Vector Autoregression for Forecasting the Number of COVID-19 Cases and Analyzing Behavioral Indicators in the Philippines: Ecologic Time-Trend Study.

ABSTRACT:

Background

Traditional surveillance systems rely on routine collection of data. The inherent delay in retrieval and analysis of data leads to reactionary rather than preventive measures. Forecasting and analysis of behavior-related data can supplement the information from traditional surveillance systems.

Objective

We assessed the use of behavioral indicators, such as the general public's interest in the risk of contracting SARS-CoV-2 and changes in their mobility, in building a vector autoregression model for forecasting and analysis of the relationships of these indicators with the number of COVID-19 cases in the National Capital Region.

Methods

An etiologic, time-trend, ecologic study design was used to forecast the daily number of cases in 3 periods during the res

SUBMITTER: Latorre AAE 

PROVIDER: S-EPMC10337462 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature