Deep neural networks for endemic measles dynamics: Comparative analysis and integration with mechanistic models.

ABSTRACT: Measles is an important infectious disease system both for its burden on public health and as an opportunity for studying nonlinear spatio-temporal disease dynamics. Traditional mechanistic models often struggle to fully capture the complex nonlinear spatio-temporal dynamics inherent in measles outbreaks. In this paper, we first develop a high-dimensional feed-forward neural network model with spatial features (SFNN) to forecast endemic measles outbreaks and systematically compare its predictive power with that of a classical mechanistic model (TSIR). We illustrate the utility of our model using England and Wales measles data from 1944-1965. These data present multiple modeling challenges due to the interplay between metapopulations, seasonal trends, and nonlinear dynamics related to demog

SUBMITTER: Madden WG 

PROVIDER: S-EPMC11620694 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature