Reality Check for the Chinese Microblog Space: A Random Sampling Approach

Chinese microblogs have drawn global attention to this online application’s potential impact on the country’s social and political environment. However, representative and reliable statistics on Chinese microbloggers are limited. Using a random sampling approach, this study collected Chinese microblog data from the service provider, analyzing the profile and the pattern of usage for 29,998 microblog accounts. From our analysis, 57.4% (95% CI 56.9%,58.0%) of the accounts’ timelines were empty. Among the 12,774 non-zero statuses samples, 86.9% (95% CI 86.2%,87.4%) did not make original post in a 7-day study period. By contrast, 0.51% (95% CI 0.4%,0.65%) wrote twenty or more original posts and 0.45% (95% CI 0.35%,0.60%) reposted more than 40 unique messages within the 7-day period. A small group of microbloggers created a majority of contents and drew other users’ attention. About 4.8% (95% CI 4.4%,5.2%) of the 12,774 users contributed more than 80% (95% CI,78.6%,80.3%) of the original posts and about 4.8% (95% CI 4.5%,5.2%) managed to create posts that were reposted or received comments at least once. Moreover, a regression analysis revealed that volume of followers is a key determinant of creating original microblog posts, reposting messages, being reposted, and receiving comments. Volume of friends is found to be linked only with the number of reposts. Gender differences and regional disparities in using microblogs in China are also observed.     

Get the News Out Loudly and Quickly: The Influence of the Media on Limiting Emerging Infectious Disease Outbreaks

During outbreaks of infectious diseases with high morbidity and mortality, individuals closely follow media reports of the outbreak. Many will attempt to minimize contacts with other individuals in order to protect themselves from infection and possibly death. This process is called social distancing. Social distancing strategies include restricting socializing and travel, and using barrier protections. We use modeling to show that for short-term outbreaks, social distancing can have a large influence on reducing outbreak morbidity and mortality. In particular, public health agencies working together with the media can significantly reduce the severity of an outbreak by providing timely accounts of new infections and deaths. Our models show that the most effective strategy to reduce infections is to provide this information as early as possible, though providing it well into the course of the outbreak can still have a significant effect. However, our models for long-term outbreaks indicate that reporting historic infection data can result in more infections than with no reporting at all. We examine three types of media influence and we illustrate the media influence with a simulated outbreak of a generic emerging infectious disease in a small city. Social distancing can never be complete; however, for a spectrum of outbreaks, we show that leaving isolation (stopping applying social distancing measures) for up to 4 hours each day has modest effect on the overall morbidity and mortality.     

A review of the emergence of antibiotic resistance in bioaerosols and its monitoring methods

Despite significant public health concerns regarding infectious diseases in air environments, potentially harmful microbiological indicators, such as antibiotic resistance genes (ARGs) in bioaerosols, have not received significant attention. Traditionally, bioaerosol studies have focused on the characterization of microbial communities; however, a more serious problem has recently arisen due to the presence of ARGs in bioaerosols, leading to an increased prevalence of horizontal gene transfer (HGT). This constitutes a process by which bacteria transfer genes to other environmental media and consequently cause infectious disease. Antibiotic resistance in water and soil environments has been extensively investigated in the past few years by applying advanced molecular and biotechnological methods. However, ARGs in bioaerosols have not received much attention. In addition, ARG and HGT profiling in air environments is greatly limited in field studies due to the absence of suitable methodological approaches. Therefore, this study comprehensively describes recent findings from published studies and some of the appropriate molecular and biotechnological methods for monitoring antibiotic resistance in bioaerosols. In addition, this review discusses the main knowledge gaps regarding current methodological issues and future research directions.

     

Emotions as infectious diseases in a large social network: the SISa model

Human populations are arranged in social networks that determine interactions and influence the spread of diseases, behaviours and ideas. We evaluate the spread of long-term emotional states across a social network. We introduce a novel form of the classical susceptible–infected–susceptible disease model which includes the possibility for ‘spontaneous’ (or ‘automatic’) infection, in addition to disease transmission (the SISa model). Using this framework and data from the Framingham Heart Study, we provide formal evidence that positive and negative emotional states behave like infectious diseases spreading across social networks over long periods of time. The probability of becoming content is increased by 0.02 per year for each content contact, and the probability of becoming discontent is increased by 0.04 per year per discontent contact. Our mathematical formalism allows us to derive various quantities from the data, such as the average lifetime of a contentment ‘infection’ (10 years) or discontentment ‘infection’ (5 years). Our results give insight into the transmissive nature of positive and negative emotional states. Determining to what extent particular emotions or behaviours are infectious is a promising direction for further research with important implications for social science, epidemiology and health policy. Our model provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviours, health states, ideas or diseases with reservoirs.     

Are there Characteristics of Infectious Diseases that Raise Special Ethical Issues?1

This paper examines the characteristics of infectious diseases that raise special medical and social ethical issues, and explores ways of integrating both current bioethical and classical public health ethics concerns. Many of the ethical issues raised by infectious diseases are related to these diseases' powerful ability to engender fear in individuals and panic in populations. We address the association of some infectious diseases with high morbidity and mortality rates, the sense that infectious diseases are caused by invasion or attack on humans by foreign micro-organisms, the acute onset and rapid course of many infectious diseases, and, in particular, the communicability of infectious diseases. The individual fear and community panic associated with infectious diseases often leads to rapid, emotionally driven decision making about public health policies needed to protect the community that may be in conflict with current bioethical principles regarding the care of individual patients. The discussion includes recent examples where dialogue between public health practitioners and medical-ethicists has helped resolve ethical issues that require us to consider the infected patient as both a victim with individual needs and rights and as a potential vector of disease that is of concern to the community.     

ITEPE: A Source Tracing Algorithm for the Microblog

Finding the true source of a social network is a crucial component of social network information tracing. Using the new media microblog as an example, this paper provides a source tracing algorithm ITEPE (Initiators and Early Participants Extraction) to solve this problem. First, the cascade (session tree) is built according to the retweeting of a microblog, after which the cascade set (session forest) is clustered by topical relevance. Second, real initiators are identified through the user relationship network and information cascade network. The influence index and conformity index of every node is then iteratively calculated according to text sentiment analysis and information cascades and the early important participants are extracted. Finally, the real initiators and early participants are evaluated through an experiment.     

Considerations in Estimating Social and Economic Impacts of Immunotoxic Agents

To make appropriate regulatory policy decisions, the potential social and economic impacts of the policy must first be established. For environmental and occupational agents, social and economic impacts are derived from animal toxicology and, when available, human studies that serve as the base for risk-benefit analysis (RBA). Because immune function is associated with resistance to infectious disease, developing RBA for data derived from immunotoxicology studies will require determining the changes in the frequency or severity of infectious disease resulting from an exposure. Fortunately, considerable information is readily available for identifying the frequency of infectious diseases in the general population and its social and economic impacts and to assist the risk assessor when conducting RBA for immunotoxicology endpoints. The following is a brief review describing some issues in using immunotoxicity data when conducting RBA. It presents an economic methodology to determine the economic impacts of infectious diseases to society, sources where these types of information are available, and an example using a specific infectious disease, otitis media.     

Social Determinants of Health Information Seeking among Chinese Adults in Hong Kong

Background

Health communication inequalities were observed in Western population but less is known about them among the Chinese. We investigated health information seeking behaviours and its social determinants among Chinese adults in Hong Kong.

Methods

Probability-based sample surveys over telephone were conducted in 2009, 2010/11 and 2012 to monitor family health and information use. Frequency of health information seeking from television, radio, newspapers/magazines and Internet were recorded and dichotomised as ≥1 time/month and <1 time/month (reference). Logistic regression was used to yield adjusted odds ratios (aOR) of health information seeking for different demographic characteristics, socioeconomic status (education, employment and income), chronic disease and behaviours (smoking, drinking and physical activity).

Results

Among 4553 subjects in all surveys, most (85.1%) had sought health information monthly from newspapers/magazines (66.2%), television (61.4%), radio (35.6%) or Internet (33.2%). Overall, being male, lower education attainment, lower household income, ever-smoking and physical inactivity were associated with less frequent health information seeking (all P <0.05). Compared with younger people, older people were less likely to search health information from Internet but more like to obtain it from radio (both P for trend <0.001). Having chronic diseases was associated with frequent health information seeking from television (aOR  =  1.25, 95% CI: 1.07–1.47) and Internet (aOR  =  1.46, 95% CI: 1.24–1.73).

