Investigating the cumulative lag effects of environmental exposure under urban differences on COVID-19
Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved..
Although all walks of life are paying less attention to COVID-19, the spread of COVID-19 has never stopped. As an infectious disease, its transmission speed is closely related to the atmosphere environment, particularly the temperature (T) and PM2.5 concentrations. However, How T and PM2.5 concentrations are related to the spread of SARS-CoV-2 and how much their cumulative lag effect differ across cities is unclear. To identify the characteristics of cumulative lag effects of environmental exposure under city differences, this study used a generalized additive model to investigate the associations between T/PM2.5 concentrations and the daily number of new confirmed COVID-19 cases (NNCC) during the outbreak period in the second half of 2021 in Shaoxing, Shijiazhuang, and Dalian. The results showed that except for PM2.5 concentrations in Shaoxing, the NNCC in the three cities generally increased with the unit increase of T and PM2.5 concentrations. In addition, the cumulative lag effects of T/PM2.5 concentrations on NNCC in the three cities reached a peak at lag 26/25, lag 10/26, and lag 18/13 days, respectively, indicating that the response of NNCC to T and PM2.5 concentrations varies among different regions. Therefore, combining local meteorological and air quality conditions to adopt responsive measures is an important way to prevent and control the spread of SARS-CoV-2.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 Suppl 1 |
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| Enthalten in: |
Journal of infection and public health - 17 Suppl 1(2024) vom: 31. März, Seite 76-81 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Jiemei [VerfasserIn] |
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| Links: |
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| Themen: |
Air Pollutants |
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| Anmerkungen: |
Date Completed 25.03.2024 Date Revised 25.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jiph.2023.06.002 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM357928296 |
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| 520 | |a Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved. | ||
| 520 | |a Although all walks of life are paying less attention to COVID-19, the spread of COVID-19 has never stopped. As an infectious disease, its transmission speed is closely related to the atmosphere environment, particularly the temperature (T) and PM2.5 concentrations. However, How T and PM2.5 concentrations are related to the spread of SARS-CoV-2 and how much their cumulative lag effect differ across cities is unclear. To identify the characteristics of cumulative lag effects of environmental exposure under city differences, this study used a generalized additive model to investigate the associations between T/PM2.5 concentrations and the daily number of new confirmed COVID-19 cases (NNCC) during the outbreak period in the second half of 2021 in Shaoxing, Shijiazhuang, and Dalian. The results showed that except for PM2.5 concentrations in Shaoxing, the NNCC in the three cities generally increased with the unit increase of T and PM2.5 concentrations. In addition, the cumulative lag effects of T/PM2.5 concentrations on NNCC in the three cities reached a peak at lag 26/25, lag 10/26, and lag 18/13 days, respectively, indicating that the response of NNCC to T and PM2.5 concentrations varies among different regions. Therefore, combining local meteorological and air quality conditions to adopt responsive measures is an important way to prevent and control the spread of SARS-CoV-2 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Cumulative lag effects | |
| 650 | 4 | |a PM(2.5) concentrations | |
| 650 | 4 | |a Public Health Emergency | |
| 650 | 4 | |a Urban differences | |
| 650 | 7 | |a Air Pollutants |2 NLM | |
| 650 | 7 | |a Particulate Matter |2 NLM | |
| 700 | 1 | |a Ruan, Zhaohui |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Xiuyan |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Shikui |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xia |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Xingrun |e verfasserin |4 aut | |
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