Analyzing Social Media Data To Prevent Outbreaks
Researchers Develop New Method Of Analyzing Social Media Data To Identify Potential Disease Outbreaks
A new method to analyze social media data could help predict future outbreaks of diseases and viruses like COVID-19 and the measles.
In a new study, researchers from the University of Waterloo examined computer simulations to develop a new method of analyzing interactions on social media that can predict when a disease outbreak is likely.
The method predicts the tipping point beyond which a series of small incidents, like people espousing anti-vaccine views, become significant enough to cause a larger more important change – like an outbreak of measles.
“We believe our method could be very useful for central decision-makers,” said Chris Bauch, a professor in Waterloo’s Faculty of Mathematics and lead researcher on the study. “Provinces, states, and countries monitoring vaccine sentiment in their jurisdictions could use this method to identify patterns in social media data to help determine areas most likely to have a disease outbreak.
“Once they’ve identified populations that are exhibiting these signals, they can try to build trust and boost vaccine coverage in vaccine-hesitant members of those populations,” said Bauch.
Using a simulated social media network, the team found that there are two reliable early warning signals that precede a disease outbreak: dissimilar joint counts and mutual information.
“A dissimilar joint count is the number of instances of communication between, for example, pro-vaxxers and anti-vaxxers, which we found tends to increase prior to an outbreak,” said Brendon Phillips, a PhD candidate in Waterloo’s Department of Applied Mathematics and co-author of the study describing the new mothod. “Mutual informationmeasures the relationship between someone’s opinion and whether they’re sick or not,” said Phillips.
“We used computer simulations to examine how likely it is that someone who is an anti-vaxxer was infected in the past or that they’re susceptible to infection now.”
In developing the new method, the researchers created a simulated network of individuals with features common to many global childhood diseases, such as measles and chickenpox. They then used computer simulations to assess if a likely outbreak manifests in how often clusters of people who think in similar ways connect.
The researchers found that at this stage in its development, the method can show that a disease outbreak is likely but not exactly when it will come as that depends partly on chance events.
The study detailing this new method, Spatial early warning signals of social and epidemiological tipping points in a coupled behaviour-disease network authored by Phillips and Bauch and University of Guelph professor MadhurAnand was recently published in the journal Scientific Reports.
Question: We are living in a time of fake news, social media influencers, digital snake oil salespeople and a growing number of half -baked online medical advisors. How dangerous could some of these radical views be in a time of a pandemic as their seeds are sown across social media platforms?
Answer: These views can be literally deadly. In fact, a statistical analysis has shown that the statements of Brazil's president Bolsonaro downplaying the need for physical distancing have increased COVID-19 cases in Brazil. Brazil is experiencing one of the worst outbreaks of COVID-19 at the moment.
Question: Humans often believe that if they read it and it aligns with their way of thinking, then it must be true. When it comes to antibiotic-resistant maladies, we're beginning to see afflictions which we thought we had eradicated coming back for another go at the human race. There are reliable sources warning that perhaps there may be a health crisis involving some of these diseases in about 15 years. While social media may be a great way of mass communication and helpful in many aspects, could too much of the wrong information put us all in peril? Is it this powerful?
Answer: I think social media is powerful because it allows us to be more selective about where we get our information from, unlike traditional mass media like broadcast television. This has pros and cons. One of the cons is that we tend to ignore information that doesn’t confirm our existing ideas and opinions. As a result, I think we can become more closed-minded. In psychology, this phenomenon is called confirmation bias.
Below is the research Abstract and the Introduction to the full report. The link below goes to the amazingly detailed Scientific Report.
Spatial Early Warning Signals Of Social And Epidemiological Tipping Points In A Coupled Behaviour-disease Network
The resurgence of infectious diseases due to vaccine refusal has highlighted the role of interactions between disease dynamics and the spread of vaccine opinion on social networks. Shifts between disease elimination and outbreak regimes often occur through tipping points. It is known that tipping points can be predicted by early warning signals (EWS) based on characteristic dynamics near the critical transition, but the study of EWS in coupled behaviour-disease networks has received little attention. Here, we test several EWS indicators measuring spatial coherence and autocorrelation for their ability to predict a critical transition corresponding to disease outbreaks and vaccine refusal in a multiplex network model. The model couples paediatric infectious disease spread through a contact network to binary opinion dynamics of vaccine opinion on a social network. Through change point detection, we find that mutual information and join count indicators provided the best EWS. We also show the paediatric infectious disease natural history generates a discrepancy between population-level vaccine opinions and vaccine immunity status, such that transitions in the social network may occur before epidemiological transitions. These results suggest that monitoring social media for EWS of paediatric infectious disease outbreaks using these spatial indicators could be successful.
Resurgences of vaccine-preventable diseases severely stress public health systems, interrupt tourism and public services, and disrupt economies through the huge costs of large-scale interventions. These impacts motivate the study of factors that support vaccine uptake. Undervaccination may be attributed to vaccine refusal, the spread of anti-vaccine opinion facilitated by media coverage and its sensationalisation of true adverse vaccine effects3, the expectation of adverse effects, misstatement of the cause of illnesses, the spread of other rumours and false information, and the effect of social norms.
These phenomena show how the social diffusion of information is heavily responsible for the trajectory of disease spread through its ability to alter individual behaviour. Much work has modelled opinion dynamics for different applications through the use of voter models and majority opinion models, among other frameworks, and their combination with network structure has revealed much about the occurrence of opinion cascades and forecasting. For instance, models coupling behavioural dynamics and spreading processes change the predicted dynamics of influenza transmission and climate change alike.
Opinion propagation can be represented by information diffusion through social networks.
Chris T. Bauch, University of Waterloo
Brendon Phillips, University of Waterloo
Madhur Anand, Univeristy of Guelph