Seasonal characteristics and virus strain variation of influenza epidemics in Guangzhou city, 2015 – 2022: seasonal-trend decomposition-based analysis
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摘要:
目的 分析广东省广州市流行性感冒(流感)病毒季节性流行特征和各病原型别的变迁规律,为制定适宜本地流行病学的防控措施提供依据。 方法 选取中国流感监测信息系统中2015 — 2022年广州市每周流感病原学监测数据,利用基于局部加权回归的季节性分解将流感病毒总阳性率及各型别阳性率序列分解成趋势分量、季节分量及剩余分量,通过计算协方差,分析所得分量序列对阳性率序列波动的贡献率。 结果 共检测样本39198份,流感病毒核酸阳性3632份,总体阳性率9.27%。趋势序列显示2015 — 2019年总阳性率呈轻微波动上升(4.57%~16.07%),2020 — 2022年经历“U”形低谷后趋水平。季节性序列显示流行高峰有冬春季和春夏季。剩余序列显示2022年剩余分量波动峰值达48.96%,较以往年份有明显增大。A(H3N2)型流行峰型与B(Victoria)型相似,可有冬季和夏季高峰,但夏季高峰晚于B(Victoria)型。A(H1N1) pdm09型与B(Yamagata)型峰型和流行时间相似,呈单峰流行,集中在冬春季。剩余成分是总阳性率波动的主要贡献因素,占59.16%(80.81/136.60),其次是季节成分和趋势成分,分别占25.19%(34.41/136.60)和15.65%(21.38/136.60)。 结论 流感季节性高峰和持续时间与流行的病原型别有关,不同亚型流感具有型别特异的流行特征和变化趋势。流感流行模式不仅受长期趋势和季节效应影响,随机波动效应起着更重要作用。 Abstract:Objective To study prevalence characteristics and change pattern of dominant virus strain of influenza epidemics in Guangzhou city and to provide evidence for formulating region-specific prevention and control measures. Methods Weekly influenza surveillance data of Guangzhou city, Guangdong province from 2015 to 2022 were collected from China Influenza Surveillance Information System. The positive rate series of influenza virus and its pathogen subtype were decomposed into trend/season/remainder component by using the seasonal-trend decomposition procedure based on locally weighted regression. By calculating covariance, the contribution rate of the obtained components to the fluctuation of positive rate was analyzed. Results Of 39 198 surveillance specimens sampled in the city during the 8-year period, 3 632 (9.27%) were positive for influenza virus nucleic acid. Trend series analysis showed that the positive rate fluctuated from 4.57% to 16.07%, with an increasing trend, during 2015 – 2019 and varied with a U-shape trajectory during 2020 – 2022. Winter-spring and spring-summer peaks of the positive rate were observed in season series analysis and the remainder component analysis revealed the highest fluctuation range of 48.96% for the positive rate in 2022. The positive rate of influenza A (H3N2) presented a similar season series to that of influenza B (Victoria), with winter and summer peaks, but the summer peaks of influenza A (H3N2) was later than that of influenza B (Victoria); the peak shape and prevalence duration (single peaks mainly in winter-spring season) of influenza A (H1N1) pdm09 positivity were similar to those of influenza B (Yamagata). In general, the remainder component was the main contributor to the fluctuation of the total positive rate, accounting for 59.16% of overall fluctuation (covariance of remainder component versus that of original series: 80.81/136.60), followed by season and trend component, accounting for 25.19% (34.41/136.60) and 15.68% (21.38/136.60) of overall fluctuation, respectively. Conclusion There were virus subtype-specific differences in seasonal peak and prevalence duration of influenza infection and random fluctuation exerted a greater effect than long-term trend and seasonal variation on influenza epidemic pattern in Guangzhou city. -
Key words:
- influenza /
- subtype /
- seasonal-trend decomposition /
- time series
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表 1 2015 — 2022年广州市流感样病例病流感病毒核酸检测情况
Table 1. Year-specific nucleic acid positivity by influenza virus types among 39 198 influenza-like illness cases in Guangzhou city, 2015 – 2022
年份 检测数 总阳性数 总阳性率(%) 病原型别 A(H1N1)pdm09 A(H3N2) B(Victoria) B(Yamagata) 2015 3574 308 8.62 14 110 5 179 2016 3559 433 12.17 233 44 122 34 2017 4776 573 12.00 127 367 31 48 2018 5225 603 11.54
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455 193 2019 5300 807 15.23 389 162 256 0 2020 6661 176 2.64 55 117 4 0 2021 4888 115 2.35 0 0 115 0 2022 5215 617 11.83 0 409 208 0 合计 39198 3632 9.27 1169 1213 796 454 表 2 新冠疫情前后各分量对流感病毒总阳性率波动的贡献率
Table 2. Contribution of trend, seasonal and remaining component to overall fluctuation of influenza virus nucleic acid positivity among 39 198 influenza-like illness cases before and after COVID-19 epidemic in Guangzhou city
年度 原始序列Cov$({{Y}}_{{t}},{{Y}}_{{t}})$ 趋势分量 季节分量 剩余分量 $\mathrm{C}\mathrm{o}\mathrm{v}({{T} }_{{t} },{{Y} }_{{t} })$ % $\mathrm{C}\mathrm{o}\mathrm{v}\left({{S} }_{{t} },{{Y} }_{{t} }\right)$ % $\mathrm{C}\mathrm{o}\mathrm{v}({{R} }_{{t} },{{Y} }_{{t} })$ % 2015 — 2019 126.21 7.31 5.79 40.73 32.27 78.17 61.94 2020 59.89 9.07 15.14 18.51 30.91 32.31 53.95 2021 21.23 9.85 46.40 2.46 11.58 8.92 42.02 2022 261.20 2.44 0.93 52.04 19.92 206.72 79.14 2015 — 2022 136.60 21.38 15.65 34.41 25.19 80.81 59.16 -
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