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

The Dynamics of Health Behavior Sentiments on a Large Online Social Network

Modifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individuals - homophily - or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious - by which we merely mean predictive of future negative sentiment expression - while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content-dependent

The dynamics of health behavior sentiments on a large online social network

Modifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individuals - homophily - or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious - by which we merely mean predictive of future negative sentiment expression - while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content-dependen

Digital Epidemiology

Mobile, social, real-time: the ongoing revolution in the way people communicate has given rise to a new kind of epidemiology. Digital data sources, when harnessed appropriately, can provide local and timely information about disease and health dynamics in populations around the world. The rapid, unprecedented increase in the availability of relevant data from various digital sources creates considerable technical and computational challenges

Digital epidemiology

Mobile, social, real-time: the ongoing revolution in the way people communicate has given rise to a new kind of epidemiology. Digital data sources, when harnessed appropriately, can provide local and timely information about disease and health dynamics in populations around the world. The rapid, unprecedented increase in the availability of relevant data from various digital sources creates considerable technical and computational challenges

Evolving Clustered Random Networks

We propose a Markov chain simulation method to generate simple connected random graphs with a specified degree sequence and level of clustering. The networks generated by our algorithm are random in all other respects and can thus serve as generic models for studying the impacts of degree distributions and clustering on dynamical processes as well as null models for detecting other structural properties in empirical networks

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 are 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.Comment: Accepted for publication in PLoS Computational Biolog

(A) Proportion of negative sentiments <i>p(-)</i> in the network communities. Dashed line shows overall proportion in the opinionated network.

<p>The proportions of negative and positive sentiments are significantly different from the overall proportions in the entire opinionated network (with the exception of community E). (<b>B</b>) Effect of positive assortativity index (<i>r</i>) on relative risk increase (compared to risk at <i>r</i>∼0) of disease outbreaks that infect at least 3% of the population. Blue line shows best fit of linear regression (confidence interval based on standard error). (<b>C</b>) Relative risk increase (compared to risk at <i>r</i>∼0) of disease outbreaks of a given fraction of the population (on horizontal axis) for two values of assortativity index (<i>r</i>), 0.075 (red) and 0.145 (green). Note that the latter corresponds to <i>r</i> found in the opinionated network (see main text).</p

(A) Total number of negative (red), positive (green), and neutral (blue) tweets relating to influenza A(H1N1) vaccination during the Fall wave of the 2009 pandemic.

<p>(<b>B</b>) Daily (gray) and 14 day moving average (blue) sentiment score during the same time. (<b>C</b>) Correlation between estimated vaccination rates for individuals older than 6 months, and sentiment score per HHS region (black dots) and states (gray dots). Numbers represent the ten regions as defined by the US Department of Human Health & Services. Lines shows best fit of linear regression (blue for regions, red for states).</p

Capacitive-triboelectric based hybrid sensor system for human-like tactile perception

Human skin contains slowly-adapting (SA) and rapidly-adapting mechanoreceptors (RA) through which it can discriminate between static and dynamic tactile stimuli. Bio-mimicking of such human tactile sensing systems using flexible and reliable sensors has recently gained importance for developing future robots and prosthetics with better sensory capabilities. In this work, a hybrid flexible sensor system consisting of a capacitive pressure sensor (CPS) (mimicking SA mechanoreceptors) firmly stacked over a triboelectric nanogenerator (TENG) (mimicking RA mechanoreceptors) was developed. CPS consisted of porous Ecoflex as the dielectric material, while the two layers used in TENG were made using copper-nickel conducting fabric and ITO-coated PET sheet. This hybrid sensor system was characterized and showed good sensitivity for CPS and voltage response for the TENG device. Later, three distinct scenarios for the hybrid system have been demonstrated, in which it was used for the qualitative hardness assessment, slip detection and impact/vibration detection. In summary, the signals from both CPS and TENG complement each other, making this hybrid sensor system capable of simultaneously detecting both static and dynamic pressure signals