Early detection of disease outbreaks and non-outbreaks using incidence data
<|reference_start|>Early detection of disease outbreaks and non-outbreaks using incidence data: 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.<|reference_end|>
@article{gao2024early,
    title={Early detection of disease outbreaks and non-outbreaks using incidence
  data},
    author={Shan Gao, Amit K. Chakraborty, Russell Greiner, Mark A. Lewis, and Hao
  Wang},
    journal={arXiv preprint arXiv:2404.08893},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08893},
    primaryClass={cs.LG math.DS q-bio.PE stat.AP}
}