Conclusions

This study has provided the first evidence on health information inequalities from a non-Western population with advanced mass media and Internet penetration. Socioeconomic inequalities and behavioural clustering of health information seeking suggested more resources are needed for improving health communication in disadvantage groups.     

A Quantitative Approach to the Prioritization of Zoonotic Diseases in North America: A Health Professionals’ Perspective

Background

Currently, zoonoses account for 58% to 61% of all communicable diseases causing illness in humans globally and up to 75% of emerging human pathogens. Although the impact of zoonoses on animal health and public health in North America is significant, there has been no published research involving health professionals on the prioritization of zoonoses in this region.

Methodology/Principal Findings

We used conjoint analysis (CA), a well-established quantitative method in market research, to identify the relative importance of 21 key characteristics of zoonotic diseases for their prioritization in Canada and the US. Relative importance weights from the CA were used to develop a point-scoring system to derive a recommended list of zoonoses for prioritization in Canada and the US. Study participants with a background in epidemiology, public health, medical sciences, veterinary sciences and infectious disease research were recruited to complete the online survey (707 from Canada and 764 from the US). Hierarchical Bayes models were fitted to the survey data to derive CA-weighted scores for disease criteria. Scores were applied to 62 zoonotic diseases to rank diseases in order of priority.

Conclusions/Significance

We present the first zoonoses prioritization exercise involving health professionals in North America. Our previous study indicated individuals with no prior knowledge in infectious diseases were capable of producing meaningful results with acceptable model fits (79.4%). This study suggests health professionals with some knowledge in infectious diseases were capable of producing meaningful results with better-fitted models than the general public (83.7% and 84.2%). Despite more similarities in demographics and model fit between the combined public and combined professional groups, there was more uniformity across priority lists between the Canadian public and Canadian professionals and between the US public and US professionals. Our study suggests that CA can be used as a potential tool for the prioritization of zoonoses.     

Remote sensing and geographic information systems in the spatial temporal dynamics modeling of infectious diseases

Similar to species immigration or exotic species invasion, infectious disease transmission is strengthened due to the globalization of human activities. Using schistosomiasis as an example, we propose a conceptual model simulating the spatio-temporal dynamics of infectious diseases. We base the model on the knowledge of the interrelationship among the source, media, and the hosts of the disease. With the endemics data of schistosomiasis in Xichang, China, we demonstrate that the conceptual model is feasible; we introduce how remote sensing and geographic information systems techniques can be used in support of spatio-temporal modeling; we compare the different effects caused to the entire population when selecting different groups of people for schistosomiasis control. Our work illustrates the importance of such a modeling tool in supporting spatial decisions. Our modeling method can be directly applied to such infectious diseases as the plague, lyme disease, and hemorrhagic fever with renal syndrome. The application of remote sensing and geographic information systems can shed light on the modeling of other infectious disease and invasive species studies.     

Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak

Background

Surveys are popular methods to measure public perceptions in emergencies but can be costly and time consuming. We suggest and evaluate a complementary “infoveillance” approach using Twitter during the 2009 H1N1 pandemic. Our study aimed to: 1) monitor the use of the terms “H1N1” versus “swine flu” over time; 2) conduct a content analysis of “tweets”; and 3) validate Twitter as a real-time content, sentiment, and public attention trend-tracking tool.

Methodology/Principal Findings

Between May 1 and December 31, 2009, we archived over 2 million Twitter posts containing keywords “swine flu,” “swineflu,” and/or “H1N1.” using Infovigil, an infoveillance system. Tweets using “H1N1” increased from 8.8% to 40.5% (R ² = .788; p<.001), indicating a gradual adoption of World Health Organization-recommended terminology. 5,395 tweets were randomly selected from 9 days, 4 weeks apart and coded using a tri-axial coding scheme. To track tweet content and to test the feasibility of automated coding, we created database queries for keywords and correlated these results with manual coding. Content analysis indicated resource-related posts were most commonly shared (52.6%). 4.5% of cases were identified as misinformation. News websites were the most popular sources (23.2%), while government and health agencies were linked only 1.5% of the time. 7/10 automated queries correlated with manual coding. Several Twitter activity peaks coincided with major news stories. Our results correlated well with H1N1 incidence data.

Conclusions

This study illustrates the potential of using social media to conduct “infodemiology” studies for public health. 2009 H1N1-related tweets were primarily used to disseminate information from credible sources, but were also a source of opinions and experiences. Tweets can be used for real-time content analysis and knowledge translation research, allowing health authorities to respond to public concerns.     

