A Deep Learning-Enhanced Compartmental Model and Its Application in Modeling Omicron in China.

ABSTRACT: The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, whose calculation depend upon detailed statistics on epidemiological characteristics, which are expensive, economically and resource-wise, to collect. In addition, infectious diseases spread in three dimensions: temporal, spatial, and mobile, i.e., they affect a population through not only the time progression of infection, but also the geographic distribution and physical mobility of the population. However, the parameterization process for the mainstream compartmental models does not effectively capture the spatial and mobile dimensions. As an alternative, deep learning techniques are utilized in estimating these stochastic parameters with greatly reduced

SUBMITTER: Deng Q 

PROVIDER: S-EPMC11428411 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature