The previous low prevalence of preventable diseases has led some people to ignore health risks, instead focusing on all the imagined dangers of vaccination. Falling vaccination rates, driven by social interaction, then leave a greater proportion of the population susceptible to infection. These are perfect conditions for an epidemic, as seen by the recent resurgences of measles, mumps, whooping cough, polio, rubella and others illnesses. Also of interest are the effects of "echo chambers", occurring when people of similar opinion form social bonds. This create insular groups where interaction reinforces convictions, rather than challenging them.
Viewing disease spread as a dynamical system, epidemics represent critical transitions. Sometimes, these shifts are preceded (or accompanied) by easily recognisable characteristic behaviours, called early warning signals. Investigating and identifying a class of these signals is crucial to mitigating the global economic and infrastructural damage caused by disease resurgence. Surprisingly, to date, there has been no research done on finding early warning signals in coupled systems.
Simulation of a disease process has shown that critical transitions were preceded by sharp increases in the mutual dependence of physical and social dynamics. Also, both spatial autocorrelation and the number of connected graph components peak at the transition point, with spatial autocorrelation giving false warning signals under certain conditions.
In this talk, we will further discuss these results and the construction of model, as well as the measurements chosen and the reliability of the resulting signals.
This talk, which is accessible to a general CS audience, places Google's
results into context. We will cover the nature of the experiment, the
machine learning techniques used in the paper (convolutional and
adversarial neural networks), the results, and how they relate to modern
cryptography. We discuss why, contrary to much of the reporting on this
story, this approach is fundamentally flawed in terms of creating secure
communication methods, as the original authors acknowledge. Finally, we
point out some legitimate takeaways from the results.