  Forecasting the occurrence and absence of novel disease outbreaks is
essential for disease management. Here, we develop a general model, with no
real-world training data, that accurately forecasts outbreaks and
non-outbreaks. We propose a novel framework, using a feature-based time series
classification method to forecast outbreaks and non-outbreaks. We tested our
methods on synthetic data from a Susceptible-Infected-Recovered model for
slowly changing, noisy disease dynamics. Outbreak sequences give a
transcritical bifurcation within a specified future time window, whereas
non-outbreak (null bifurcation) sequences do not. We identified incipient
differences in time series of infectives leading to future outbreaks and
non-outbreaks. These differences are reflected in 22 statistical features and 5
early warning signal indicators. Classifier performance, given by the area
under the receiver-operating curve, ranged from 0.99 for large expanding
windows of training data to 0.7 for small rolling windows. Real-world
performances of classifiers were tested on two empirical datasets, COVID-19
data from Singapore and SARS data from Hong Kong, with two classifiers
exhibiting high accuracy. In summary, we showed that there are statistical
features that distinguish outbreak and non-outbreak sequences long before
outbreaks occur. We could detect these differences in synthetic and real-world
data sets, well before potential outbreaks occur.
