基于vSEIdRm模型的人口迁移以及离汉交通管控对新冠肺炎疫情发展的影响分析

不同于传统( Susceptible-Exposed-Infected-Removed)SEIR流行病传播动力学模型,本文在近期研究的Varying Coefficient Susceptible-Exposed-Infected-Diagnosed-Removed (vSEIdR)模型基础上加上人口迁徙(Migration) 模块,设计开发了vSEIdRm模型,该模型考虑了跨区域人口迁徙对疫情传播的影响,并允许流行病传播参数随时间变化。本文首先对人口迁移数据进行统计分析,建立其与各省新冠肺炎疫情发展的联系。之后,基于vSEIdRm模型估计了疫情初期各省份来自武汉的输入病例数,并定量刻画了离汉交通管控的效果。研究结果显示,离汉交通管控措施有效地减少了各省份的疫情规模。     

地方性知识与现代专业知识的结合——一个傣族村寨防控新冠肺炎疫情的经验

新型冠状病毒肺炎疫情期间,基于一个西双版纳傣族村寨的田野考察,收集西双版纳各级政府、防疫部门及普通民众应对新型冠状病毒肺炎疫情威胁所采取的策略和措施,从中观察傣族民众对新型冠状病毒肺炎疫情的相关认知、行为和心理反应,揭示在此次抗击疫情的实践中,从疫情危机中孕育焕发的集体生存意识以及地方性知识与现代流行病防控专业知识相结合所发挥的重要作用。同时,从人类学视角对边疆民族地区应对这一重大疫情危机的过程进行考察,从而提供有价值的民族志文本。     

Construction of a Dose–Illness Relationship via Modeling Morbidity and Application to Risk Assessment of Wastewater Reuse

A disease burden (DB) evaluation for environmental pathogens is generally performed using disability‐adjusted life years with the aim of providing a quantitative assessment of the health hazard caused by pathogens. A critical step in the preparation for this evaluation is the estimation of morbidity between exposure and disease occurrence. In this study, the method of a traditional dose–response analysis was first reviewed, and then a combination of the theoretical basis of a “single‐hit” and an “infection‐illness” model was performed by incorporating two critical factors: the “infective coefficient” and “infection duration.” This allowed a dose–morbidity model to be built for direct use in DB calculations. In addition, human experimental data for typical intestinal pathogens were obtained for model validation, and the results indicated that the model was well fitted and could be further used for morbidity estimation. On this basis, a real case of a water reuse project was selected for model application, and the morbidity as well as the DB caused by intestinal pathogens during water reuse was evaluated. The results show that the DB attributed to Enteroviruses was significant, while that for enteric bacteria was negligible. Therefore, water treatment technology should be further improved to reduce the exposure risk of Enteroviruses . Since road flushing was identified as the major exposure route, human contact with reclaimed water through this pathway should be limited. The methodology proposed for model construction not only makes up for missing data of morbidity during risk evaluation, but is also necessary to quantify the maximum possible DB.     

清朝前期(1644～1839年)湖南生物灾害研究

清朝前期(1644～1839年)湖南境内发生的生物灾害主要为虫灾和疫灾。虫灾以危及稻麦的蝗灾为主,疫灾既有危及人体的瘟疫也有危害耕畜的牛瘟,对社会生产生活都造成了较大的影响。     

“死亡之城”与大都市的未来

刘易斯.芒福德认为罗马帝国的扩张把古代罗马城变成了一座名副其实的世界城市,他称之为＂死亡之城＂。这个国际化大都市在当时形成了其他城市所没有的独特吸引力,成为后代许多大都市所培养的都市情结的始祖。在芒福德看来,大都市的强势地位不仅表现为对外省或外地资源的掠夺或抽吸,还表现在对其辐射自身的影响。而随之而来的大都市病是不可避免的都市文化副产品,它的寄生性是使城市走向死亡的致命病症。如今,社会发展的诉求与生存环境的限制已不只是中国面临的巨大挑战,也是整个现代文明病症的一种症状。中国在进行了30年的大规模城市化建设之后,是时候对正在出现的＂特大城市象皮病＂,作出诊断和治疗了。     

