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

Abstract

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.

Identification and Prediction of Tuberculosis in Eastern China: Analyses from 10-year Population-based Notification Data in Zhejiang Province, China

“…Comparing our predictions with actual notified TB number from the TBIMS system in 2019 demonstrated the accuracy in our model's fit. Previous studies in other regions also used the SARIMA model to give a short-term prediction of TB epidemics with high predictive precision, which was consistent with our findings 46,47 .…”

Comparative Analysis of Three Predictive Models of Performance Indicators with Results-Based Management: Cancer Data Statistics in a National Institute of Health

“…For nonstationary time series data, the mean and variance are unstable, and they are generally converted to stationary time series by means of a differential operation. Then, stationary time series data are used to establish the ARMA mode [ 58 ]. Finally, ES was tested as the easiest forecasting technique and demonstrated that it can be used by businesses of all kinds [ 17 ].…”

“…Figure 1 shows the ARIMA(p,d,q) model; for nonstationary time series data, the mean and variance are unstable, and such data are generally converted to a stationary time series first by means of a differential operation [58]. The ARIMA model integrates autoregressive and moving average calculations.…”

Time Series Modeling of Tuberculosis Cases in India from 2017 to 2022 Based on the SARIMA-NNAR Hybrid Model

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