Abstract
BackgroundMany 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.MethodsWe 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, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018.ResultsBoth the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively.ConclusionsOur study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.
Highlights
Many studies have compared the performance of time series models in predicting pulmonary tuber‐ culosis (PTB), but few have considered the role of meteorological factors in their prediction models
Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMA with exogenous variables (ARIMAX) model was superior to the autoregressive integrated moving average (ARIMA) and recurrent neural network (RNN) models in study settings
We initially identified the parameters of the ARIMA model (p, q, P, and Q) to construct alternative models for each city according to the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of the stationary series (Additional file 1: Figure S1, a1–a3, and b1–b3)
Summary
Many studies have compared the performance of time series models in predicting pulmonary tuber‐ culosis (PTB), but few have considered the role of meteorological factors in their prediction models. Time series analysis plays a vital role in predicting trends by identifying the way in which health-related events change with time. The ARIMA with exogenous variables (ARIMAX) model exhibits superior prediction performance by adding other event-related factors as input variables. Another commonly used time series analysis model is based on an artificial neural network (ANN), which is designed to simulate the way the human brain analyzes and processes information. The ANN has been applied to construct time series models to forecast human diseases [7, 8]. The ability to model temporal dependencies makes it appropriate to analyze a time series, which consists of a sequence of points that are not independent [9, 10]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
