We propose a robust parameter estimation method for dynamical systems based on Statistical Learning techniques which aims to estimate a set of parameters that well fit the dynamics in order to obtain robust evidences about the qualitative behaviour of its trajectory. The method is quite general and flexible, since it dos not rely on any specific property of the dynamical system, and represents a mathematical formalisation of the procedure consisting of sampling and testing parameters, in which evolutions generated by candidate parameters are tested against observed data to assess goodness-of-fit. The Statistical Learningframework introduces a mathematically rigorous scheme to this general approach for parameter estimation, adding to the great field of parameter estimation in dynamical systems . The method is specially useful for estimating parameters in epidemiological compartmental models . We illustrate it in simulated and real data about COVID-19 spread in the US in order to assess qualitatively the peak of deaths by the disease .
We propose a robust parameter estimation method for dynamical systems based on Statistical Learning techniques which aims to estimate a set of parameters that well fit the dynamics in order to obtain robust evidences about the qualitative behaviour of its trajectory. The method is quite general and flexible, since it does not rely on any specific property of the dynamical system, and represents a reinterpretation of Approximate Bayesian Computation methods through the lens of Statistical Learning . The method is specially useful for estimating parameters in epidemiological compartmental models in order to obtain qualitative properties of a disease evolution. We apply it to simulated and real data about COVID-19 spread in the US in order to evaluate qualitatively its evolution over time, showing how one may assess the effectiveness of measures implemented to slow the spread and some qualitative features of the disease current and future evolution .
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mathematical formalisation of the procedure consisting of sampling and testing parameters, in which evolutions generated by candidate parameters are tested against observed data to assess goodness-of-fit. The Statistical Learningframework introduces a mathematically rigorous scheme to this general approach for parameter estimation, adding to the great field of parameter estimation in dynamical systems
reinterpretation of Approximate Bayesian Computation methods through the lens of Statistical Learning
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. We illustrate it in
in order to obtain qualitative properties of a disease evolution. We apply it to
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assess qualitatively the peak of deaths by the disease
evaluate qualitatively its evolution over time, showing how one may assess the effectiveness of measures implemented to slow the spread and some qualitative features of the disease current and future evolution