人口流动的冲击

<正>时至今日,人口迁移流动已经是一种普遍的社会经济现象。在我国迁移与流动的统计、调查及研究中,较普遍采用的关于人口迁移的定义为：“发生在国内不同省区或县(市、市辖区)之间的、各类改变户口登记常住地的人口移动。以及发生在各经济类型地区之间的和各自然类型地区之间的、具有人口学意义的改变户口登记常住地的人口移动”。此定义包括组织移民和自发迁移,比如建国后国家组织的大规模的开发移民、环境移民及近年的三峡水库移民。提起人口流动,人们往往会联想到“民工潮”。一般来说,流动人口特指那些临时性的人口移动。而在目前情况下,     

农村基层疫情防控逻辑、困境与优化路径

新型冠状病毒疫情发生时,正值中国春节前后,疫情的突发性、严重性和大规模的流动人口跨区域迁移返乡,致使农村成为疫情防控的重要战场。剖析当下农村基层政府在疫情防控工作中的执行场域及样态发现,无缝隙政府模式运作下"网格化+精细化+信息化"的运行机制构成了当下农村疫情防控的主要策略。但当下农村疫情防控仍面临着集体行动不合理、行为习惯不科学、部分党政干部不作为和医疗物资不充足的现实困境。优化基层疫情防控路径,必须提高农村居民卫生意识,凝聚民心共克危机;明确基层应急管理标准依据,增加农村应急物资储备;合理利用大数据,加强基层应急管理信息化建设;推进危机防控内外部监督,建立有效的主体问责和容错机制。     

OA办公系统在疾控公文管理中的应用分析

本文在新型冠状病毒肺炎疫情防控(简称“新冠疫情防控”)下,针对OA办公系统在疾控公文管理中的应用进行了分析,以进一步提高疾控公文管理水平,致力于疾控工作尤其是新冠疫情防控的更好开展和取得更大成就。     

对贫困地区人口迁移政策的思考--以湖北省恩施市龙凤镇为例

通过对全国综合扶贫改革试点---恩施市龙凤镇人口状况调查结果显示：存在农业人口数量多，贫困人口占比高，人口迁移任务重；外出打工农民比例大，家庭“老+小”特征明显，人口迁移工作难度大；劳动力素质低、技能水平弱，迁移后生存难等问题。为此，要实施分类与统一相结合的迁移政策，让贫困人口实行镇内镇外迁移双向选择；注重教育，提高迁移人口的文化素质和技能水平；以民生为本，逐步完善迁移人口社会保障体系。     

办公室人员如何应对甲型H1N1流感病毒

自2009年3月墨西哥和美国等先后出现人感染甲型H1N1流感病毒以来,甲型H1N1流感就开始被世界卫生组织高度重视。世卫组织之所以对这次疫情高度关注,主要源于三大理由：这次出现的人流感病例与动物流感病毒有关联,而且在实验中已证实引发疫情的病毒是猪流感病毒A／H1N1亚型,是一种之前从未在人和猪身上出现过的新型猪流感病毒;人感染猪流感病毒的疫情在多个社区爆发;发病人群多为青壮年,而不是季节性流感的易感人群一老人和儿童,这与人禽流感非常相似。     

城市突发公共卫生事件的循证治理机制研究

城市突发公共卫生事件的治理是城市公共管理、城市治理体系和治理能力现代化建设的重要工作之一。在分析我国现有突发公共卫生事件治理体系及存在问题的基础上,构建了循证治理的理论框架,并从循证治理的证据、公共价值和领导力三要素出发,总结突发公共卫生事件的循证治理过程,包括循证防控期、循证预警期、循证控制期和事后循证重建期四个过程,围绕循证治理过程中证据的生产、传播和应用,总结突发公共卫生事件的循证治理机制。以2019年新型冠状病毒肺炎(COVID-19)疫情的早期治理过程为案例,对疫情防控过程中证据的生产、传播和使用过程进行实证研究。研究结果表明,COVID-19疫情的早期证据质量被高估,决策者的公共价值导向和决策者的领导力还有待提升。最后从构建循证治理的证据平台角度出发,提出优化突发公共卫生事件循证治理的若干政策建议。     

基于鲁棒优化的突发公共卫生事件精准筛查策略研究

“精准筛查”是落实突发公共卫生事件精准防控工作的基础保障和重要手段，但是如何科学合理地制定突发公共卫生事件精准筛查策略尚未开展深入研究。本文根据传染病传播机理，将待检测群体进行分类，引入“检测效用”刻画各类人群在不同时间检测对突发公共卫生事件传播扩散的抑制程度，建立以检测效用最大化为目标的鲁棒优化模型，并将其转化为易求解的鲁棒等价模型，确定最优的筛查方案。最后通过算例仿真验证模型及其转化方法的可行性和有效性，为突发公共卫生事件的精准筛查策略提供了理论指导和决策支持。     

可及性视角下医疗组织分布特征及其对疫情防控的影响研究

基于可及性理论,从医疗服务资源的可获得性和可达性两个角度量化各地级市疫情防控定点医疗组织机构的空间分布特征以及空间组合模式,并探讨其对疫情防控效果的影响。研究结果显示,在控制了城市基本特征、人员流动程度、基础医疗资源状况和省级固定效应之后,按人口聚集分布定点医院显著负向影响各城市累计确诊病例数,而按人口聚集分布发热门诊显著增加确诊病例数。相比于其他空间组合模式,定点医院按人口聚集分布,发热门诊按空间平均分布的组合方式对于疫情防控的效果最好。随着疫情严重程度的上升,按人口聚集分布定点医院对于疫情的抑制作用以及按人口聚集分布发热门诊对疫情防控的负面影响被进一步强化。     

突发疫情环境下的应急预算分配优化模型研究北大核心CSSCI

针对突发疫情环境下应急预算分配与疫情传播扩散之间的交互作用问题,本文构建了一类创新的决策框架模型。在该决策框架中,既考虑了不同感染区域之间人口流动对疫情扩散行为的影响,又融合了应急预算受限条件下隔离病房数量有限并由此导致的患者入院治疗率变化等关键因素。在此基础上,借鉴背包问题的建模思想,构建了政府应急预算资金分配的混合整数非线性规划模型并进行了模型线性化转换。算例测试表明,突发疫情环境下政府的应急预算应该向人口流入区域进行适当政策倾斜,且应急预算资金和起始干预时间都存在较明显的阈值效应。超过阈值后,即使增加应急预算,对疫情控制作用并不明显。     

COVID-19疫情下一种新的地铁平峰运营策略

地铁在城市交通中发挥着重要作用。然而，在新冠肺炎(COVID-19)疫情下，地铁的运营出现了包括消毒、限流及出行独立等多重约束。错峰出行成为了众多城市地铁运营过程中的必然选择。如何既满足居民的基本出行需求，为顺利实现复工复学提供交通支持，又能有效降低乘客感染病毒的风险与追踪密切接触者的成本成为了城市地铁运营的新目标。本文通过对北京地铁运营现状及居民的出行规律分析发现，在新型冠状病毒疫情下，地铁运营过程中存在出行需求与地铁运力不匹配，复工复产与疫情防控，乘客交叉出行数量过多等难题，并针对以上难题提出了分时段复工出行与周末可复工在内的复杂指派模型。这一指派模型不仅实现了城市关键地铁站点平峰人流量、降低疫情传播风险及追踪难度的目标，同时通过模型目标函数与约束条件的灵活修改可实现更为复杂的乘坐地铁复工复产目标。本文所提出的模型在复杂周期性平峰问题中具有较强的推广应用价值。     

A Probabilistic Transmission Dynamic Model to Assess Indoor Airborne Infection Risks

The purpose of this article is to quantify the public health risk associated with inhalation of indoor airborne infection based on a probabilistic transmission dynamic modeling approach. We used the Wells-Riley mathematical model to estimate (1) the CO2 exposure concentrations in indoor environments where cases of inhalation airborne infection occurred based on reported epidemiological data and epidemic curves for influenza and severe acute respiratory syndrome (SARS), (2) the basic reproductive number, R0 (i.e., expected number of secondary cases on the introduction of a single infected individual in a completely susceptible population) and its variability in a shared indoor airspace, and (3) the risk for infection in various scenarios of exposure in a susceptible population for a range of R0. We also employ a standard susceptible-infectious-recovered (SIR) structure to relate Wells-Riley model derived R0 to a transmission parameter to implicate the relationships between indoor carbon dioxide concentration and contact rate. We estimate that a single case of SARS will infect 2.6 secondary cases on average in a population from nosocomial transmission, whereas less than 1 secondary infection was generated per case among school children. We also obtained an estimate of the basic reproductive number for influenza in a commercial airliner: the median value is 10.4. We suggest that improving the building air cleaning rate to lower the critical rebreathed fraction of indoor air can decrease transmission rate. Here, we show that virulence of the organism factors, infectious quantum generation rates (quanta/s by an infected person), and host factors determine the risk for inhalation of indoor airborne infection.     

重大传染病疫情演化情境下应急物资配置决策建模分析:以新冠肺炎疫情为例

基于对新冠肺炎疫情时空分布的分析,从时段、关键事件、传播动力学、空间分布、感染规模、信息特征、医疗资源等7个维度构建了重大传染病疫情演化的5种情境,提出了各种情境下需要解决的5个关键应急物资配置决策问题。综合考虑应急物资配置的空间、信息、物资、供应、需求和网络等特性,分析了每一个决策问题进行建模优化的关键因素。基于这些关键因素构建了一个多周期贝叶斯序贯决策模型,给出了求解步骤和解析解,并结合武汉疫情进行了算例分析,验证了模型的有效性。在重大传染病疫情演化情境下,综合考虑这些关键因素,应用贝叶斯决策分析进行应急物资配置决策建模有利于建立更加符合实际的决策模型,减少决策损失。     

The Impact of Population,Contact, and Spatial Heterogeneity on Epidemic Model Predictions

Our objective was to evaluate the effect that complexity in the form of different levels of spatial, population, and contact heterogeneity has in the predictions of a mechanistic epidemic model. A model that simulates the spatiotemporal spread of infectious diseases between animal populations was developed. Sixteen scenarios of foot‐and‐mouth disease infection in cattle were analyzed, involving combinations of the following factors: multiple production‐types (PT) with heterogeneous contact and population structure versus single PT, random versus actual spatial distribution of population units, high versus low infectivity, and no vaccination versus preemptive vaccination. The epidemic size and duration was larger for scenarios with multiple PT versus single PT. Ignoring the actual unit locations did not affect the epidemic size in scenarios with multiple PT/high infectivity, but resulted in smaller epidemic sizes in scenarios using multiple PT/low infectivity. In conclusion, when modeling fast‐spreading epidemics, knowing the actual locations of population units may not be as relevant as collecting information on population and contact heterogeneity. In contrast, both population and spatial heterogeneity might be important to model slower spreading epidemic diseases. Our findings can be used to inform data collection and modeling efforts to inform health policy and planning.     

Assessing Infection Control Measures for Pandemic Influenza

We construct a mathematical model of aerosol (i.e., droplet-nuclei) transmission of influenza within a household containing one infected and embed it into an epidemic households model in which infecteds occasionally infect someone from another household; in a companion paper, we argue that the contribution from contact transmission is trivial for influenza and the contribution from droplet transmission is likely to be small. Our model predicts that the key infection control measure is the use of N95 respirators, and that the combination of respirators, humidifiers, and ventilation reduces the threshold parameter (which dictates whether or not an epidemic breaks out) by ≈20% if 70% of households comply, and by ≈40% if 70% of households and workplaces comply (≈28% reduction would have been required to control the 1918 pandemic). However, only ≈30% of the benefits in the household are achieved if these interventions are used only after the infected develops symptoms. It is also important for people to sleep in separate bedrooms throughout the pandemic, space permitting. Surgical masks with a device (e.g., nylon hosiery) to reduce face-seal leakage are a reasonable alternative to N95 respirators if the latter are in short supply.     

