Cargando…
Determining the Case Fatality Rate of COVID-19 in Italy: Novel Epidemiological Study
BACKGROUND: COVID-19, which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate (CFR). OBJECTIVE: The main objective of this stu...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8834871/ https://www.ncbi.nlm.nih.gov/pubmed/34963659 http://dx.doi.org/10.2196/32638 |
_version_ | 1784649290189111296 |
---|---|
author | Yan, Mengqing Kang, Wenjun Guo, Zhifeng Wang, Qi Wang, Peizhong Peter Zhu, Yun Yang, Yongli Wang, Wei |
author_facet | Yan, Mengqing Kang, Wenjun Guo, Zhifeng Wang, Qi Wang, Peizhong Peter Zhu, Yun Yang, Yongli Wang, Wei |
author_sort | Yan, Mengqing |
collection | PubMed |
description | BACKGROUND: COVID-19, which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate (CFR). OBJECTIVE: The main objective of this study was to explore the value of the discharged CFR (DCFR) to make more accurate forecasts of epidemic trends of COVID-19 in Italy. METHODS: We retrieved the epidemiological data of COVID-19 in Italy published by the John Hopkins Coronavirus Resource Center. We then used the proportion of deaths to discharged cases(including deaths and recovered cases) to calculate the total DCFR (tDCFR), monthly DCFR (mDCFR), and stage DCFR (sDCFR). Furthermore, we analyzed the trend in the mDCFR between January and December 2020 using joinpoint regression analysis, used ArcGIS version 10.7 to visualize the spatial distribution of the epidemic CFR, and assigned different colors to each province based on the CFR or tDCFR. RESULTS: We calculated the numbers and obtained the new indices of the tDCFR and mDCFR for calculating the fatality rate. The results showed that the tDCFR and mDCFR fluctuated greatly from January to May. They first showed a rapid increase followed by a rapid decline after reaching the peak. The map showed that the provinces with a high tDCFR were Emilia-Romagna, Puglia, and Lombardia. The change trend of the mDCFR over time was divided into the following 2 stages: the first stage (from January to May) and the second stage (from June to December). With regard to worldwide COVID-19 statistics, among 6 selected countries, the United States had the highest tDCFR (4.26%), while the tDCFR of the remaining countries was between 0.98% and 2.72%. CONCLUSIONS: We provide a new perspective for assessing the fatality of COVID-19 in Italy, which can use ever-changing data to calculate a more accurate CFR and scientifically predict the development trend of the epidemic. |
format | Online Article Text |
id | pubmed-8834871 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-88348712022-03-08 Determining the Case Fatality Rate of COVID-19 in Italy: Novel Epidemiological Study Yan, Mengqing Kang, Wenjun Guo, Zhifeng Wang, Qi Wang, Peizhong Peter Zhu, Yun Yang, Yongli Wang, Wei JMIR Public Health Surveill Original Paper BACKGROUND: COVID-19, which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate (CFR). OBJECTIVE: The main objective of this study was to explore the value of the discharged CFR (DCFR) to make more accurate forecasts of epidemic trends of COVID-19 in Italy. METHODS: We retrieved the epidemiological data of COVID-19 in Italy published by the John Hopkins Coronavirus Resource Center. We then used the proportion of deaths to discharged cases(including deaths and recovered cases) to calculate the total DCFR (tDCFR), monthly DCFR (mDCFR), and stage DCFR (sDCFR). Furthermore, we analyzed the trend in the mDCFR between January and December 2020 using joinpoint regression analysis, used ArcGIS version 10.7 to visualize the spatial distribution of the epidemic CFR, and assigned different colors to each province based on the CFR or tDCFR. RESULTS: We calculated the numbers and obtained the new indices of the tDCFR and mDCFR for calculating the fatality rate. The results showed that the tDCFR and mDCFR fluctuated greatly from January to May. They first showed a rapid increase followed by a rapid decline after reaching the peak. The map showed that the provinces with a high tDCFR were Emilia-Romagna, Puglia, and Lombardia. The change trend of the mDCFR over time was divided into the following 2 stages: the first stage (from January to May) and the second stage (from June to December). With regard to worldwide COVID-19 statistics, among 6 selected countries, the United States had the highest tDCFR (4.26%), while the tDCFR of the remaining countries was between 0.98% and 2.72%. CONCLUSIONS: We provide a new perspective for assessing the fatality of COVID-19 in Italy, which can use ever-changing data to calculate a more accurate CFR and scientifically predict the development trend of the epidemic. JMIR Publications 2022-02-10 /pmc/articles/PMC8834871/ /pubmed/34963659 http://dx.doi.org/10.2196/32638 Text en ©Mengqing Yan, Wenjun Kang, Zhifeng Guo, Qi Wang, Peizhong Peter Wang, Yun Zhu, Yongli Yang, Wei Wang. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 10.02.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Yan, Mengqing Kang, Wenjun Guo, Zhifeng Wang, Qi Wang, Peizhong Peter Zhu, Yun Yang, Yongli Wang, Wei Determining the Case Fatality Rate of COVID-19 in Italy: Novel Epidemiological Study |
title | Determining the Case Fatality Rate of COVID-19 in Italy: Novel Epidemiological Study |
title_full | Determining the Case Fatality Rate of COVID-19 in Italy: Novel Epidemiological Study |
title_fullStr | Determining the Case Fatality Rate of COVID-19 in Italy: Novel Epidemiological Study |
title_full_unstemmed | Determining the Case Fatality Rate of COVID-19 in Italy: Novel Epidemiological Study |
title_short | Determining the Case Fatality Rate of COVID-19 in Italy: Novel Epidemiological Study |
title_sort | determining the case fatality rate of covid-19 in italy: novel epidemiological study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8834871/ https://www.ncbi.nlm.nih.gov/pubmed/34963659 http://dx.doi.org/10.2196/32638 |
work_keys_str_mv | AT yanmengqing determiningthecasefatalityrateofcovid19initalynovelepidemiologicalstudy AT kangwenjun determiningthecasefatalityrateofcovid19initalynovelepidemiologicalstudy AT guozhifeng determiningthecasefatalityrateofcovid19initalynovelepidemiologicalstudy AT wangqi determiningthecasefatalityrateofcovid19initalynovelepidemiologicalstudy AT wangpeizhongpeter determiningthecasefatalityrateofcovid19initalynovelepidemiologicalstudy AT zhuyun determiningthecasefatalityrateofcovid19initalynovelepidemiologicalstudy AT yangyongli determiningthecasefatalityrateofcovid19initalynovelepidemiologicalstudy AT wangwei determiningthecasefatalityrateofcovid19initalynovelepidemiologicalstudy |