tetano
Editor, Senior Moderator
J Med Virol
. 2020 Jul 21.
doi: 10.1002/jmv.26325. Online ahead of print.
Ambient Air Pollution, Meteorology, and COVID-19 Infection in Korea
Tung Hoang[SUP] 1 [/SUP], Tho Tran Thi Anh[SUP] 2 [/SUP]
Affiliations
Abstract
Background: The outbreak of novel pneumonia coronavirus disease has become a public health concern worldwide. Here, for the first time, the association between Korean meteorological factors and air pollutants and the COVID-19 infection was investigated.
Materials and methods: Data of air pollutants, meteorological factors, and daily COVID-19 confirmed cases of 7 metropolitan cities and 9 provinces were obtained from February 03, 2020 to May 05, 2020 during the first wave of pandemic across Korea. We applied the generalized additive model to investigate the temporal relationship.
Results: There was a significantly non-linear association between daily temperature and COVID-19 confirmed cases. Each 1[SUP]o[/SUP] C increase in temperature was associated with 9% (lag 0-14, OR=1.09, 95% CI=1.03-1.15) increase of COVID-19 confirmed cases when the temperature was below 8[SUP]o[/SUP] C. A 0.01 ppm increase in NO[SUB]2[/SUB] (lag 0-7, lag 0.14, and lag 0-21) was significantly associated with increases of COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.02-1.25), 1.19 (1.09-1.30), and 1.30 (1.19-1.41), respectively. A 0.1 ppm increase in CO (lag 0-21) was associated with the increase in COVID-19 confirmed cases (OR=1.10, 95% CI=1.04-1.16). There was a positive association between per 0.001 ppm of SO[SUB]2[/SUB] concentration (lag 0, lag 0-7, and lag 0-14) and COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.04-1.22), 1.20 (1.11-1.31), and 1.15 (1.07-1.25), respectively.
Conclusion: There were significantly temporal associations between temperature, NO[SUB]2[/SUB] , CO, and SO[SUB]2[/SUB] concentrations and daily COVID-19 confirmed cases in Korea. This article is protected by copyright. All rights reserved.
Keywords: COVID-19; Korea; air pollution; generalized additive model.
. 2020 Jul 21.
doi: 10.1002/jmv.26325. Online ahead of print.
Ambient Air Pollution, Meteorology, and COVID-19 Infection in Korea
Tung Hoang[SUP] 1 [/SUP], Tho Tran Thi Anh[SUP] 2 [/SUP]
Affiliations
- PMID: 32691877
- DOI: 10.1002/jmv.26325
Abstract
Background: The outbreak of novel pneumonia coronavirus disease has become a public health concern worldwide. Here, for the first time, the association between Korean meteorological factors and air pollutants and the COVID-19 infection was investigated.
Materials and methods: Data of air pollutants, meteorological factors, and daily COVID-19 confirmed cases of 7 metropolitan cities and 9 provinces were obtained from February 03, 2020 to May 05, 2020 during the first wave of pandemic across Korea. We applied the generalized additive model to investigate the temporal relationship.
Results: There was a significantly non-linear association between daily temperature and COVID-19 confirmed cases. Each 1[SUP]o[/SUP] C increase in temperature was associated with 9% (lag 0-14, OR=1.09, 95% CI=1.03-1.15) increase of COVID-19 confirmed cases when the temperature was below 8[SUP]o[/SUP] C. A 0.01 ppm increase in NO[SUB]2[/SUB] (lag 0-7, lag 0.14, and lag 0-21) was significantly associated with increases of COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.02-1.25), 1.19 (1.09-1.30), and 1.30 (1.19-1.41), respectively. A 0.1 ppm increase in CO (lag 0-21) was associated with the increase in COVID-19 confirmed cases (OR=1.10, 95% CI=1.04-1.16). There was a positive association between per 0.001 ppm of SO[SUB]2[/SUB] concentration (lag 0, lag 0-7, and lag 0-14) and COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.04-1.22), 1.20 (1.11-1.31), and 1.15 (1.07-1.25), respectively.
Conclusion: There were significantly temporal associations between temperature, NO[SUB]2[/SUB] , CO, and SO[SUB]2[/SUB] concentrations and daily COVID-19 confirmed cases in Korea. This article is protected by copyright. All rights reserved.
Keywords: COVID-19; Korea; air pollution; generalized additive model.