湖南省气温对新型冠状病毒肺炎发病数的滞后影响

毛倩; 刘玉洁; 王喆; 管佩霞; 肖宇飞; 朱高培; 孟维静; 王素珍; 石福艳

doi:10.16462/j.cnki.zhjbkz.2021.04.007

湖南省气温对新型冠状病毒肺炎发病数的滞后影响

doi: 10.16462/j.cnki.zhjbkz.2021.04.007

1.
261053 潍坊，潍坊医学院公共卫生学院卫生统计学系
2.
261053 潍坊，潍坊医学院生命科学与技术学院学生工作办公室

基金项目:

国家自然科学基金 81803337

国家自然科学基金 81872719

国家统计局课题 2018LY79

山东省自然科学基金 ZR2019MH034

山东省高等学校青创人才引育计划 2019-6-156

山东省高等学校青创人才引育计划 Lu-Jiao

潍坊医学院博士启动基金 2017BSQD51

详细信息

通讯作者:
石福艳，E-mail：shifuyan@126.com

中图分类号: R183.3
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出版历程
- 收稿日期: 2020-07-27
- 修回日期: 2020-11-29
- 网络出版日期: 2021-05-11
- 刊出日期: 2021-04-10

Lag effect of temperature on the incidence of COVID-19 in Hunan Province

1.
Department of Health Statistics, School of Public Health, Weifang Medical University, Weifang 261053, China
2.
Student Affairs Office, School of Life Science and Technology, Weifang Medical University, Weifang 261053, China

Funds:

National Natural Science Foundation of China 81803337

National Natural Science Foundation of China 81872719

National Bureau of Statistics 2018LY79

Natural Science Foundation of Shandong Province ZR2019MH034

Colleges and Universities Talent Introduction Program of Shandong Province 2019-6-156

Colleges and Universities Talent Introduction Program of Shandong Province Lu-Jiao

Doctor Starting Fund Project of Weifang Medical University 2017BSQD51

More Information

Corresponding author: SHI Fu-yan, E-mail: shifuyan@126.com

摘要

摘要: 目的研究湖南省日均气温对COVID-19日发病数的滞后影响，为疫情的有效防控提供科学依据。方法本研究对2020年1月21日―2020年3月2日湖南省气象因素和空气质量因素与COVID-19日发病数进行Spearman相关分析和分布滞后非线性模型分析。结果观察期间，湖南省新型冠状病毒肺炎报告新发病例共1 018例。分布滞后非线性模型结果显示，日均气温与COVID-19日发病数的关系呈非线性，累积发病风险随气温的升高而降低，且发病人群的气温风险最低点为0 ℃。高温对日发病数的影响为短期即时效应，低温对每日发病人数的影响具有滞后性，滞后效应长达12 d，当日均温为-5 ℃，滞后天数为8 d时，相对危险度最高(RR=2.20, 95% CI=1.16~4.19)，且高温(10 ℃)较低温(6 ℃)影响更为显著。结论气温是影响湖南省COVID-19发病的因素，且有滞后性；高温和低温均可导致发病风险升高，应针对脆弱人群和危重患者加强防护措施从而降低发病风险。
- 分布滞后非线性模型 /
- 日均气温 /
- 日发病数 /
- COVID-19
Abstract: Objective To explore the lag effect of daily average temperature on the incidence of coronavirus disease 2019 (COVID-19) in Hunan Province and to provide scientific evidences for effective prevention of COVID-19. Methods The meteorological factors, the air quality factors and the data conincidence of COVID-19 reported in Hunan Province during January 21, 2020 to March 2, 2020 were collected. Spearman correlation and distributed lag non-linear model analysis were performed. Results A total of 1 018 COVID-19 cases were reported in Hunan Province. The distribution lag non-linear model results showed that the influence of daily average temperature on the incidence of COVID-19 presented a nonlinear relationship. The cumulative relative incidence risk of COVID-19 decreased with the increase of daily average temperature, and the lowest temperature risk of the patients was 0 ℃. Both cold temperature and hot temperature increased incidence risk of COVID-19. It was indicated that the hot effects were immediate, however, the cold effects with obvious lag effect persisted up to 12 days. The highest relative risk of COVID-19 incidence was associated with lag 8-day daily average temperature of -5 ℃(RR=2.20, 95% CI=1.16-4.19). The influence of high temperature(10 ℃) was more significant than that of low temperature(6 ℃). Conclusion The daily average temperature, especially cold or hot temperature, was an important influencing factor of the incidence of COVID-19 in Hunan Province, which had lag influence on the incidence of COVID-19. We suggested that some related preventive measures should be adopted to protect vulnerable population and severe patients to reduce the incidence risk.
- Distributional lag non-linear model /
- Daily average temperature /
- Daily incidence /
- COVID-19

