Informed Random Forest to Model Associations of Epidemiological Priors, Government Policies, and Public Mobility.

ABSTRACT: Background. Infectious diseases constitute a significant concern worldwide due to their increasing prevalence, associated health risks, and the socioeconomic costs. Machine learning (ML) models and epidemic models formulated using deterministic differential equations are the most dominant tools for analyzing and modeling the transmission of infectious diseases. However, ML models can be inconsistent in extracting the dynamics of a disease in the presence of data drifts. Likewise, the capability of epidemic models is constrained to parameter dimensions and estimation. We aimed at creating a framework of informed ML that integrates a random forest (RF) with an adapted susceptible infectious recovered (SIR) model to account for accuracy and consistency in stochasticity within the dynam

SUBMITTER: Thapelo TS 

PROVIDER: S-EPMC10752195 | biostudies-literature | 2023 Jul-Dec

REPOSITORIES: biostudies-literature