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基于时间序列分析的季节性自回归分数阶积分移动平均模型估计中国乙型和丙型肝炎流行情况。

Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China.

机构信息

Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan Province, China.

Beijing Key Laboratory of Antimicrobial Agents/Laboratory of Pharmacology, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China.

出版信息

World J Gastroenterol. 2023 Nov 14;29(42):5716-5727. doi: 10.3748/wjg.v29.i42.5716.

Abstract

BACKGROUND

Hepatitis B (HB) and hepatitis C (HC) place the largest burden in China, and a goal of eliminating them as a major public health threat by 2030 has been set. Making more informed and accurate forecasts of their spread is essential for developing effective strategies, heightening the requirement for early warning to deal with such a major public health threat.

AIM

To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average (SARFIMA) for projections into 2030, and to compare the effectiveness with the seasonal autoregressive integrated moving average (SARIMA).

METHODS

Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023. Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality. Two periods (from January 2004 to June 2022 and from January 2004 to December 2015, respectively) were used as the training sets to develop both models, while the remaining periods served as the test sets to evaluate the forecasting accuracy.

RESULTS

There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023. Overall, HB remained steady [average annual percentage change (AAPC) = 0.44, 95% confidence interval (95%CI): -0.94-1.84] while HC was increasing (AAPC = 8.91, 95%CI: 6.98-10.88), and both had a peak in March and a trough in February. In the 12-step-ahead HB forecast, the mean absolute deviation (15211.94), root mean square error (18762.94), mean absolute percentage error (0.17), mean error rate (0.15), and root mean square percentage error (0.25) under the best SARFIMA (3, 0, 0) (0, 0.449, 2) were smaller than those under the best SARIMA (3, 0, 0) (0, 1, 2) (16867.71, 20775.12, 0.19, 0.17, and 0.27, respectively). Similar results were also observed for the 90-step-ahead HB, 12-step-ahead HC, and 90-step-ahead HC forecasts. The predicted HB incidents totaled 9865400 (95%CI: 7508093-12222709) cases and HC totaled 1659485 (95%CI: 856681-2462290) cases during 2023-2030.

CONCLUSION

Under current interventions, China faces enormous challenges to eliminate HB and HC epidemics by 2030, and effective strategies must be reinforced. The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions, surpassing the capabilities of SARIMA.

摘要

背景

乙型肝炎(HB)和丙型肝炎(HC)在中国造成的负担最大,因此设定了到 2030 年消除其作为重大公共卫生威胁的目标。为了制定有效的策略,需要更准确地预测它们的传播情况,这就需要提前发出警报,以应对这一重大公共卫生威胁。

目的

通过设计典范季节性自回归分数阶积分移动平均(SARFIMA)模型来监测 HB 和 HC 流行情况,并预测到 2030 年的情况,同时与季节性自回归整合移动平均(SARIMA)进行比较。

方法

从 2004 年 1 月至 2023 年 6 月,我们在中国获得了乙型肝炎和丙型肝炎的每月发病数据。采用描述性分析和霍德里克-普雷斯科特法来识别趋势和季节性。将两个时期(分别为 2004 年 1 月至 2022 年 6 月和 2004 年 1 月至 2015 年 12 月)作为训练集来开发两种模型,而其余时期作为测试集来评估预测准确性。

结果

从 2004 年 1 月至 2023 年 6 月,HB 有 23400874 例,HC 有 3590867 例。总的来说,HB 保持稳定(年平均变化率(AAPC)=0.44,95%置信区间(95%CI):-0.94-1.84),而 HC 呈上升趋势(AAPC=8.91,95%CI:6.98-10.88),且两者均在 3 月达到高峰,2 月达到低谷。在 HB 的 12 步预测中,最佳 SARFIMA(3,0,0)(0,0.449,2)下的平均绝对偏差(15211.94)、均方根误差(18762.94)、平均绝对百分比误差(0.17)、平均误差率(0.15)和均方根百分比误差(0.25)均小于最佳 SARIMA(3,0,0)(0,1,2)(16867.71,20775.12,0.19,0.17 和 0.27)。对于 HB 的 90 步预测、HC 的 12 步预测和 HC 的 90 步预测,也观察到了类似的结果。2023-2030 年,预计 HB 发病数为 9865400 例(95%CI:7508093-12222709),HC 发病数为 1659485 例(95%CI:856681-2462290)。

结论

在当前的干预措施下,中国在 2030 年前消除 HB 和 HC 流行面临巨大挑战,必须加强有效的策略。SARFIMA 纳入公共卫生管理 HB 和 HC 流行,可以更准确地预测疫情,提供更高效的干预措施,这可能优于 SARIMA。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34e/10701333/74d2b7aac982/WJG-29-5716-g001.jpg

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