Characterizing Poliovirus Transmission and Evolution: Insights from Modeling Experiences with Wild and Vaccine‐Related Polioviruses

With national and global health policymakers facing numerous complex decisions related to achieving and maintaining polio eradication, we expanded our previously developed dynamic poliovirus transmission model using information from an expert literature review process and including additional immunity states and the evolution of oral poliovirus vaccine (OPV). The model explicitly considers serotype differences and distinguishes fecal‐oral and oropharyngeal transmission. We evaluated the model by simulating diverse historical experiences with polioviruses, including one country that eliminated wild poliovirus using both OPV and inactivated poliovirus vaccine (IPV) (USA), three importation outbreaks of wild poliovirus (Albania, the Netherlands, Tajikistan), one situation in which no circulating vaccine‐derived polioviruses (cVDPVs) emerge despite annual OPV use and cessation (Cuba), three cVDPV outbreaks (Haiti, Madura Island in Indonesia, northern Nigeria), one area of current endemic circulation of all three serotypes (northern Nigeria), and one area with recent endemic circulation and subsequent elimination of multiple serotypes (northern India). We find that when sufficient information about the conditions exists, the model can reproduce the general behavior of poliovirus transmission and outbreaks while maintaining consistency in the generic model inputs. The assumption of spatially homogeneous mixing remains a significant limitation that affects the performance of the differential equation‐based model when significant heterogeneities in immunity and mixing may exist. Further studies on OPV virus evolution and improved understanding of the mechanisms of mixing and transmission may help to better characterize poliovirus transmission in populations. Broad application of the model promises to offer insights in the context of global and national policy and economic models.     

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.     

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.     

音乐声波对人体作用机理探讨——兼谈音乐逍遥气功的实践

本文介绍了作者在音乐与气功相结合方面所作的创造性实践和研究。在特制音乐声波激发下,能很收快到传统气功的功效,使练功者达到强身治病的目的。实践证明音乐逍遥气功对多种慢性病有很好疗效。文章从物理学、生物物理学的角度,以系统论的方法对音乐逍遥气功的机理进行探讨。认为这是自觉利用各种形式、以一定规律动态变化的能量流的一种新的尝试。     

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.     

Money, real output, and deficit effects of coffee booms in Colombia

This paper develops a Vector Error Correction (VEC) model and uses the recently developed technique of ‘generalized’ impulse response analysis to test the empirical relationships in the Colombian economy between coffee revenues and a set of macro variables. We find that coffee price (revenue) shocks have exerted an important influence on money growth, inflation, and real exchange rates, and the direction of these effects are in line with some of the predictions of traditional Dutch Disease type models. The major difference between our results and the results of Dutch Disease type models arise in the effects of coffee booms on real output. We find that in the time horizon of 5 years after the boom, real output has increased in response to the effects of the coffee boom. The finding that coffee booms can result in positive long-run output effects is an important finding since it contradicts the traditional conclusion of Dutch Disease type models which envision an adverse long-run effect on output. We also find that the long-run effect of coffee booms is to reduce both current account and government deficits. These results illustrate strikingly that the term “Dutch Disease” is an unfortunately pejorative term that obscures the fact that coffee booms need not be viewed as a “disease” but as an extraordinary opportunity to strengthen internal and external balances.     

深圳正落入“后中等收入陷阱”吗?——对深圳服务业内部结构调整的思考

本文基于威廉·鲍莫尔提出的"鲍莫尔病"概念,对深圳市是否正落入"后中等收入陷阱"进行了验证。在此基础上,结合伦敦、巴黎和旧金山等国外城市的发展经验,提出深圳应优先发展生产性服务业,抑制消费性服务业成本的快速上涨。     

Foreign aid,public investment,and the informal economy

This paper examines the absorption of foreign aid in the presence of formal and informal production. Calibrating a two-sector open economy model to 67 aid-recipient countries for 1990–2019, we show that an increase in foreign aid drives resources into the informal sector, and away from the formal sector. With untied aid, the expansion of the informal sector can lead to an economic contraction through the Dutch Disease effect. An economic expansion with an increase in the share of formal production can be attained by re-allocating existing aid to public investment rather than an increase in the aggregate level of aid.     

Spatially Clustered Outbreak Detection Using the EWMA SCAN Statistics with Multiple Sized Windows

Spatio-temporal surveillance methods for detecting outbreaks of disease are fairly common in the literature with the SCAN statistic setting the benchmark. If the shape and size of the outbreaks are known in advance, then the SCAN statistic can be trained to efficiently detect these, however this is seldom true. Therefore, we want to devise plans that are efficient at detecting a number of outbreaks that vary in size and shape. This article examines plans which use the exponential weighted moving average statistic to build temporal memory into plans and tries to develop robust plans for detecting outbreaks of unknown shapes and sizes.