The response of influenza-like illnesses to short-term weather variability intensity and risk early warning

doi:10.11755/j.issn.1006-7639(2023)-06-0952

Abstract

Abstract:

Exploring the quantitative impact of short-term weather variability intensity (SWVI) on influenza incidence in Hubei Province is of significant importance for conducting early risk warning and formulating prevention policies. Based on the influenza incidence data and meteorological station observation, an index of SWVI has been built, which can measure the cumulative changes over a short-term in minimum temperature between two consecutive weeks. Based on the Distributed Lag Nonlinear Model (DLNM), the relation between SWVI index and influenza incidence risk was evaluated and a set of method for level classification of influenza incidence risk was developed. The results show that the intra-annual variation of number of Influenza-Like Illnesses (ILI) exhibited bimodal structure, with the first peak occurring in autumn and winter, and the second peak appearing in early summer months. The SWVI index also exhibited a bimodal distribution, but the peak occurring earlier than the peak of ILI. From November to March of the following year, SWVI index had a strong indicative significance for the change of ILI morbidity. In this period, when SWVI reaches 8.0 ℃, the cumulative relative risk (RR) of ILI incidence at the same period and the next week was 1.16 (95% confidence interval: 1.087-1.250). In addition, SWVI index also had an indirect effect on the risk of ILI with a lag of 4-9 weeks, which was less affected than the immediate effect, but lasted longer. Using the percentile method and the relationship model between the SWVI index and the ILI incidence risk, a set of influenza risk early warning method was established. When the SWVI index was greater than or equal to 8.0 ℃, the influenza incidence reached high risk level.

Key words: influenza, incidence risk, short-term weather variability intensity

摘要：

研究短期天气变化强度对湖北省流感发病的定量影响，对开展发病风险预警和制定防御对策具有重要意义。本文利用2009—2020年湖北省流感发病数据和气象站观测数据，通过建立一项衡量短期内相邻两周最低气温累积变化的短期天气变化强度（Short-term Weather Variability Intensity， SWVI）指数，基于分布滞后非线性模型（Distributed Lag Nonlinear Model，DLNM）定量评估SWVI指数与流感发病风险的关系，探讨流感发病风险预警等级划分方法。结果表明：湖北省流感样病例（Influenza-Like Illnesses，ILI）发病人数年内变化呈双峰型，峰值位于秋冬季，次高峰位于前夏；SWVI指数也呈双峰型，但峰值出现时间较ILI发病人数早。11月至次年3月，SWVI指数对ILI发病率的变化有较强的指示意义，该时段内当SWVI指数达到8.0 ℃时，同期至未来1周ILI发病的累积相对风险（Relative Risk， RR）达1.16（95%置信区间为：1.087~1.250）；此外，SWVI还对滞后4—9周的ILI发病风险产生间接影响，相比即刻效应影响程度小，但持续时间长。利用百分位法及SWVI指数与ILI发病风险的关系模型，建立一套基于SWVI指数的流感发病风险预警方法，当SWVI大于等于8.0 ℃时，其对流感发病影响达高风险。

关键词: 流感, 发病风险, 短期天气变化强度

CLC Number:

P467

ZHAO Xiaofang, FANG Sida, LEI Xiaomei, LIU Min, YU Xiao, XU Hui. The response of influenza-like illnesses to short-term weather variability intensity and risk early warning[J]. Journal of Arid Meteorology, 2023, 41(6): 952-960.

赵小芳, 方思达, 雷小妹, 刘敏, 余晓, 徐慧. 流感发病对短期天气变化强度的响应及风险预警研究[J]. 干旱气象, 2023, 41(6): 952-960.

