Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China.

ABSTRACT:

Background

Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB.

Methods

We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, w

SUBMITTER: Li ZQ 

PROVIDER: S-EPMC7641658 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature