The idea behind adaptive behavioral epidemiology is that groups and individuals respond to the knowledge of a disease threat by changing their habits to avoid interactions with those who are contagious. Network-based models take this adaptive behavior into account by allowing the network to “rewire” its connections.
Experiments now quantitatively confirm the standard model of electrokinetics, in which electric fields drive the flow of electrolytes, potentially leading to better sensors and biomedical diagnostic devices.
Small nonequilibrium systems behave quite unexpectedly when in contact with a thermal reservoir. However, all of them—from molecular machines to molecular magnets—are described by a single fluctuation theorem.
Stochastic resonance, in which a periodic signal applied to a nonlinear system can be amplified by adding noise, has been observed in a mechanical system and predicted to occur in a Bose-Einstein condensate.
How the structural organization of a network evolves as it is observed on larger and larger scales remains an open question. Now, a general and systematic approach to answer this question may be in sight.
Given that vaccine supplies are often limited, a quantitative understanding of how the number and frequency of vaccinations can affect the growth rate of disease would be useful. Physicists show that even a small number of randomly vaccinated individuals can exponentially increase the extinction rate of a disease.