A Physics-Informed Neural Network approach for compartmental epidemiological models.

ABSTRACT: Compartmental models provide simple and efficient tools to analyze the relevant transmission processes during an outbreak, to produce short-term forecasts or transmission scenarios, and to assess the impact of vaccination campaigns. However, their calibration is not straightforward, since many factors contribute to the rapid change of the transmission dynamics. For example, there might be changes in the individual awareness, the imposition of non-pharmacological interventions and the emergence of new variants. As a consequence, model parameters such as the transmission rate are doomed to vary in time, making their assessment more challenging. Here, we propose to use Physics-Informed Neural Networks (PINNs) to track the temporal changes in the model parameters and the state variables. PINNs

SUBMITTER: Millevoi C 

PROVIDER: S-EPMC11407682 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature