Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks

Recent research has focused on the monitoring of global–scale online data for improved detection of epidemics, mood patterns, movements in the stock market political revolutions, box-office revenues, consumer behaviour and many other important phenomena. However, privacy considerations and the sheer scale of data available online are quickly making global monitoring infeasible, and existing methods do not take full advantage of local network structure to identify key nodes for monitoring. Here, we develop a model of the contagious spread of information in a global-scale, publicly-articulated social network and show that a simple method can yield not just early detection, but advance warning of contagious outbreaks. In this method, we randomly choose a small fraction of nodes in the network and then we randomly choose a friend of each node to include in a group for local monitoring. Using six months of data from most of the full Twittersphere, we show that this friend group is more central in the network and it helps us to detect viral outbreaks of the use of novel hashtags about 7 days earlier than we could with an equal-sized randomly chosen group. Moreover, the method actually works better than expected due to network structure alone because highly central actors are both more active and exhibit increased diversity in the information they transmit to others. These results suggest that local monitoring is not just more efficient, but also more effective, and it may be applied to monitor contagious processes in global–scale networks.     

Performance of Social Network Sensors during Hurricane Sandy

Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the “friendship paradox”, is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users’ network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple “sentiment sensing” technique that can detect and locate disasters.     

Social Modulation of Contagious Yawning in Wolves

On the basis of observational and experimental evidence, several authors have proposed that contagious yawn is linked to our capacity for empathy, thus presenting a powerful tool to explore the root of empathy in animal evolution. The evidence for the occurrence of contagious yawning and its link to empathy, however, is meagre outside primates and only recently domestic dogs have demonstrated this ability when exposed to human yawns. Since dogs are unusually skilful at reading human communicative behaviors, it is unclear whether this phenomenon is deeply rooted in the evolutionary history of mammals or evolved de novo in dogs as a result of domestication. Here we show that wolves are capable of yawn contagion, suggesting that such ability is a common ancestral trait shared by other mammalian taxa. Furthermore, the strength of the social bond between the model and the subject positively affected the frequency of contagious yawning, suggesting that in wolves the susceptibility of yawn contagion correlates with the level of emotional proximity. Moreover, female wolves showed a shorter reaction time than males when observing yawns of close associates, suggesting that females are more responsive to their social stimuli. These results are consistent with the claim that the mechanism underlying contagious yawning relates to the capacity for empathy and suggests that basic building blocks of empathy might be present in a wide range of species.     

Detection of Pathogenic Viruses in Sewage Provided Early Warnings of Hepatitis A Virus and Norovirus Outbreaks

Most persons infected with enterically transmitted viruses shed large amounts of virus in feces for days or weeks, both before and after onset of symptoms. Therefore, viruses causing gastroenteritis may be detected in wastewater, even if only a few persons are infected. In this study, the presence of eight pathogenic viruses (norovirus, astrovirus, rotavirus, adenovirus, Aichi virus, parechovirus, hepatitis A virus [HAV], and hepatitis E virus) was investigated in sewage to explore whether their identification could be used as an early warning of outbreaks. Samples of the untreated sewage were collected in proportion to flow at Ryaverket, Gothenburg, Sweden. Daily samples collected during every second week between January and May 2013 were pooled and analyzed for detection of viruses by concentration through adsorption to milk proteins and PCR. The largest amount of noroviruses was detected in sewage 2 to 3 weeks before most patients were diagnosed with this infection in Gothenburg. The other viruses were detected at lower levels. HAV was detected between weeks 5 and 13, and partial sequencing of the structural VP1protein identified three different strains. Two strains were involved in an ongoing outbreak in Scandinavia and were also identified in samples from patients with acute hepatitis A in Gothenburg during spring of 2013. The third strain was unique and was not detected in any patient sample. The method used may thus be a tool to detect incipient outbreaks of these viruses and provide early warning before the causative pathogens have been recognized in health care.     

深圳市南山区学校及托幼机构2010-2012年传染病暴发疫情分析

目的:分析南山区学校幼儿园2010-2012年传染病暴发疫情流行情况,探讨预防及控制疫情暴发的策略及方法.方法:以2010年南山区区级研究课题《学校传染病监测和预警系统研究》中开发的《学校传染病监测和预警系统》中的监测数据为基础数据,同时结合现场流行病学调查结果,采用MS Excel和SPSS对数据进行统计分析.结果:2010-2012年南山区学校及托幼机构共发生519起暴发疫情,其中甲类传染病疫情0起,乙类疫情56起,丙类传染病暴发疫情428起,其他传染病暴发疫情35起.暴发疫情种类以流感、手足口病、流行性出血热、感染性腹泻、水痘等为主,罹患人群共4227人,全区8个街道均有疫情发生,春秋季节暴发疫情较多,尤以3月份和9月份居多.结论:应重点加强学校和托幼机构等易感人群密集场所的传染病监测及疫情报告机制,落实晨检和因病缺课报告工作,提高疫情预警效率并及时采取防控措施,预防及控制流感、感染性腹泻、手足口病、水痘等常见传染病暴发疫情的发生,保护学生及幼儿的身体健康.     

A Rational Approach to Estimating the Surgical Demand Elasticity Needed to Guide Manpower Reallocation during Contagious Outbreaks

Background

Emerging infectious diseases continue to pose serious threats to global public health. So far, however, few published study has addressed the need for manpower reallocation needed in hospitals when such a serious contagious outbreak occurs.

Aim

To quantify the demand elasticity of the major surgery types in order to guide future manpower reallocation during contagious outbreaks.

Materials and Methods

Based on a nationwide research database in Taiwan, we extracted the monthly volumes of major surgery types for the period 1998–2003, which covered the SARS period, in order to carry out a time series analysis. The demand elasticity of each surgery type was then estimated by autoregressive integrated moving average (ARIMA) analysis.

