Epidemic inference through generative neural networks
- URL: http://arxiv.org/abs/2111.03383v2
- Date: Mon, 8 Nov 2021 09:47:35 GMT
- Title: Epidemic inference through generative neural networks
- Authors: Indaco Biazzo, Alfredo Braunstein, Luca Dall'Asta, Fabio Mazza
- Abstract summary: We present a new generative neural networks framework that can sample the most probable infection cascades compatible with observations.
The framework can infer the parameters governing the spreading of infections.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reconstructing missing information in epidemic spreading on contact networks
can be essential in prevention and containment strategies. For instance,
identifying and warning infective but asymptomatic individuals (e.g., manual
contact tracing) helped contain outbreaks in the COVID-19 pandemic. The number
of possible epidemic cascades typically grows exponentially with the number of
individuals involved. The challenge posed by inference problems in the
epidemics processes originates from the difficulty of identifying the almost
negligible subset of those compatible with the evidence (for instance, medical
tests). Here we present a new generative neural networks framework that can
sample the most probable infection cascades compatible with observations.
Moreover, the framework can infer the parameters governing the spreading of
infections. The proposed method obtains better or comparable results with
existing methods on the patient zero problem, risk assessment, and inference of
infectious parameters in synthetic and real case scenarios like spreading
infections in workplaces and hospitals.
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