Figures/Tables 10

Tab.1 Summary of influenza surveillance sentinel hospitals in Hubei Province

序号	哨点医院名称	代表地区
1	鄂州市中心医院	鄂州
2	恩施州中心医院	恩施
3	黄冈市中心医院	黄冈
4	黄石市第二医院	黄石
5	黄石市中心医院（中心院区）	黄石
6	荆门市第一人民医院	荆门
7	荆州市第二人民医院	荆州
8	十堰市人民医院	十堰
9	十堰市太和医院	十堰
10	松滋市人民医院	荆州
11	随州市中心医院	随州
12	武汉儿童医院	武汉
13	武汉市一医院	武汉
14	咸宁市中心医院	咸宁
15	襄阳市第一人民医院	襄阳
16	襄阳市中心医院	襄阳
17	孝感市中心医院	孝感
18	宜昌市第二人民医院	宜昌
19	宜昌市中心人民医院	宜昌

Fig.1 The inter-annual (a) and intra-annual (b) variation of number and morbidity of ILI from 2010 to 2019 in Hubei Province

Fig.2 The intra-annual variation of number of ILI cases (a) and index of short-term weather variability intensity (b) in Wuhan during 2009-2019

Fig.3 The lag correlation between ILI morbidity and SWVI index (a, 0 represents the current year, 1 represents the next year; the asterisks pass the significant test at α=0.05), and the scatter plot between average SWVI index from November to December and maximum ILI morbidity during November to March of the following year (b) in Wuhan during 2009-2020

Fig.4 The weekly variation of SWVI and ILI morbidity during influenza outbreak from the 43rd week in 2016 to the 16th week in 2017 in Wuhan

Fig.5 The exposure response relationship between SWVI index and ILI morbidity with a lag of 0 (a) and 2 (b) weeks (a, b), and the lag effect of SWVI index on ILI morbidity (c, d) at the 5th quantile (c, 0.0 ℃) and the 95th quantile (d, 8.0℃) in Wuhan during 2009-2020 (The black solid line is the relative risk, the oblique line areas represent the 95% confidence interval)

Tab.2 The relative risks and 95% confidence interval of ILI morbidity caused by SWVI index with different quantiles in Wuhan during 2009-2020

滞后时间	第5个百分位数（0.0 ℃）	第95个百分位数（8.0 ℃）
滞后0周	1.058（0.918~1.219）	1.117（1.051~1.187）*
滞后1周	1.033（0.971~1.099）	1.043（1.020~1.067）*
滞后2周	1.020（0.947~1.099）	1.016（0.986~1.046）
滞后3周	1.013（0.951~1.078）	1.016（0.988~1.039）
滞后4周	1.007（0.960~1.057）	1.023（1.003~1.043）*
滞后5周	1.001（0.948~1.056）	1.035（1.013~1.058）*
滞后6周	0.993（0.936~1.054）	1.043（1.018~1.068）*
滞后7周	0.983（0.933~1.037）	1.044（1.021~1.066）*
滞后8周	0.974（0.929~1.022）	1.037（1.018~1.056）*
滞后9周	0.968（0.909~1.031）	1.028（1.004~1.053）*

Fig.6 The weekly variation of SWVI index and number of ILI in Wuhan from the 38th week in 2021 to the 9th week in 2022

Fig.7 The distribution of occurrence frequency of SWVI index with different grades in typical cities in Hubei from 2009 to 2020

Tab.3 The level classification of ILI incidence risk based on SWVI index

SWVI指数阈值/℃	划分依据	预警等级
SWVI=0.0	无天气波动	低
0.0<SWVI≤5.6	模型阈值，SWVI指数对流感发病风险无显著影响	较低
5.6<SWVI<8.0	SWVI指数的第95个百分位数，流感发病风险增加	较高
SWVI≥8.0	流感发病风险大幅增加	高

