Application of the ARIMA Model in Forecasting the Incidence of Tuberculosis in Anhui During COVID-19 Pandemic from 2021 to 2022

Abstract

Objective

Forecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources. In this study, we predict the incidence of pulmonary tuberculosis by establishing the autoregressive integrated moving average (ARIMA) model and providing support for pulmonary tuberculosis prevention and control during COVID-19 pandemic.

Methods

Registered tuberculosis(TB) cases from January 2013 to December 2020 in Anhui province were analysed using traditional descriptive epidemiological methods. Then we used the monthly incidence rate of TB from January 2013 through June 2020 to construct ARIMA model, and used the incidence rate from July 2020 to December 2020 to evaluate the forecasting accuracy. Ljung Box test, Akaike's information criterion(AICc), Bayesian information criterion(BIC) and Realtive error were used to evaluate the model fitting and forecasting effect, Finally, the optimal model was used to forecast the expected monthly incidence of tuberculosis for 2021 and 2022 to learn about the incidence trend.

Results

A total of 255,656 TB cases were registered. The reported rate of tuberculosis was highest in 2013 and lowest in 2020. The peak incidence was in March, Tongling (71.97/100,000), Chizhou (59.93/100,000), and Huainan (58.36/100,000) had the highest number of cases. The ratio of male to female incidence was 2.59:1, with the largest proportion of people being between 66 and 75 years old. The main occupation of patients was farmer. ARIMA (0, 1, 1) (0, 1, 1)₁₂ model was the optimal model to forecast the incidence trend of TB.

Conclusion

Tongling, Chizhou, and Huainan should strengthen measures for TB. In particular, the government should pay more attention on elderly people to prevent tuberculosis infections. The rate of TB patient registration and reporting has decreased under the pandemic of COVID-19. The ARIMA model can be a useful tool for predicting future TB cases.

Keywords:

• tuberculosis
• incidence
• epidemiological characteristics
• COVID-19
• time-series study
• ARIMA model

Data Sharing Statement

The data are not publicly available owing to privacy or ethical restrictions, as they contain sensitive information. The data are held by the Anhui Chest Hospital (Anhui Provincial Tuberculosis Institute). Requests to access the data can be sent to Xiao-Hong Kan ([email protected]), Chief of Scientific Research and Education at the Anhui Chest Hospital (Anhui Provincial Tuberculosis Institute).

Ethics Approval and Informed Consent

This study was approved by the Ethics Committee of Anhui Chest Hospital (K2020-011). Personal information of patients did not appear in this study; thus, informed consent was not needed.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no competing interests.

Additional information

Funding

This study was supported by grants from National Key Project for infectious Disease of China (2018ZX10722301-001-004) and Project of Anhui Provincial Health Commission (AHWJ2021b001). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.