Aging 2.0: Health Information about Dementia on Twitter

Online social media is widespread, easily accessible and attracts a global audience with a widening demographic. As a large proportion of adults now seek health information online and through social media applications, communication about health has become increasingly interactive and dynamic. Online health information has the potential to significantly impact public health, especially as the population gets older and the prevalence of dementia increases. However, little is known about how information pertaining to age-associated diseases is disseminated on popular social media platforms. To fill this knowledge gap, we examined empirically: (i) who is using social media to share information about dementia, (ii) what sources of information about dementia are promoted, and (iii) which dementia themes dominate the discussion. We data-mined the microblogging platform Twitter for content containing dementia-related keywords for a period of 24 hours and retrieved over 9,200 tweets. A coding guide was developed and content analysis conducted on a random sample (10%), and on a subsample from top users’ tweets to assess impact. We found that a majority of tweets contained a link to a third party site rather than personal information, and these links redirected mainly to news sites and health information sites. As well, a large number of tweets discussed recent research findings related to the prediction and risk management of Alzheimer’s disease. The results highlight the need for the dementia research community to harness the reach of this medium and its potential as a tool for multidirectional engagement.     

The More the Better? A Comparison of the Information Sources Used by the Public during Two Infectious Disease Outbreaks

Recent infectious disease outbreaks have resulted in renewed recognition of the importance of risk communication planning and execution to public health control strategies. Key to these efforts is public access to information that is understandable, reliable and meets their needs for informed decision-making on protective health behaviours. Learning from the trends in sources used in previous outbreaks will enable improvements in information access in future outbreaks. Two separate random-digit dialled telephone surveys were conducted in Alberta, Canada, to explore information sources used by the public, together with their perceived usefulness and credibility, during the 2003 Severe Acute Respiratory Syndrome (SARS) epidemic (n = 1209) and 2009–2010 H1N1 pandemic (n = 1206). Traditional mass media were the most used information sources in both surveys. Although use of the Internet increased from 25% during SARS to 56% during H1N1, overall use of social media was not as high as anticipated. Friends and relatives were commonly used as an information source, but were not deemed very useful or credible. Conversely, doctors and health professionals were considered credible, but not consulted as frequently. The use of five or more information sources increased by almost 60% between the SARS and H1N1 surveys. There was a shift to older, more educated and more affluent respondents between the surveys, most likely caused by a decrease in the use of landlines amongst younger Canadians. It was concluded that people are increasingly using multiple sources of health risk information, presumably in a complementary manner. Subsequently, although using online media is important, this should be used to augment rather than replace more traditional information channels. Efforts should be made to improve knowledge transfer to health care professionals and doctors and provide them with opportunities to be more accessible as information sources. Finally, the future use of telephone surveys needs to account for the changing demographics of the respondents accessed through such surveys.     

Emerging and Reemerging Infectious Diseases: Biocomplexity as an Interdisciplinary Paradigm

Understanding factors responsible for reemergence of diseases believed to have been controlled and outbreaks of previously unknown infectious diseases is one of the most difficult scientific problems facing society today. Significant knowledge gaps exist for even the most studied emerging infectious diseases. Coupled with failures in the response to the resurgence of infectious diseases, this lack of information is embedded in a simplistic view of pathogens and disconnected from a social and ecological context, and assumes a linear response of pathogens to environmental change. In fact, the natural reservoirs and transmission rates of most emerging infectious diseases primarily are affected by environmental factors, such as seasonality or meteorological events, typically producing nonlinear responses that are inherently unpredictable. A more realistic view of emerging infectious diseases requires a holistic perspective that incorporates social as well as physical, chemical, and biological dimensions of our planet’s systems. The notion of biocomplexity captures this depth and richness, and most importantly, the interactions of human and natural systems. This article provides a brief review and a synthesis of interdisciplinary approaches and insights employing the biocomplexity paradigm and offers a social–ecological approach for addressing and garnering an improved understanding of emerging infectious diseases. Drawing on findings from studies of cholera and other examples of emerging waterborne, zoonotic, and vectorborne diseases, a “blueprint” for the proposed interdisciplinary research framework is offered which integrates biological processes from the molecular level to that of communities and regional systems, incorporating public health infrastructure and climate aspects.     

Remote sensing and geographic information systems in the spatial temporal dynamics modeling of infectious diseases

Similar to species immigration or exotic species invasion,infectious disease transmis-sion is strengthened due to the globalization of human activities. Using schistosomiasis as an exam-ple,we propose a conceptual model simulating the spatio-temporal dynamics of infectious diseases. We base the model on the knowledge of the interrelationship among the source,media,and the hosts of the disease. With the endemics data of schistosomiasis in Xichang,China,we demonstrate that the conceptual model is feasible; we introduce how remote sensing and geographic information systems techniques can be used in support of spatio-temporal modeling; we compare the different effects caused to the entire population when selecting different groups of people for schistosomiasis control. Our work illustrates the importance of such a modeling tool in supporting spatial decisions. Our mod-eling method can be directly applied to such infectious diseases as the plague,lyme disease,and hemorrhagic fever with renal syndrome. The application of remote sensing and geographic informa-tion systems can shed light on the modeling of other infectious disease and invasive species studies.     

A metric of influential spreading during contagion dynamics through the air transportation network

The spread of infectious diseases at the global scale is mediated by long-range human travel. Our ability to predict the impact of an outbreak on human health requires understanding the spatiotemporal signature of early-time spreading from a specific location. Here, we show that network topology, geography, traffic structure and individual mobility patterns are all essential for accurate predictions of disease spreading. Specifically, we study contagion dynamics through the air transportation network by means of a stochastic agent-tracking model that accounts for the spatial distribution of airports, detailed air traffic and the correlated nature of mobility patterns and waiting-time distributions of individual agents. From the simulation results and the empirical air-travel data, we formulate a metric of influential spreading--the geographic spreading centrality--which accounts for spatial organization and the hierarchical structure of the network traffic, and provides an accurate measure of the early-time spreading power of individual nodes.     

Situational awareness and health protective responses to pandemic influenza A (H1N1) in Hong Kong: a cross-sectional study

Background

Whether information sources influence health protective behaviours during influenza pandemics or other emerging infectious disease epidemics is uncertain.

Methodology

Data from cross-sectional telephone interviews of 1,001 Hong Kong adults in June, 2009 were tested against theory and data-derived hypothesized associations between trust in (formal/informal) information, understanding, self-efficacy, perceived susceptibility and worry, and hand hygiene and social distancing using Structural Equation Modelling with multigroup comparisons.

Principal Findings

Trust in formal (government/media) information about influenza was associated with greater reported understanding of A/H1N1 cause (β = 0.36) and A/H1N1 prevention self-efficacy (β = 0.25), which in turn were associated with more hand hygiene (β = 0.19 and β = 0.23, respectively). Trust in informal (interpersonal) information was negatively associated with perceived personal A/H1N1 susceptibility (β = −0.21), which was negatively associated with perceived self-efficacy (β = −0.42) but positively associated with influenza worry (β = 0.44). Trust in informal information was positively associated with influenza worry (β = 0.16) which was in turn associated with greater social distancing (β = 0.36). Multigroup comparisons showed gender differences regarding paths from trust in formal information to understanding of A/H1N1 cause, trust in informal information to understanding of A/H1N1 cause, and understanding of A/H1N1 cause to perceived self-efficacy.

Conclusions/Significance

Trust in government/media information was more strongly associated with greater self-efficacy and handwashing, whereas trust in informal information was strongly associated with perceived health threat and avoidance behaviour. Risk communication should consider the effect of gender differences.     

提高医院传染病管理的实践

目的：总结解放军第302医院在防控传染病方面所践采取的措施,提高医院传染病管理相关工作。方法：完善传染病管理组织,明确责任;认真落实传染病管理相关制度;加强相关知识培训,强化医护人员责任意识,提高能动性、自觉性。总结分析以上相关措施实施后,2005年-2010年期间传染病报告情况。结果：2005年-2010年每年传染病疫情报告卡填写完整率逐渐提高,医院传染病报告漏报率呈逐年下降趋势,近两年呈现填写完整无漏报的情况。结论：提高医院传染病管理的相关措施的实施,使医护人员对的传染病防控意识有所提高,做到早发现、早报告、早隔离、早治疗,降低传染病传播风险,有效减少了医院交叉感染的发生,保障人们的生命健康和社会的发展稳定。     

Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control

There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC-estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness.