染是现代信用风险管理的热点和难点问题。本文基于复杂网络的平均场理论,运用传染病模型刻画关联信用个体之间关联信用风险的传染机制;并考虑潜伏期和感染期均具传染性的双重传播路径,分析了网络中关联信用风险传染的稳定状态;进一步,在一般关联信用个体网络环境中,探讨了关联信用风险传染的影响因素对稳定状态的影响。研究表明：关联信用风险传染的临界值和网络结构有关,与潜伏期和感染期呈反向变化,与潜伏个体和感染个体的相互转化率呈同向变化,与潜伏期传染率和感染期传染率呈同向变化;稳定状态时潜伏个体密度与潜伏期和传染率呈同向变化,与感染期呈反向变化;稳定状态时感染个体密度与感染期和传染率呈同向变化,与潜伏期呈反向变化。本文的研究丰富了信用风险的研究范畴,为企业和银行等信用个体管控信用风险提供了新的视角。