تقرير
A Bayesian generative neural network framework for epidemic inference problems
العنوان: | A Bayesian generative neural network framework for epidemic inference problems |
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المؤلفون: | Biazzo, Indaco, Braunstein, Alfredo, Dall'Asta, Luca, Mazza, Fabio |
المصدر: | Sci Rep 12, 19673 (2022) |
سنة النشر: | 2021 |
المجموعة: | Computer Science Condensed Matter |
مصطلحات موضوعية: | Computer Science - Social and Information Networks, Condensed Matter - Statistical Mechanics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
الوصف: | The reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies. The identification and warning of infectious but asymptomatic individuals (i.e., contact tracing), the well-known patient-zero problem, or the inference of the infectivity values in structured populations are examples of significant epidemic inference problems. As the number of possible epidemic cascades grows exponentially with the number of individuals involved and only an almost negligible subset of them is compatible with the observations (e.g., medical tests), epidemic inference in contact networks poses incredible computational challenges. We present a new generative neural networks framework that learns to generate the most probable infection cascades compatible with observations. The proposed method achieves better (in some cases, significantly better) or comparable results with existing methods in all problems considered both in synthetic and real contact networks. Given its generality, clear Bayesian and variational nature, the presented framework paves the way to solve fundamental inference epidemic problems with high precision in small and medium-sized real case scenarios such as the spread of infections in workplaces and hospitals. Comment: 30 pages, 6 figures, 1 table |
نوع الوثيقة: | Working Paper |
DOI: | 10.1038/s41598-022-20898-x |
URL الوصول: | http://arxiv.org/abs/2111.03383 |
رقم الأكسشن: | edsarx.2111.03383 |
قاعدة البيانات: | arXiv |
DOI: | 10.1038/s41598-022-20898-x |
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