Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model.

ABSTRACT:

Background

Forecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy.

Methods

COVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Mu

SUBMITTER: McClymont H 

PROVIDER: S-EPMC9941072 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature