An Optimal Control Approach to Learning in SIDARTHE Epidemic model
- URL: http://arxiv.org/abs/2010.14878v2
- Date: Thu, 28 Jan 2021 10:53:02 GMT
- Title: An Optimal Control Approach to Learning in SIDARTHE Epidemic model
- Authors: Andrea Zugarini, Enrico Meloni, Alessandro Betti, Andrea Panizza,
Marco Corneli, Marco Gori
- Abstract summary: We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data.
We forecast the epidemic evolution in Italy and France.
- Score: 67.22168759751541
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The COVID-19 outbreak has stimulated the interest in the proposal of novel
epidemiological models to predict the course of the epidemic so as to help
planning effective control strategies. In particular, in order to properly
interpret the available data, it has become clear that one must go beyond most
classic epidemiological models and consider models that, like the recently
proposed SIDARTHE, offer a richer description of the stages of infection. The
problem of learning the parameters of these models is of crucial importance
especially when assuming that they are time-variant, which further enriches
their effectiveness. In this paper we propose a general approach for learning
time-variant parameters of dynamic compartmental models from epidemic data. We
formulate the problem in terms of a functional risk that depends on the
learning variables through the solutions of a dynamic system. The resulting
variational problem is then solved by using a gradient flow on a suitable,
regularized functional. We forecast the epidemic evolution in Italy and France.
Results indicate that the model provides reliable and challenging predictions
over all available data as well as the fundamental role of the chosen strategy
on the time-variant parameters.
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