Chen Shuangshuang, Wang Xinqiang, Zhao Jiawen, Zhang Yongzhong, Kan Xiaohong
Department of Scientific Research and Education, Anhui Chest Hospital (Anhui Provincial Tuberculosis Institute), Hefei, People's Republic of China.
Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, People's Republic of China.
Infect Drug Resist. 2022 Jul 4;15:3503-3512. doi: 10.2147/IDR.S367528. eCollection 2022.
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.
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.
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) model was the optimal model to forecast the incidence trend of TB.
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.
预测肺结核的季节性和趋势对于合理分配卫生资源很重要。在本研究中，我们通过建立自回归积分滑动平均（ARIMA）模型来预测肺结核的发病率，并为COVID-19大流行期间的肺结核预防和控制提供支持。
采用传统描述性流行病学方法分析2013年1月至2020年12月安徽省登记的结核病病例。然后我们使用2013年1月至2020年6月的结核病月发病率构建ARIMA模型，并使用2020年7月至12月的发病率评估预测准确性。使用Ljung Box检验、赤池信息准则（AICc）、贝叶斯信息准则（BIC）和相对误差来评估模型拟合和预测效果，最后，使用最优模型预测2021年和2022年结核病的预期月发病率，以了解发病趋势。
共登记255,656例结核病病例。结核病报告率在2013年最高，2020年最低。发病高峰在3月，铜陵（71.97/10万）、池州（59.93/10万）和淮南（58.36/10万）的病例数最多。男女发病率之比为2.59:1，66至75岁的人群比例最大。患者的主要职业是农民。ARIMA(0, 1, 1)(0, 1, 1)模型是预测结核病发病趋势的最优模型。
铜陵、池州和淮南应加强结核病防治措施。特别是，政府应更加关注老年人，以预防结核病感染。在COVID-19大流行期间，结核病患者登记和报告率有所下降。ARIMA模型可作为预测未来结核病病例的有用工具。