References 39

[1]	陈思齐, 俞敏, 周脉耕, 等, 2021. 基于体感温度-寿命损失年暴露反应关系确定体感温度的健康风险预警阈值研究[J]. 中华流行病学杂志, 42(8): 1 445-1 452.
[2]	邓源, 任翔, 郭玉清, 等, 2023. 2008—2020年我国北方15个城市流感与气象因素的关联性研究[J]. 中华流行病学杂志, 44(5): 765-771.
[3]	方道奎, 周国宏, 冯锦姝, 等, 2019. 深圳市高温热浪健康风险指数的建立和应用评估[J]. 环境卫生学杂志, 9(1): 14-18.
[4]	付之鸥, 鲍昌俊, 李中杰, 等, 2020. 基于“大数据”的流感预警研究进展[J]. 中华流行病学杂志, 41(6): 975-980.
[5]	范进进, 秦鹏程, 史瑞琴, 等, 2022. 气候变化背景下湖北省高温干旱复合灾害变化特征[J]. 干旱气象, 40(5): 780-790. DOI
[6]	黄照, 刘涛, 许燕君, 等, 2018. 基于死亡数据用DLNM构建气象健康指数[J]. 环境卫生学杂志, 8(5): 368-373.
[7]	黄开龙, 林锦春, 马盼, 等, 2021. 气象条件对深圳市罗湖区上呼吸道感染就诊人数的影响[J]. 干旱气象, 39(6): 995-1 005.
[8]	李瑞盈, 张一博, 杨佳, 等, 2019. 秦皇岛气象因素对儿童下呼吸道疾病就诊人数影响及预测研究[J]. 干旱气象, 37(3): 460-466.
[9]	卢晶晶, 吕劲文, 钱峥, 2016. 宁波市高温中暑气象等级评定方法研究[J]. 气象与环境学报, 32(4): 91-97.
[10]	李国栋, 张俊华, 焦耿军, 等, 2013. 气候变化对传染病爆发流行的影响研究进展[J]. 生态学报, 33(21): 6 762-6 773.
[11]	蒙丽君, 胡国清, 姚梦, 等, 2022. 环境气象因素对新型冠状病毒肺炎传播影响的研究进展[J]. 环境与职业医学, 39(3): 348-352.
[12]	马盼, 王馨梓, 张莉, 等, 2022. 深圳流感发病的气象诱因及预测建模研究[J]. 气象学报, 80(3): 421-432.
[13]	马蕾, 赵蔚, 杨柳, 等, 2023. 宁夏“星空旅游”气候资源适宜度评估[J]. 干旱气象, 41(2): 309-317. DOI
[14]	秦康, 张业武, 张鹏, 等, 2019. 中国大陆三种流感监测数据的时效性比较[J]. 中华疾病控制杂志, 23(4): 387-391.
[15]	殷宁潞, 李俊林, 洪也, 等, 2023. 辽宁省两县域城市气温对呼吸系统疾病住院人数的影响[J]. 干旱气象, 41(1): 132-142. DOI
[16]	徐静, 刘华悦, 靳甜甜, 等, 2021. 气温对秦皇岛市儿童呼吸系统疾病的影响[J]. 干旱气象, 39(2): 326-332.
[17]	曾舸, 黄超洋, 王小磊, 等, 2022. 2017—2020年湖南省哨点监测流感样病例流行特征[J]. 实用预防医学, 29(11): 1 342-1 345.
[18]	周艳丽, 张海艳, 王珺, 等, 2021. 北京市东城区流感样病例与气象因素的相关性分析[J]. 中国公共卫生管理, 37(5): 650-653.
[19]	祝寒松, 王明斋, 谢忠杭, 等, 2019. 厦门市流行性感冒发病与气象因素影响[J]. 中国公共卫生, 35(10): 1 404-1 409.
[20]	AKAIKE H, 1974. A new look at the statistical model identification[J]. IEEE Transactions on Automatic Control, 19(6): 716-723.
[21]	AXELSEN J B, YAARI R, GRENFELL B T, et al, 2014. Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers[J]. Proceedings of the National Academy of Sciences of the United States of America, 111(26): 9 538-9 542.
[22]	BRESEE J, HAYDEN F G, 2013. Epidemic influenza-responding to the expected but unpredictable[J]. The New England Journal of Medicine, 368(7): 589-592.
[23]	CAINI S, SPREEUWENBERG P, DONKER G, et al, 2018. Climatic factors and long-term trends of influenza-like illness rates in The Netherlands, 1970-2016[J]. Environmental Research, 167: 307-313. DOI PMID
[24]	CANNELL J J, ZASLOFF M, GARLAND C F, et al, 2008. On the epidemiology of influenza[J]. Journal of Virology, 5(1): 29-29.
[25]	CARDER M, MCNAMEE R, BEVERLAND I, et al, 2005. The lagged effect of cold temperature and wind chill on cardiorespiratory mortality in Scotland[J]. Occupational and Environmental Medicine, 62(10): 702-710. PMID
