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Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020
To describe the geographical heterogeneity of COVID-19 across prefectures in mainland China, we estimated doubling times from daily time series of the cumulative case count between 24 January and 24 February 2020. We analyzed the prefecture-level COVID-19 case burden using linear regression models a...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620948/ https://www.ncbi.nlm.nih.gov/pubmed/36417193 http://dx.doi.org/10.3390/epidemiologia2010009 |
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author | Fung, Isaac Chun-Hai Zhou, Xiaolu Cheung, Chi-Ngai Ofori, Sylvia K. Muniz-Rodriguez, Kamalich Cheung, Chi-Hin Lai, Po-Ying Liu, Manyun Chowell, Gerardo |
author_facet | Fung, Isaac Chun-Hai Zhou, Xiaolu Cheung, Chi-Ngai Ofori, Sylvia K. Muniz-Rodriguez, Kamalich Cheung, Chi-Hin Lai, Po-Ying Liu, Manyun Chowell, Gerardo |
author_sort | Fung, Isaac Chun-Hai |
collection | PubMed |
description | To describe the geographical heterogeneity of COVID-19 across prefectures in mainland China, we estimated doubling times from daily time series of the cumulative case count between 24 January and 24 February 2020. We analyzed the prefecture-level COVID-19 case burden using linear regression models and used the local Moran’s I to test for spatial autocorrelation and clustering. Four hundred prefectures (~98% population) had at least one COVID-19 case and 39 prefectures had zero cases by 24 February 2020. Excluding Wuhan and those prefectures where there was only one case or none, 76 (17.3% of 439) prefectures had an arithmetic mean of the epidemic doubling time <2 d. Low-population prefectures had a higher per capita cumulative incidence than high-population prefectures during the study period. An increase in population size was associated with a very small reduction in the mean doubling time (−0.012, 95% CI, −0.017, −0.006) where the cumulative case count doubled ≥3 times. Spatial analysis revealed high case count clusters in Hubei and Heilongjiang and fast epidemic growth in several metropolitan areas by mid-February 2020. Prefectures in Hubei and neighboring provinces and several metropolitan areas in coastal and northeastern China experienced rapid growth with cumulative case count doubling multiple times with a small mean doubling time. |
format | Online Article Text |
id | pubmed-9620948 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96209482022-11-18 Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020 Fung, Isaac Chun-Hai Zhou, Xiaolu Cheung, Chi-Ngai Ofori, Sylvia K. Muniz-Rodriguez, Kamalich Cheung, Chi-Hin Lai, Po-Ying Liu, Manyun Chowell, Gerardo Epidemiologia (Basel) Article To describe the geographical heterogeneity of COVID-19 across prefectures in mainland China, we estimated doubling times from daily time series of the cumulative case count between 24 January and 24 February 2020. We analyzed the prefecture-level COVID-19 case burden using linear regression models and used the local Moran’s I to test for spatial autocorrelation and clustering. Four hundred prefectures (~98% population) had at least one COVID-19 case and 39 prefectures had zero cases by 24 February 2020. Excluding Wuhan and those prefectures where there was only one case or none, 76 (17.3% of 439) prefectures had an arithmetic mean of the epidemic doubling time <2 d. Low-population prefectures had a higher per capita cumulative incidence than high-population prefectures during the study period. An increase in population size was associated with a very small reduction in the mean doubling time (−0.012, 95% CI, −0.017, −0.006) where the cumulative case count doubled ≥3 times. Spatial analysis revealed high case count clusters in Hubei and Heilongjiang and fast epidemic growth in several metropolitan areas by mid-February 2020. Prefectures in Hubei and neighboring provinces and several metropolitan areas in coastal and northeastern China experienced rapid growth with cumulative case count doubling multiple times with a small mean doubling time. MDPI 2021-03-11 /pmc/articles/PMC9620948/ /pubmed/36417193 http://dx.doi.org/10.3390/epidemiologia2010009 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Fung, Isaac Chun-Hai Zhou, Xiaolu Cheung, Chi-Ngai Ofori, Sylvia K. Muniz-Rodriguez, Kamalich Cheung, Chi-Hin Lai, Po-Ying Liu, Manyun Chowell, Gerardo Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020 |
title | Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020 |
title_full | Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020 |
title_fullStr | Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020 |
title_full_unstemmed | Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020 |
title_short | Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020 |
title_sort | assessing early heterogeneity in doubling times of the covid-19 epidemic across prefectures in mainland china, january–february, 2020 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620948/ https://www.ncbi.nlm.nih.gov/pubmed/36417193 http://dx.doi.org/10.3390/epidemiologia2010009 |
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