Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary
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RESEARCH ARTICLE
Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China Zhong‑Qi Li1†, Hong‑Qiu Pan2†, Qiao Liu1†, Huan Song1 and Jian‑Ming Wang1*
Abstract Background: 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. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predict‑ ing 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 autore‑ gressive 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. Results: Both 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. Conclusions: Our 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. Keywords: Pulmonary tuberculosis, Meteorological factor, Time series, Predicting
*Correspondence: [email protected] † Zhong-Qi Li, Hong-Qiu Pan and Qiao Liu contributed equally to this work 1 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave., Nanjing 211166, China Full list of author information is available at the end of the article
Background Tuberculosis (TB) is a chronic communicable disease that severely threatens human health, ranking among the top ten causes of death worldwide. The World Health Organization (WHO) estimated that approximately 10 million people fell ill with TB around the world in 2019. Furthermore, there were an estimated 1.2 million
© The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adap
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