A Scenario‐Based Evaluation of the Middle East Respiratory Syndrome Coronavirus and the Hajj

Between April 2012 and June 2014, 820 laboratory‐confirmed cases of the Middle East respiratory syndrome coronavirus (MERS‐CoV) have been reported in the Arabian Peninsula, Europe, North Africa, Southeast Asia, the Middle East, and the United States. The observed epidemiology is different to SARS, which showed a classic epidemic curve and was over in eight months. The much longer persistence of MERS‐CoV in the population, with a lower reproductive number, some evidence of human‐to‐human transmission but an otherwise sporadic pattern, is difficult to explain. Using available epidemiological data, we implemented mathematical models to explore the transmission dynamics of MERS‐CoV in the context of mass gatherings such as the Hajj pilgrimage, and found a discrepancy between the observed and expected epidemiology. The fact that no epidemic occurred in returning Hajj pilgrims in either 2012 or 2013 contradicts the long persistence of the virus in human populations. The explanations for this discrepancy include an ongoing, repeated nonhuman/sporadic source, a large proportion of undetected or unreported human‐to‐human cases, or a combination of the two. Furthermore, MERS‐CoV is occurring in a region that is a major global transport hub and hosts significant mass gatherings, making it imperative to understand the source and means of the yet unexplained and puzzling ongoing persistence of the virus in the human population.     

Selecting Nonpharmaceutical Interventions for Influenza

Models of influenza transmission have focused on the ability of vaccination, antiviral therapy, and social distancing strategies to mitigate epidemics. Influenza transmission, however, may also be interrupted by hygiene interventions such as frequent hand washing and wearing masks or respirators. We apply a model of influenza disease transmission that incorporates hygiene and social distancing interventions. The model describes population mixing as a Poisson process, and the probability of infection upon contact between an infectious and susceptible person is parameterized by p. While social distancing interventions modify contact rates in the population, hygiene interventions modify p. Public health decision making involves tradeoffs, and we introduce an objective function that considers the direct costs of interventions and new infections to determine the optimum intervention type (social distancing versus hygiene intervention) and population compliance for epidemic mitigation. Significant simplifications have been made in these models. However, we demonstrate that the method is feasible, provides plausible results, and is sensitive to the selection of model parameters. Specifically, we show that the optimum combination of nonpharmaceutical interventions depends upon the probability of infection, intervention compliance, and duration of infectiousness. Means by which realism can be increased in the method are discussed.     

Quantifying Water Pathogen Risk in an Epidemiological Framework

Traditionally, microbial risk assessors have used point estimates to evaluate the probability that an individual will become infected. We developed a quantitative approach that shifts the risk characterization perspective from point estimate to distributional estimate, and from individual to population. To this end, we first designed and implemented a dynamic model that tracks traditional epidemiological variables such as the number of susceptible, infected, diseased, and immune, and environmental variables such as pathogen density. Second, we used a simulation methodology that explicitly acknowledges the uncertainty and variability associated with the data. Specifically, the approach consists of assigning probability distributions to each parameter, sampling from these distributions for Monte Carlo simulations, and using a binary classification to assess the output of each simulation. A case study is presented that explores the uncertainties in assessing the risk of giardiasis when swimming in a recreational impoundment using reclaimed water. Using literature-based information to assign parameters ranges, our analysis demonstrated that the parameter describing the shedding of pathogens by infected swimmers was the factor that contributed most to the uncertainty in risk. The importance of other parameters was dependent on reducing the a priori range of this shedding parameter. By constraining the shedding parameter to its lower subrange, treatment efficiency was the parameter most important in predicting whether a simulation resulted in prevalences above or below non outbreak levels. Whereas parameters associated with human exposure were important when the shedding parameter was constrained to a higher subrange. This Monte Carlo simulation technique identified conditions in which outbreaks and/or nonoutbreaks are likely and identified the parameters that most contributed to the uncertainty associated with a risk prediction.     

A probabilistic transmission and population dynamic model to assess tuberculosis infection risk

The purpose of this study was to examine tuberculosis (TB) population dynamics and to assess potential infection risk in Taiwan. A well‐established mathematical model of TB transmission built on previous models was adopted to study the potential impact of TB transmission. A probabilistic risk model was also developed to estimate site‐specific risks of developing disease soon after recent primary infection, exogenous reinfection, or through endogenous reactivation (latently infected TB) among Taiwan regions. Here, we showed that the proportion of endogenous reactivation (53–67%) was larger than that of exogenous reinfection (32–47%). Our simulations showed that as epidemic reaches a steady state, age distribution of cases would finally shift toward older age groups dominated by latently infected TB cases as a result of endogenous reactivation. A comparison of age‐weighted TB incidence data with our model simulation output with 95% credible intervals revealed that the predictions were in an apparent agreement with observed data. The median value of overall basic reproduction number (R₀) in eastern Taiwan ranged from 1.65 to 1.72, whereas northern Taiwan had the lowest R₀ estimate of 1.50. We found that total TB incidences in eastern Taiwan had 25–27% probabilities of total proportion of infected population exceeding 90%, whereas there were 36–66% probabilities having exceeded 20% of total proportion of infected population attributed to latently infected TB. We suggested that our Taiwan‐based analysis can be extended to the context of developing countries, where TB remains a substantial cause of elderly morbidity and mortality.     

A Multicompartment SIS Stochastic Model with Zonal Ventilation for the Spread of Nosocomial Infections: Detection,Outbreak Management,and Infection Control

In this work, we study the environmental and operational factors that influence airborne transmission of nosocomial infections. We link a deterministic zonal ventilation model for the airborne distribution of infectious material in a hospital ward, with a Markovian multicompartment SIS model for the infection of individuals within this ward, in order to conduct a parametric study on ventilation rates and their effect on the epidemic dynamics. Our stochastic model includes arrival and discharge of patients, as well as the detection of the outbreak by screening events or due to symptoms being shown by infective patients. For each ventilation setting, we measure the infectious potential of a nosocomial outbreak in the hospital ward by means of a summary statistic: the number of infections occurred within the hospital ward until end or declaration of the outbreak. We analytically compute the distribution of this summary statistic, and carry out local and global sensitivity analysis in order to identify the particular characteristics of each ventilation regime with the largest impact on the epidemic spread. Our results show that ward ventilation can have a significant impact on the infection spread, especially under slow detection scenarios or in overoccupied wards, and that decreasing the infection risk for the whole hospital ward might increase the risk in specific areas of the health‐care facility. Moreover, the location of the initial infective individual and the protocol in place for outbreak declaration both form an interplay with ventilation of the ward.     

Optimal control analysis of a multigroup SEAIHRD model for COVID-19 epidemic

The COVID-19 pandemic has threatened public health and caused substantial economic loss to most countries worldwide. A multigroup susceptible–exposed–asymptomatic–infectious–hospitalized–recovered–dead (SEAIHRD) compartment model is first constructed to model the spread of the disease by dividing the population into three age groups: young (aged 0–19), prime (aged 20–64), and elderly (aged 65 and over). Then, we develop a free terminal time, partially fixed terminal state optimal control problem to minimize deaths and costs associated with hospitalization and the implementation of different control strategies. And the optimal strategies are derived under different assumptions about medical resources and vaccination. Specifically, we explore optimal control strategies for reaching herd immunity in the COVID-19 outbreak in a free terminal time situation to evaluate the effect of nonpharmaceutical interventions (NPIs) and vaccination as control measures. The transmission rate of SARS-CoV-2 is calibrated by using real data in the United States at the early stage of the epidemic. Through numerical simulation, we conclude that the outbreak of COVID-19 can be contained by implementing appropriate control of the prime age population and relatively strict control measures for young and elderly populations. Within a specific period, strict control measures should be implemented before the vaccine is marketed.