From Policy to Prediction: Forecasting COVID-19 Dynamics Under Imperfect Vaccination.

ABSTRACT: Understanding the joint impact of vaccination and non-pharmaceutical interventions on COVID-19 development is important for making public health decisions that control the pandemic. Recently, we created a method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model (Wang et al. in Bull Math Biol 84:57, 2022). In this paper, we extend the method to the post-vaccination period, accordingly obtain a retrospective forecast of COVID-19 daily confirmed cases in the US, and identify the relative influence of the policies used

SUBMITTER: Wang X 

PROVIDER: S-EPMC9297284 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature