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.

研究以无实际训练数据为基础，利用基于特征的时间序列分类方法准确预测疾病爆发和非爆发。通过对合成数据和真实数据集的测试，发现在爆发发生之前可以通过统计特征和早期预警信号指示器区分爆发和非爆发序列。