[26]	DU M, ZHU H, YIN X, et al, 2022. Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014-2017[J]. PLoS One, 17(12), e0277045. DOI:10.1371/journal.pone.0277045.
[27]	FIELLER E C, HARTLEY H O, PEARSON E S, 1957. Tests for rank correlation coefficients. i[J]. Biometrika, 44(3/4): 470-481.
[28]	GUO Y, BARNETT A G, YU W, et al, 2011. A large change in temperature between neighbouring days increases the risk of mortality[J]. PLoS One, 6(2), e16511. DOI:10.1371/journal.pone.0016511.
[29]	GASPARRINI A, 2011. Distributed lag linear and non-linear models in R: the package dlnm[J]. Journal of Statistical Software, 43(8): 1-20. PMID
[30]	GUO Q Z, DONG Z Q, ZENG W L, et al, 2019. The effects of meteorological factors on influenza among children in Guangzhou, China[J]. Influenza and Other Respiratory Viruses, 13(2): 166-175. DOI PMID
[31]	KANG Y T, TANG H S, JIANG L L, et al, 2020. Air temperature variability and high-sensitivity C reactive protein in a general population of China[J]. Science of the Total Environment, 749, 141588. DOI:10.1016/j.scitotenv.2020.141588.
[32]	LIU Z D, ZHANG J, ZHANG Y, et al, 2019. Effects and interaction of meteorological factors on influenza: based on the surveillance data in Shaoyang, China[J]. Environmental Research, 172: 326-332. DOI PMID
[33]	LOWEN A C, MUBAREKA S, STEEL J, et al, 2007. Influenza virus transmission is dependent on relative humidity and temperature[J]. PLoS Pathogens, 3(10): 1 470-1 476.
[34]	LIU Q, TAN Z M, SUN J, et al, 2020. Changing rapid weather variability increases influenza epidemic risk in a warming climate[J]. Environmental Research Letters, 15(4), 044004. DOI:10.1088/1748-9326/ab70bc.
[35]	SPIGA R, BATTON H M, Sarazin M, 2016. Predicting fluctuating rates of hospitalizations in relation to influenza epidemics and meteorological factors[J]. PLoS One, 11(6), e0157492. DOI:10.1371/journal.pone.0157492.
[36]	SCHNEIDER A, SCHUH A, MAETZEL F K, et al, 2008. Weather-induced ischemia and arrhythmia in patients undergoing cardiac rehabilitation: another difference between men and women[J]. International Journal of Biometeorology, 52(6): 535-547. DOI PMID
[37]	VICEDO-CABRERA A M, FORSBERG B, TOBIAS A, et al, 2016. Associations of inter-and intraday temperature change with mortality[J]. American Journal of Epidemiology, 183(4): 286-293.
[38]	ZHU H, CHEN S, LU W, et al, 2022. Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm[J]. BMC Public Health, 22(1), 2335. DOI:10.1186/s12889-022-14299-y. PMID
[39]	ZHANG K, ROOD R B, MICHAILIDIS G, et al, 2012. Comparing exposure metrics for classifying ‘dangerous heat’ in heat wave and health warning systems[J]. Environment International, 46: 23-29.