Effect of COVID-19 non-pharmaceutical interventions on the epidemic trends of scarlet fever in China: A population-based surveillance and modeling study.

IF 4.8 2区医学 Q1 INFECTIOUS DISEASES

International Journal of Infectious Diseases Pub Date : 2025-06-27 DOI:10.1016/j.ijid.2025.107969

Xiaojuan Zhang, Yuanhai You, Jie Liu, Lu Sun, Haijian Zhou, Xingxing Zhang, Bike Zhang

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引用次数: 0

Abstract

Background: This study analyzed the epidemiological patterns of scarlet fever, and any changes therein, before, during, and after the COVID-19 pandemic in China and provided new perspectives for optimizing prevention and control strategies.

Methods: Data for clinically diagnosed and laboratory-confirmed cases between January 1, 2005, and December 31, 2024, were collected from the National Notifiable Infectious Disease Surveillance System. Descriptive analysis was used to summarize the characteristics in pre-COVID-19 (2005-2019), during COVID-19 (2020-2022), and post-COVID-19 (2023-2024) periods. Dynamic changes in distribution pattern were explored through spatial autocorrelation analysis. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to evaluate impact of non-pharmaceutical interventions (NPIs) on disease.

Findings: During 2005-2024, 876,680 cases were reported (crude annual incidence: 3.22/100 000). The annual morbidity rates for three periods were 3.56, 1.58, and 3.25/100 000. Significant differences were observed among the periods (P < 0.001). The actual cases during COVID-19 period decreased by 78.43% compared to the SARIMA model predictions. Significant geography-based clustering of cases was identified.

Interpretation: It demonstrated exceptional impacts of NPIs on the epidemic trends and high-risk regions of scarlet fever in China. Hence, tight surveillance programs are needed to protect populations against future pandemics.

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COVID-19非药物干预对中国猩红热流行趋势的影响：基于人群的监测和建模研究

背景：分析2019冠状病毒病（COVID-19）大流行前、中、后中国猩红热流行特征及其变化，为优化防控策略提供新的视角。方法：收集2005年1月1日至2024年12月31日国家法定传染病监测系统的临床诊断病例和实验室确诊病例资料。采用描述性分析方法，总结了疫情前期（2005-2019年）、疫情期间（2020-2022年）和疫情后（2023-2024年）的特征。通过空间自相关分析，探索其分布格局的动态变化。建立了季节性自回归综合移动平均（SARIMA）模型来评估非药物干预对疾病的影响。结果：2005-2024年共报告876680例（粗年发病率：3.22/10万）。3个时期的年发病率分别为3.56 /10万、1.58 /10万和3.25/10万。各时期间差异有统计学意义（P < 0.001）。与SARIMA模型预测相比，新冠肺炎期间的实际病例减少了78.43%。确定了显著的基于地理的病例聚类。解释：这证明了国家免疫倡议对中国猩红热流行趋势和高危地区的特殊影响。因此，需要严格的监测计划来保护人群免受未来流行病的侵害。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Infectious Diseases 医学-传染病学

CiteScore

18.90

自引率

2.40%

发文量

1020

审稿时长

30 days

期刊介绍： International Journal of Infectious Diseases (IJID) Publisher: International Society for Infectious Diseases Publication Frequency: Monthly Type: Peer-reviewed, Open Access Scope: Publishes original clinical and laboratory-based research. Reports clinical trials, reviews, and some case reports. Focuses on epidemiology, clinical diagnosis, treatment, and control of infectious diseases. Emphasizes diseases common in under-resourced countries.