Effect of resistance training on single muscle fiber contractile function in older men

Reproduced with permission     

A new approach to investigating spatial variations of disease

Summary.  For rare diseases the observed disease count may exhibit extra Poisson variability, particularly in areas with low or sparse populations. Hence the variance of the estimates of disease risk, the standardized mortality ratios, may be highly unstable. This overdispersion must be taken into account otherwise subsequent maps based on standardized mortality ratios will be misleading and, rather than displaying the true spatial pattern of disease risk, the most extreme values will be highlighted. Neighbouring areas tend to exhibit spatial correlation as they may share more similarities than non-neighbouring areas. The need to address overdispersion and spatial correlation has led to the proposal of Bayesian approaches for smoothing estimates of disease risk. We propose a new model for investigating the spatial variation of disease risks in conjunction with an alternative specification for estimates of disease risk in geographical areas—the multivariate Poisson–gamma model. The main advantages of this new model lie in its simplicity and ability to account naturally for overdispersion and spatial auto-correlation. Exact expressions for important quantities such as expectations, variances and covariances can be easily derived.     

Modeling of rates over a hierarchical health administrative structure

Identifying the distribution of the incidence rate of a disease over a region is a prediction problem where area‐specific random effects are to be estimated. The authors consider the inclusion of such effects at different levels of a hierarchical health administrative structure. They develop inference procedures for this type of multi‐level model and show that the predicted rates are approximately weighted sums of the crude rates obtained by pooling data on each level of the hierarchy. Their techniques are illustrated using infant mortality data from British Columbia.     

Stereotypical Disease Inferences From Gay/Lesbian Versus Heterosexual Voices

Voice is a cue used to categorize speakers as members of social groups, including sexual orientation. We investigate the consequences of such voice-based categorization, showing that people infer stereotype-congruent disease likelihood on the basis of vocal information and without explicit information about the speaker’s sexual orientation. Study 1 and Study 2 reveal that participants attribute diseases to gay/lesbian and heterosexual men and women in line with stereotypes. Gay speakers were more likely to be associated with gay and female diseases, and lesbian speakers with male diseases. These findings demonstrate that likelihood to suffer from diseases is erroneously, but stereotypically, inferred from targets’ vocal information.     

Evaluation of Bayesian multiple stage estimation under spatial CAR model variants

In this study, an evaluation of Bayesian hierarchical models is made based on simulation scenarios to compare single-stage and multi-stage Bayesian estimations. Simulated datasets of lung cancer disease counts for men aged 65 and older across 44 wards in the London Health Authority were analysed using a range of spatially structured random effect components. The goals of this study are to determine which of these single-stage models perform best given a certain simulating model, how estimation methods (single- vs. multi-stage) compare in yielding posterior estimates of fixed effects in the presence of spatially structured random effects, and finally which of two spatial prior models – the Leroux or ICAR model, perform best in a multi-stage context under different assumptions concerning spatial correlation. Among the fitted single-stage models without covariates, we found that when there is low amount of variability in the distribution of disease counts, the BYM model is relatively robust to misspecification in terms of DIC, while the Leroux model is the least robust to misspecification. When these models were fit to data generated from models with covariates, we found that when there was one set of covariates – either spatially correlated or non-spatially correlated, changing the values of the fixed coefficients affected the ability of either the Leroux or ICAR model to fit the data well in terms of DIC. When there were multiple sets of spatially correlated covariates in the simulating model, however, we could not distinguish the goodness of fit to the data between these single-stage models. We found that the multi-stage modelling process via the Leroux and ICAR models generally reduced the variance of the posterior estimated fixed effects for data generated from models with covariates and a UH term compared to analogous single-stage models. Finally, we found the multi-stage Leroux model compares favourably to the multi-stage ICAR model in terms of DIC. We conclude that the mutli-stage Leroux model should be seriously considered in applications of Bayesian disease mapping when an investigator desires to fit a model with both fixed effects and spatially structured random effects to Poisson count data.