Mapping spatiotemporal patterns of events using social media: a case study of influenza trends

Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US using Twitter data. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data.     

南京市呼吸道疾病时间序列模型的建立与分析

利用1990～2000年南京市支气管哮喘与感冒逐月就诊人数病例资料和同期国家气象中心资料室的月平均压、温、湿等资料,讨论了2种疾病发病人数的月际和年际变化规律,分别建立了包含气象因子的预报2种疾病逐月就诊人数的条件异方差和动态回归模型。南京地区支气管哮喘就诊人数高峰期出现在春末与秋季,逐年的就诊人数呈现上升趋势,其发病与当月月平均气压、上一个月月平均温度有关。感冒就诊人数高峰期出现在夏秋季,其发病与月平均最低温度和月极端最低温度有关。将模型应用于2种疾病逐月就诊人数的等级预报中,其预报等级准确率分别达75．0％和66．7％,表明此建模方案是可行的,此类模型有一定的使用价值。     

地理信息系统(GIS)在新发H7N9禽流感防控中的应用

通过对H7N9禽流感防控研究的归纳,总结了目前GIS技术在新发H7N9禽流感疫情的时空分布、流行规律、影响因素和风险评估等方面的应用研究,揭示了GIS技术在疫情防控中发挥的重要作用,为其他传染病特别是新发传染病的防控研究提供了借鉴。     

Remote sensing and avian influenza: A review of image processing methods for extracting key variables affecting avian influenza virus survival in water from Earth Observation satellites

Recent studies have highlighted the potential role of water in the transmission of avian influenza (AI) viruses and the existence of often interacting variables that determine the survival rate of these viruses in water; the two main variables are temperature and salinity.     

Meteorological parameters as predictors for seasonal influenza

Seasonal influenza causes 5?million severe illnesses and 500,000 deaths annually worldwide. Among the factors that have been linked to influenza transmission are meteorological parameters, especially temperature and humidity. Low temperature and humidity have been associated with influenza seasonality in the temperate regions, whereas the tropics typically observe higher influenza transmission during rainy season. In this study, we assessed the role of meteorological factors on influenza transmission using both satellite-derived and ground station data for temperate and sub-tropical regions. Auto Regressive Integrated Moving Average and Neural Network were employed to assess the meteorological indicators and for forecasting. Our findings show that measures of temperature, humidity, rainfall and solar radiation can be used as indicators to forecast influenza. We also found that rainfall can be used as a predictor for sub-tropical region, but not in all temperate regions. Overall, our models can predict the timing of influenza peak.     

一株H_5亚型禽流感病毒血凝素基因的克隆与序列分析

根据GenBank中发表的H5亚型禽流感病毒HA基因序列设计2对引物,用RT-PCR方法从禽流感病毒广东分离病毒株(A/Chicken/Guangdong/1997)中扩增HA基因cDNA片段,并将其克隆至pMD-18T载体进行核苷酸序列测定。结果表明:用2对引物所扩增的片段大小分别约为1300 bp和800 bp,经序列拼接获得的HA基因cDNA长度约为1601 bp,编码533个氨基酸,与国内己发表的11个代表株的核苷酸和氨基酸序列同源性为分别为96.9%~99.9%和86.5%~93.0%;HA基因编码的氨基酸序列的系统进化树也表明A/Chicken/Guang-dong/1997、A/Goose/Huadong/01/2000、A/Ck/Hk/37.4/2002、A/Chicken/Zhoukou/2/02、A/Duck/Guangxi/53/2002、A/Duck/Fujian/01/2002等毒株处于同一进化枝,亲缘关系较近;而与A/Silly/Chicken/Hongkong/SF189/01株处于不同进化枝,亲缘关系较远。     

An ontology-based framework for extracting spatio-temporal influenza data using Twitter

Early detection of influenza outbreaks is one of the key priorities on a national level for preparedness and planning. This study presents the design and implementation of Fluwitter, which is a spatio-temporal web-based prototype framework for pseudo real-time detection of influenza outbreaks from Twitter. Specifically, the framework integrates PostgreSQL database server with PostGIS spatial extension, Twitter streaming client, pre-processor, tagger and similarity calculator for semantic information extraction (IE). The IE of tagged terms is supported by Natural Language Processing (NLP) techniques, DBpediaSpotlight and WordNet Similarity for Java (WS4J), while data analytics, visualization, and mapping are supported by GeoServer and other GIS Free Open Source Software (FOSS). The prototype was calibrated to maximize detection of influenza using rules developed from ontology-based semantic similarity scores. The Twitter-generated influenza cases were validated by weekly hospitalization records issued by Ohio Department of Health (ODH). The optimized rule produced a final F-measure value of 0.72 and accuracy (ACC) value of 94.4%. The validation suggested the existence of moderate correlations for the beginning of the time period Southeast region (r?=?0.52), the Northwestern region (r?=?0.38), and the Central region (r?=?0.33) and weak correlations for the entire time period. The potential strengths and benefits of the prototype are shown through spatio-temporal assessment and visualization of influenza potential in Ohio.     

Agent-based simulation for a dual-diffusion process of influenza and human preventive behavior

Influenza disease diffuses from one individual to another through their close contacts, while preventive behavior propagates through inter-personal influences. These two diffusion processes take place simultaneously and interact with one another in opposite directions. Many current influenza studies consider the diffusion of influenza, but few of them have incorporated the diffusion of human preventive behavior. This article proposes an original dual-diffusion model to couple these two diffusion processes. The conceptual framework of the model incorporates these two opposite processes into a human contact network. An agent-based approach, network theory, disease model, and behavioral model are integrated to formulate the conceptual framework. Model implementation simulates an influenza epidemic in an urbanized area of one million individuals. The simulation results suggest that the proposed model offers a close representation of the observed data. The research findings will provide a more rational basis for influenza control.     

甲流感疫情时空聚集性的GIS分析

2009年肆虐全球的甲流感疫情是由一种突变的猪流感病毒引发的流感,故又被称为猪流感。自2009年5月11日发现首例甲流感病例传入我国以来,在短短几个月的时间里,疫情迅速蔓延并呈现全国大爆发的态势。深圳因地理位置特殊,流动人口众多,一直是我国流行病的高发地区。本文以深圳市为例,对2009年5月26日至2009年11月15日间通过传染病疫情信息网络直报系统所上报的甲型H1N1流感确诊病例,分别依据患者的性别、年龄、职业等属性进行了统计,同时以日发病数为基本单位对这期间的甲流感疫情进行了时序与特征分析;并将病例数据输入地理信息系统进行地理空间定位,选取病例患者的家庭住址作为地理空间定位的基本单元,利用回顾性时空重排扫描统计量的方法对这期间深圳市的甲流感疫情进行时空聚集性分析。结果显示,深圳市的甲流感疫情的时空聚集性重点表现在9月份上旬与香港接壤的南部地区,对深圳市疫情的防控要重点布置在与香港往来的几个通关口岸处。     

禽流感病毒抗原表位分析及H5N1亚型基因工程疫苗设计

以亚洲地区H5N1亚型禽流感病毒(Avian Influenze Virus)流行株为研究对象,利用计算机软件,对同源性较高的HA1(Hemagglutnin,HA,血凝素)和NP(Nucleocapsid protein,核衣壳蛋白)进行全基因序列分析,优选出HA1蛋白的主要T细胞表位和B细胞表位,以及NP蛋白的主要CTL(Cytotoxicity T lymphocyte,细胞毒性T淋巴细胞)表位.依据这些优选表位,设计了禽流感病毒H5N1亚型基因工程疫苗.构建了基因工程疫苗表达载体pRSET-AIV,外源基因能够在大肠杆菌表达系统中得到良好表达.表达产物免疫小鼠后,血清中IgA和IgG抗体水平明显上升,在体外培养脾细胞可产生IL-2、IL-4和IFN-γ细胞因子.验证了该H5N1亚型基因工程疫苗的抗原性,证实该基因工程疫苗在免疫小鼠体内激发体液免疫的同时调动了细胞免疫.