Results

During the study period, the surgical volumes of most selected surgery types either increased or remained steady. We categorized these surgery types into low-, moderate- and high-elastic groups according to their demand elasticity. Appendectomy, ‘open reduction of fracture with internal fixation’ and ‘free skin graft’ were in the low demand elasticity group. Transurethral prostatectomy and extracorporeal shockwave lithotripsy (ESWL) were in the high demand elasticity group. The manpower of the departments carrying out the surgeries with low demand elasticity should be maintained during outbreaks. In contrast, departments in charge of surgeries mainly with high demand elasticity, like urology departments, may be in a position to have part of their staff reallocated.

Conclusions

Taking advantage of the demand variation during the SARS period in 2003, we adopted the concept of demand elasticity and used a time series approach to figure out an effective index of demand elasticity for various types of surgery that could be used as a rational reference to carry out manpower reallocation during contagious outbreak situations.     

用于凝血检测的声波传感器研究及应用进展

针对大力发展灵敏准确、实时快速及不受场地环境限制的床旁（POC）凝血检测需求,本文基于凝血过程主要物理化学变化及相应参数变化,对声波传感器在凝血检测中的传感分析原理及模型进行了分析总结。凝血过程是内源性或者外源性的激活剂激活凝血因子引发的一系列酶联反应,血液的黏度、剪切阻力、密度等物理参数随之发生变化,声波传感器通过感应凝血过程中这些物理参数的变化,建立待测样本与凝血时间、血凝块的形成动力学及蛋白质分子含量等的关系,以反映患者的凝血状态。本文重点对石英晶体微天平（QCM）、声表面波（SAW）传感器、薄膜体声波谐振器（FBAR）和兰姆（Lamb）波传感器的传感原理及数学模型进行了介绍;从传感器的结构、压电材料、敏感膜、加工等角度出发,对声波传感器的设计思路及研究重点进行了探讨;结合血液样本特色,剖析了声波传感器的检测信号与凝血过程的对应关系,并综述声波传感器在凝血过程中的应用进展;基于声波传感器快速灵敏、体积小、测试所需样品量少等特点,将其集成到微流控芯片中,以控制凝血检测环境、测试条件等,并适应床旁凝血测试应用需求,对集成声波传感器的微流控芯片在凝血检测中的应用进展和研究重点进行了分析...     

胰腺癌早期检测的生物标志物（英文）

胰腺癌症是最难诊断和治疗的恶性肿瘤之一,其特点是发病隐匿、进展迅速、预后差。目前,手术治疗仍然是首选治疗方法。然而由于缺乏早期症状,大约70%的患者在确诊时已经出现局部扩散或远端转移,从而无法进行手术治疗。由此看来,早期检测是提高患者治疗效果和预后的有效途径。临床上使用的成像方法 （CT、MRI、EUS等）通常无法检测早期病变,并且很容易受到操作员的影响。常规临床标志物如CA19-9、CA125、CA242和CEA受到限制,其敏感性或特异性不令人满意。因此,寻找新的具有高敏感性和特异性的标志物是实现胰腺癌早期检测的关键。近年来,对生物标志物的广泛研究主要集中在遗传学、转录组学和蛋白质组学上。特别是由microRNA(miRNA)、long non-coding RNA(lncRNA)和circRNA(circRNA)组成的非蛋白质编码RNA(non-protein coding RNA,ncRNA)为胰腺癌的早期检测提出了许多新思路。然而,其中绝大多数仍处于实验室研究阶段。而一项成熟的生物标志物研究应该整合基因组学、转录组学、蛋白质组学或代谢组学的数据,并结合患者的个体特征（如体重指数...     

Social Network Analysis for Program Implementation

This paper introduces the use of social network analysis theory and tools for implementation research. The social network perspective is useful for understanding, monitoring, influencing, or evaluating the implementation process when programs, policies, practices, or principles are designed and scaled up or adapted to different settings. We briefly describe common barriers to implementation success and relate them to the social networks of implementation stakeholders. We introduce a few simple measures commonly used in social network analysis and discuss how these measures can be used in program implementation. Using the four stage model of program implementation (exploration, adoption, implementation, and sustainment) proposed by Aarons and colleagues [1] and our experience in developing multi-sector partnerships involving community leaders, organizations, practitioners, and researchers, we show how network measures can be used at each stage to monitor, intervene, and improve the implementation process. Examples are provided to illustrate these concepts. We conclude with expected benefits and challenges associated with this approach.     

Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as “social sensors.” Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We confirm that these findings also hold true for tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.     

Flying Insect Detection and Classification with Inexpensive Sensors

An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect’s flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.     

Configurational Statistics of Magnetic Bead Detection with Magnetoresistive Sensors

Magnetic biosensors detect magnetic beads that, mediated by a target, have bound to a functionalized area. This area is often larger than the area of the sensor. Both the sign and magnitude of the average magnetic field experienced by the sensor from a magnetic bead depends on the location of the bead relative to the sensor. Consequently, the signal from multiple beads also depends on their locations. Thus, a given coverage of the functionalized area with magnetic beads does not result in a given detector response, except on the average, over many realizations of the same coverage. We present a systematic theoretical analysis of how this location-dependence affects the sensor response. The analysis is done for beads magnetized by a homogeneous in-plane magnetic field. We determine the expected value and standard deviation of the sensor response for a given coverage, as well as the accuracy and precision with which the coverage can be determined from a single sensor measurement. We show that statistical fluctuations between samples may reduce the sensitivity and dynamic range of a sensor significantly when the functionalized area is larger than the sensor area. Hence, the statistics of sampling is essential to sensor design. For illustration, we analyze three important published cases for which statistical fluctuations are dominant, significant, and insignificant, respectively.