تقرير
Early detection of disease outbreaks and non-outbreaks using incidence data
| العنوان: | Early detection of disease outbreaks and non-outbreaks using incidence data |
|---|---|
| المؤلفون: | Gao, Shan, Chakraborty, Amit K., Greiner, Russell, Lewis, Mark A., Wang, Hao |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics Quantitative Biology Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Mathematics - Dynamical Systems, Quantitative Biology - Populations and Evolution, Statistics - Applications |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.08893 |
| رقم الأكسشن: | edsarx.2404.08893 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |