To avoid the noncausal association between environmental factor and COVID-19 when using aggregated data : Simulation-based counterexamples for demonstration
Copyright © 2020 Elsevier B.V. All rights reserved..
In the infectious disease epidemiology, the association between an independent factor and disease incidence (or death) counts may fail to infer the association with disease transmission (or mortality risk). To explore the underlying role of environmental factors in the course of COVID-19 epidemic, the importance of following the epidemiological metric's definition and systematic analytical procedures are highlighted. Cautiousness needs to be taken when understanding the outcome association based on the aggregated data, and overinterpretation should be avoided. The existing analytical approaches to address the inferential failure mentioned in this study are also discussed.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:748 |
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| Enthalten in: |
The Science of the total environment - 748(2020) vom: 15. Dez., Seite 141590 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhao, Shi [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 28.10.2020 Date Revised 31.05.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.scitotenv.2020.141590 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM313734801 |
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| 520 | |a Copyright © 2020 Elsevier B.V. All rights reserved. | ||
| 520 | |a In the infectious disease epidemiology, the association between an independent factor and disease incidence (or death) counts may fail to infer the association with disease transmission (or mortality risk). To explore the underlying role of environmental factors in the course of COVID-19 epidemic, the importance of following the epidemiological metric's definition and systematic analytical procedures are highlighted. Cautiousness needs to be taken when understanding the outcome association based on the aggregated data, and overinterpretation should be avoided. The existing analytical approaches to address the inferential failure mentioned in this study are also discussed | ||
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