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. 2024 Sep 13;24(1):2504.
doi: 10.1186/s12889-024-19994-6.

Spatial and temporal analysis and forecasting of TB reported incidence in western China

Daren Zhao  1 , Huiwu Zhang  2   3 , Xuelian Wu  4 , Lan Zhang  4 , Shiyuan Li  5 , Sizhang He  6
Affiliations

Spatial and temporal analysis and forecasting of TB reported incidence in western China

Daren Zhao et al. BMC Public Health. .

Abstract

Objective: Tuberculosis (TB) remains an important public health concern in western China. This study aimed to explore and analyze the spatial and temporal distribution characteristics of TB reported incidence in 12 provinces and municipalities in western China and to construct the optimal models for prediction, which would provide a reference for the prevention and control of TB and the optimization of related health policies.

Methods: We collected monthly data on TB reported incidence in 12 provinces and municipalities in western China and used ArcGIS software to analyze the spatial and temporal distribution characteristics of TB reported incidence. We applied the seasonal index method for the seasonal analysis of TB reported incidence and then established the SARIMA and Holt-Winters models for TB reported incidence in 12 provinces and municipalities in western China.

Results: The reported incidence of TB in 12 provinces and municipalities in western China showed apparent spatial clustering characteristics, and Moran's I was greater than 0 (p < 0.05) over 8 years during the reporting period. Among them, Tibet was the hotspot for TB incidence in 12 provinces and municipalities in western China. The reported incidence of TB in 12 provinces and municipalities in western China from 2004 to 2018 showed clear seasonal characteristics, with seasonal indices greater than 100% in both the first and second quarters. The optimal models constructed for TB reported incidence in 12 provinces and municipalities in western China all passed white noise test (p > 0.05).

Conclusions: As a hotspot of reported TB incidence, Tibet should continue to strengthen government leadership and policy support, explore TB intervention strategies and causes. The optimal prediction models we developed for reported TB incidence in 12 provinces and municipalities in western China were different.

Keywords: Forecasting; Holt-Winters; Reported incidence; SARIMA; Spatial and temporal analysis; Tuberculosis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The geolocation of the western region of China
Fig. 2
Fig. 2
Spatial autocorrelation LISA plots for 12 provinces and municipalities in western China (A 2004 B 2009 C 2013 D 2018). In order to describe the temporal and spatial changes and evolution of TB reported incidence in western China, we divided the entire time series (2004–2018) approximately equally into 4 spatial and temporal phases. The 4 years 2004, 2009, 2013, and 2018 were used to cut up the whole time series
Fig. 3
Fig. 3
Localized Getis-Ord Gi* maps for 12 provinces and municipalities in western China (A 2004 B 2009 C 2013 D 2018). In order to describe the temporal and spatial changes and evolution of TB reported incidence in western China, we divided the entire time series (2004–2018) approximately equally into 4 spatial and temporal phases. The 4 years 2004, 2009, 2013, and 2018 were used to cut up the whole time series
Fig. 4
Fig. 4
The prediction performance of optimal model on 12 provinces and municipalities in western China, the red line represents the observed value, the blue line represents the predicted value, and the purple dotted line represents the 95% confidence interval of the predicted value. A Inner Mongolia B Guangxi C Chongqing D Sichuan E Guizhou F Yunnan G Tibet H Shaanxi I Gansu J Qinghai K Ningxia L Xinjiang
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