Division of tuberculosis control and prevention, Yunnan Center for Disease Control and Prevention, Kunming, China.
BMC Infect Dis. 2024 Aug 16;24(1):835. doi: 10.1186/s12879-024-09740-z.
Rifampicin resistant tuberculosis (RR-TB) poses a growing threat to individuals and communities. This study utilized a seasonal autoregressive integrated moving average (SARIMA) model to quantitatively predict the monthly incidence of RR-TB in Yunnan Province which could guide government health administration departments and the centers for disease control and prevention (CDC) in preventing and controlling the RR-TB epidemic.
The study utilized routine surveillance reporting data from the infectious Disease Network Surveillance and Reporting System. Monthly incidence rates of RR-TB were collected from January 2019 to December 2022. A time series SARIMA model was used to predict the number of monthly RR-TB cases in Yunnan Province in 2023, and the model was validated using time series plots, seasonal and non-seasonal differencing, autocorrelation and partial autocorrelation analysis, and white noise tests.
From 2019 to 2022, the incidence of RR-TB decreases as the incidence of all TB decreases (P < 0.05). There was no significant change in the proportion of RR-TB among all TB cases, which remained within 2.5% (P>0.05). The time series decomposition shows that it presented obvious seasonality, periodicity and randomness after being decomposed. Time series analysis was performed on the original series after 1 non-seasonal difference and 1 seasonal difference, the ADF test showed P < 0.05. According to ACF and PACF, the SARIMA (1, 1, 1) (1, 1, 0) model was chosen and statistically significant model parameter estimates (P < 0.05). The predicted seasonal trend of RR-TB incidence in 2019 to 2023 was similar to the actual data. The percentage accuracy in the prediction excesses 80% in 2019 to 2022 and is all within 95% CI. However there was a certain gap between the actual incidence and the predicted value in 2023, and the acutual incidence had increased by 12.4% compared to 2022. The percentage of accuracy in the prediction was only 70% in 2023.
We found the incidence of RR-TB was based on that of all TB in Yunnan. The SARIMA model successfully predicted the seasonal incidence trend of RR-TB in Yunnan Province in 2019 to 2023, but the prediction precision could be influenced by factors such as new infectious disease outbreaks or pandemics, social issues, environmental challenges or other unknown risks. Hence CDCs should pay special attention to the post epidemic effects of new infectious disease outbreaks or pandemics, carry out monitoring and early warning, and better optimize disease prediction models.
利福平耐药结核病（RR-TB）对个人和社区构成的威胁日益严重。本研究采用季节性自回归综合移动平均（SARIMA）模型对云南省 RR-TB 的每月发病率进行定量预测，为政府卫生行政部门和疾病预防控制中心（CDC）防控 RR-TB 疫情提供指导。
本研究利用传染病网络监测报告系统的常规监测报告数据。收集 2019 年 1 月至 2022 年 12 月 RR-TB 的月发病率数据。采用时间序列 SARIMA 模型预测云南省 2023 年 RR-TB 的月发病数，并通过时间序列图、季节性和非季节性差分、自相关和偏自相关分析以及白噪声检验对模型进行验证。
2019 年至 2022 年，RR-TB 的发病率随着所有结核病发病率的下降而下降（P<0.05）。RR-TB 在所有结核病病例中的比例无显著变化，均在 2.5%以内（P>0.05）。时间序列分解表明，分解后呈现出明显的季节性、周期性和随机性。对原始序列进行 1 次非季节性差分和 1 次季节性差分后进行时间序列分析，ADF 检验显示 P<0.05。根据 ACF 和 PACF，选择 SARIMA（1,1,1）（1,1,0）模型，且模型参数估计具有统计学意义（P<0.05）。RR-TB 发病率的预测季节性趋势与实际数据相似。2019 年至 2022 年的预测超量准确率超过 80%，且均在 95%CI 内。然而，2023 年实际发病率与预测值存在一定差距，与 2022 年相比，实际发病率增加了 12.4%。2023 年的预测准确率仅为 70%。
我们发现云南省 RR-TB 的发病率与所有结核病的发病率有关。SARIMA 模型成功预测了 2019 年至 2023 年云南省 RR-TB 的季节性发病趋势，但预测精度可能受到新发传染病爆发或大流行、社会问题、环境挑战或其他未知风险等因素的影响。因此，CDC 应特别关注新发传染病爆发或大流行的疫情后影响，开展监测和预警，并更好地优化疾病预测模型。
