1.中山大学生命科学学院,广东 广州510275
2.安阳市肿瘤医院医务科,河南 安阳455002
何淼(1963年生),男;研究方向:生物统计学、生物信息学;E-mail:lsshem@mail.sysu.edu.cn
纸质出版日期:2022-07-25,
网络出版日期:2021-07-23,
收稿日期:2021-01-15,
录用日期:2021-06-29
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何淼,石昌浩,佘铉捷等.2019冠状病毒病暴发初期时空特征及污染物评估[J].中山大学学报(自然科学版),2022,61(04):11-21.
HE Miao,SHI Changhao,SHE Xuanjie,et al.Spatial-temporal characteristics and pollutant assessment in the early stage of COVID-19 outbreak in China[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2022,61(04):11-21.
何淼,石昌浩,佘铉捷等.2019冠状病毒病暴发初期时空特征及污染物评估[J].中山大学学报(自然科学版),2022,61(04):11-21. DOI: 10.13471/j.cnki.acta.snus.2021E002.
HE Miao,SHI Changhao,SHE Xuanjie,et al.Spatial-temporal characteristics and pollutant assessment in the early stage of COVID-19 outbreak in China[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2022,61(04):11-21. DOI: 10.13471/j.cnki.acta.snus.2021E002.
为探究2020年中国内地2019冠状病毒病(COVID-19,coronavirus disease 2019)暴发初期的时空传播特征,以及评估大气污染因素对疫情传播的风险,本文从中国国家及各省市卫生健康委员会网站获取了2020年初各省份2019冠状病毒病的每日新增确诊病例数据;采用自相关分析和趋势分析等方法研究中国内地疫情传播的时空特征,采用斯皮尔曼等级相关系数和广义相加模型对影响湖北省疫情发展的大气污染因素进行了风险评估。自2020年1月20日~2月9日,中国内地2019冠状病毒病累计确诊病例39 877例。分析显示,2020年中国内地2019冠状病毒病暴发初期的前3周全局莫兰指数取值分别为0.249、0.307和0.297 (
P
<
0.01),聚集现象十分显著。湖南省、广东省、江西省、浙江省、安徽省和江苏省属于高-高聚集区。疫情热点地区基本分布在东经108°47'~123°10'和北纬25°31'~35°20'区域范围以内。湖北省每日新增确诊病例与大气可吸入颗粒物 (PM
10
)、 NO
2
和 O
3
日平均质量浓度呈现显著的正相关(
ρ
=0.515, 0.579 , 0.536,
P
<
0.05)。PM
10
和大气细颗粒物(PM
2.5
)的相对危险度 (RR,relative risk) 数值在滞后0 d (Lag 0) 达到最大,NO
2
的RR值在滞后4 d达到最大,O
3
的RR值在滞后0~1 d达到最大。NO
2
日平均质量浓度每增加10 μg/m
3
,每日新增确诊病例的超额危险度(ER,excess risk)为32.745% (95% confidence interval (CI):11.586%~57.916%)。对PM
2.5
和PM
10
分别引入NO
2
后,NO
2
日平均质量浓度每增加10 μg/m
3
,每日新增确诊病例的ER值分别为23.929%(95% CI:4.705%~46.682%)和24.672%(95% CI:5.379%~47.496%)。研究表明,2020年中国内地2019冠状病毒病暴发初期的前3周,东南方向是疫情传播的主要方向。降低疫情热点区域大气中的NO
2
质量浓度对疫情防控具有积极作用。
To explore the early stage spatial-temporal characteristics and to assess the factors of atmospheric pollution that may affect the development of coronavirus disease 2019 (COVID-19) outbreak in the Chinese Mainland in 2020, we collected the daily new cases of COVID-19 in the Municipalities and Provinces from the websites of National and Provincial Health Commission of China. The spatio-temporal characteristics of COVID-19 epidemic were studied using autocorrelation analysis and trend analysis. The Spearman's correlation coefficient for ranked data and generalized additive model were used for risk assessment of air pollutants affecting the COVID-19 epidemic of Hubei Province. Daily new cases of COVID-19 in the Chinese Mainland totaled 39 877 from January 20th to February 9th of 2020. The global Moran index values of these three weeks were 0.249, 0.307 and 0.297 (
P
<
0.01), respectively. There was a significant clustering phenomenon. The high incidence regions included Hunan Province, Guangdong Province, Jiangxi Province, Zhejiang Province, Anhui Province and Jiangsu Province. The epidemic hot spots were basically distributed in the area from 108° 47'-123° 10' E to 25° 31'-35° 20' N. Daily new cases of COVID-19 in Hubei Province was positively correlated with daily average concentrations of PM
10
, NO
2
and O
3
pollutants (
ρ
=0.515, 0.579 and 0.536,
P
<
0.05). The lag effects of air pollutions were existed. The relative risk (RR) values of PM
2.5
and PM
10
reached to maximum with lag0, the RR value of NO
2
reached to maximum with lag4, and the RR value of O
3
reached to maximum with lag 0~1. We estimated that a 10 μg/m
3
increase in day-before NO
2
daily average concentration was associated with a 32.745% (95% Confidence Interval (CI):11.586%-57.916%) excess risk (ER) of daily new cases of COVID-19. And NO
2
had a significant impact on daily new cases of COVID-19. When NO
2
was introduced to PM
2.5
and PM
10
separately, for every 10 μg/m
3
rise in NO
2
daily average concentration, the ER of daily new cases of COVID-19 was 23.929% (95% CI: 4.705%-46.682%) and 24.672% (95% CI: 5.379%-47.496%), respectively. The study showed that the southeast was the main spread direction in the early stage of COVID-19 outbreak in the Chinese Mainland in 2020. Reducing the atmospheric concentration of nitrogen dioxide in epidemic hot spots has a positive effect on epidemic prevention and control.
2019冠状病毒病 (COVID-19,coronavirus disease 2019)2020年暴发初期时空特征风险评估
coronavirus disease 2019(COVID-19)early stage of outbreak in 2020spatial-temporal characteristicsrisk assessment
LU H, STRATTON C W, TANG Y. Outbreak of pneumonia of unknown etiology in Wuhan China: The mystery and the miracle[J]. Journal of Medical Virology, 2020, 92(4): 401-402.
WU Y C, CHEN C S, CHAN Y J. The outbreak of COVID-19: An overview[J]. Wolters Kluwer Public Health Emergency Collection, 2020, 83(3): 217-220.
HEYMANN D L, SHINDO N. COVID-19: What is next for public health?[J]. The Lancet, 2020, 395(10224): 542-545.
CHENG Y J, TANG F Y, BAO C J, et al. Spatial analyses of typhoid fever in Jiangsu province, People's Republic of China[J]. Geospatial Health,2013, 7(2):279-288.
SU W, WU X, GENG X, et al. The short-term effects of air pollutants on influenza-like illness in Jinan, China[J]. BMC Public Health, 2019, 19:1319.
STOECKLIN S B, ROLLAND P, SILUE Y , et al. First cases of coronavirus disease 2019 (COVID-19) in France: Surveillance, investigations and control measures, January 2020[J]. Euro Surveillance, 2020, 25(6): 2000094.
ROTHE C, SCHUNK M, SOTHMANN P, et al. Transmission of 2019-nCoV Infection from an asymptomatic contact in Germany[J]. New England Journal of Medicine, 2020, 382(10): 970-971.
NHUNG N T T, SCHINDLER C, DIEN T M, et al. Acute effects of ambient air pollution on lower respiratory infections in Hanoi children: An eight-year time series study[J]. Environment International, 2018, 110: 139-148.
LIU Q H, LIU Z C, LI D Q, et al. Assessing the tendency of 2019-nCoV (COVID-19) outbreak in China[EB/OL].[2020-04-16]. https://www.medrxiv.org/content/10.1101/2020.02.09.20021444v5https://www.medrxiv.org/content/10.1101/2020.02.09.20021444v5.
ZHAO S, ZHUANG Z, RAN J J, et al. The association between domestic train transportation and novel coronavirus (2019-nCoV) outbreak in China from 2019 to 2020: A data-driven correlational report[J]. Travel Medicine and Infectious Disease, 2020, 33:101568.
OGEN Y. Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality [J]. Science of the Total Environment, 2020, 726:138605.
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