PROJECT AERO – Forecasting critical transitions in emerging and re-emerging infectious diseases

The statistical physics of epidemic transitions

The prevalence of a pathogen emerging in a population varies in a complex manner that is difficult to model and predict, due in part to randomness in the system. In a new paper in PLOS Computation Biology, we show that if the random component is relatively small, the underlying dynamics can be understood with the […]

Disentangling reporting and disease transmission

Second order statistics such as variance and autocorrelation can in principle provide early warning of disease (re-)emergence. Such statistics can detect the approach to an epidemic threshold, a point beyond which a major outbreak (or epidemic) becomes possible. However, changes in disease reporting probabilities can produce the same trends in the early warning statistics as changes in […]

Anticipating Outbreaks with Imperfect Data

Anticipating disease emergence is a challenge with clear public health ramifications. Theoretical studies have already demonstrated that epidemic transitions are in principle preceded by detectable temporal trends in statistics (early-warning signals). We investigated the robustness of these early-warning signals under simulated realistic disease reporting scenarios, testing the effects of case reporting error, reporting probability, and […]

Goal. Project AERO (Anticipating Emerging and Re-emerging Outbreaks) is a collaboration involving researchers at the Universities of Georgia, Michigan, and Penn State and a part of the MIDAS Network. The goal of the project is to develop new methods for forecasting emergence and re-emergence of infectious diseases based on the theory of critical slowing down.

What’s critical slowing down? When low-dimensional dynamical systems approach a tipping point, they often exhibit characteristic patterns in data that reflect the loss of the tendency to return to equilibrium. Contagion processes often act like low-dimensional dynamical systems and emergence/re-emergence occur at tipping points. Putting these together, we thought it made sense to see if disease emergence is preceded by critical slowing down.

Does it really work? We don’t know yet. Early results are promising. We’re building a team to test just how far we can go in providing early warnings of outbreaks and indicators of the effectiveness of control.

What else might this theory be useful for? Lots of things. Another application of critical slowing down in the field of public health is the development of “end game” strategies and monitoring for disease elimination.