State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Brussels, Belgium.
PLoS One. 2021 Jun 9;16(6):e0252803. doi: 10.1371/journal.pone.0252803. eCollection 2021.
A variety of infectious diseases occur in mainland China every year. Cyclic oscillation is a widespread attribute of most viral human infections. Understanding the outbreak cycle of infectious diseases can be conducive for public health management and disease surveillance. In this study, we collected time-series data for 23 class B notifiable infectious diseases from 2004 to 2020 using public datasets from the National Health Commission of China. Oscillatory properties were explored using power spectrum analysis. We found that the 23 class B diseases from the dataset have obvious oscillatory patterns (seasonal or sporadic), which could be divided into three categories according to their oscillatory power in different frequencies each year. These diseases were found to have different preferred outbreak months and infection selectivity. Diseases that break out in autumn and winter are more selective. Furthermore, we calculated the oscillation power and the average number of infected cases of all 23 diseases in the first eight years (2004 to 2012) and the next eight years (2012 to 2020) since the update of the surveillance system. A strong positive correlation was found between the change of oscillation power and the change in the number of infected cases, which was consistent with the simulation results using a conceptual hybrid model. The establishment of reliable and effective analytical methods contributes to a better understanding of infectious diseases' oscillation cycle characteristics. Our research has certain guiding significance for the effective prevention and control of class B infectious diseases.
中国大陆每年都会发生多种传染病。周期性波动是大多数人类病毒感染的普遍特征。了解传染病的爆发周期有助于公共卫生管理和疾病监测。本研究使用中国国家卫生健康委员会公开数据集，收集了 2004 年至 2020 年 23 种乙类法定传染病的时间序列数据。使用功率谱分析探索了波动特性。我们发现，数据集的 23 种乙类疾病具有明显的波动模式（季节性或散发性），根据每年不同频率的波动功率可分为三类。这些疾病具有不同的首选爆发月份和感染选择性。秋冬季节爆发的疾病更具选择性。此外，我们计算了自监测系统更新以来的前 8 年（2004 年至 2012 年）和后 8 年（2012 年至 2020 年）所有 23 种疾病的波动功率和平均感染病例数。发现波动功率的变化与感染病例数的变化之间存在很强的正相关，这与使用概念混合模型进行的模拟结果一致。建立可靠有效的分析方法有助于更好地了解传染病的波动周期特征。本研究对乙类传染病的有效预防和控制具有一定的指导意义。