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图 1 2020年1月21日―2020年3月2日湖南省COVID-19日发病数(a)和日均气温(b)的时间序列

Figure 1. The time series diagram of the incidence of COVID-19 and average daily air temperature during January 21, 2020-March 2, 2020 in Hunan Province

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图 2 湖南省日均气温与发病人数的暴露反应关系

Figure 2. The exposure-response relationship between average daily air temperature and the incidence of COVID-19 in Hunan Province

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图 3 湖南省不同滞后天数日均气温对COVID-19日发病数的影响

Figure 3. The effect of average daily temperature on the incidence of COVID-19 on different lagging days in Hunan Province

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图 4 不同滞后天数下的温度效应和不同温度下的滞后效应对日发病数的影响

Figure 4. The temperature effect under different lagging days and the lag effect under different temperature on the incidence

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表 1 湖南省COVID-19日发病数与气象因素、空气质量因素的基本情况(n=1 018)

Table 1. The characteristics of meteorological factors, air quality factor and the incidence of COVID-19 in Hunan Province (n=1 018)

变量	观测天数(d)	(min, max)	M(P₂₅, P₇₅)
日发病数(例)	42	(0.00, 78.00)	19.50(1.00, 43.0)
日均气温(℃)	42	(3.45, 17.63)	8.52(6.44, 10.67)
日降水量(mm)	42	(0.00, 33.17)	1.50(0.05, 6.45)
日均气压(hPa)	42	(985.73, 1 005.00)	996.72(993.19, 998.73)
日相对湿度(%)	42	(57.48, 93.21)	84.04(74.02, 87.83)
日照时数(h)	42	(0.07, 9.68)	1.86(0.38, 5.23)
AQI(μg/m³)	42	(30.30, 101.00)	58.92(43.85, 67.85)
PM_2.5(μg/m³)	42	(11.62, 75.46)	40.96(26.54, 49.00)
SO₂(mg/m³)	42	(3.39, 7.92)	4.77(4.15, 5.46)
CO(mg/m³)	42	(0.44, 1.29)	0.85(0.79, 0.96)
NO₂(mg/m³)	42	(6.62, 27.00)	14.65(11.54, 17.69)
O₃(mg/m³)	42	(33.31, 90.85)	63.00(50.08, 75.15)

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表 2 湖南省COVID-19日发病数与气象因素、空气质量因素的Spearman相关分析结果

Table 2. The Spearman correlation analysis results among the incidence of COVID-19, meteorological factors and air quality index in Hunan Province

变量	日均气温(℃)	日降水量(mm)	日均气压(hPa)	日相对湿度(%)	日照时数(h)	AQI (μg/m³)	PM_2.5 (μg/m³)	SO₂ (mg/m³)	CO (mg/m³)	NO₂ (mg/m³)	O₃ (mg/m³)
日降水量(mm)	-0.140
日均气压(hPa)	-0.651^a	-0.262
日相对湿度(%)	-0.001	0.851^a	-0.331
日照时数(h)	0.202	-0.730^a	0.177	-0.733^a
AQI(μg/m³)	0.100	-0.387	0.040	-0.326	0.034
PM_2.5(μg/m³)	0.097	-0.321^a	0.002	-0.230	-0.048	0.981^a
SO₂(mg/m³)	0.577^a	-0.635^a	-0.260	-0.600^a	0.535^a	0.407^a	0.357^a
CO(mg/m³)	0.286^a	0.308^a	-0.532^a	0.458^a	-0.359^a	0.343^a	0.389^a	0.111
NO₂(mg/m³)	0.685^a	-0.374^a	-0.417^a	-0.248	0.180	0.457^a	0.451^a	0.813^a	0.395^a
O₃(mg/m³)	-0.095	-0.682^a	0.376^a	-0.755^a	0.695^a	0.406^a	0.298	0.348^a	-0.354^a	0.043
日发病数(例)	-0.584^a	0.193	0.258	0.131	-0.156	0.204	0.244	-0.389	0.118	-0.412	0.051
注：^aP＜0.05。

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