Applying SARIMA, ETS, and hybrid models for prediction of tuberculosis incidence rate in Taiwan.

ABSTRACT:

Background

Tuberculosis (TB) remained one of the world's most deadly chronic communicable diseases. Future TB incidence prediction is a benefit for intervention options and resource-allocation planning. We aimed to develop rapid univariate prediction models for epidemics forecasting employment.

Methods

The surveillance data regarding Taiwan monthly TB incidence rates which from January 2005 to June 2017 were utilized for simulation modelling and from July 2017 to December 2020 for model validation. The modeling approaches including the Seasonal Autoregressive Integrated Moving Average (SARIMA), the Exponential Smoothing (ETS), and SARIMA-ETS hybrid algorithms were constructed and compared. The modeling performance of in-sample simulating training sets and pseudo-out-of-sampl

SUBMITTER: Kuan MM 

PROVIDER: S-EPMC9508881 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature