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 […]
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 […]
One of the key research products of Project AERO is the R package spaero (pronounced “sparrow”) for estimating distributional properties along rolling windows of time series. Such estimates may in some cases provide signals that the system generating the data is approaching a regime shift. Regime shifts are marked changes in the dynamics of a system that may sometimes be modeled […]
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 […]