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Article

Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020

1
Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
2
Department of Geography, Texas Christian University, Fort Worth, TX 76109, USA
3
Department of Psychology and Criminal Justice, School of Education & Behavioral Sciences, Middle Georgia State University, Macon, GA 31206, USA
4
Independent Researcher, Hong Kong Special Administrative Region, China
5
Department of Biostatistics, Boston University, Boston, MA 02215, USA
6
Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30302, USA
*
Authors to whom correspondence should be addressed.
I.C.H.F., X.Z., and C.-N.C. contributed equally as co-first authors.
S.K.O. and K.M.-R. contributed equally as co-second authors.
Academic Editor: Ubydul Haque
Received: 23 November 2020 / Revised: 8 February 2021 / Accepted: 1 March 2021 / Published: 11 March 2021
(This article belongs to the Special Issue Evolving COVID-19 Epidemiology and Dynamics)
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. View Full-Text
Keywords: coronavirus; COVID-19; doubling time; epidemiology; geography; Hu Line; SARS-CoV-2; spatial analysis; spatial clustering coronavirus; COVID-19; doubling time; epidemiology; geography; Hu Line; SARS-CoV-2; spatial analysis; spatial clustering
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MDPI and ACS Style

Fung, I.C.-H.; Zhou, X.; Cheung, C.-N.; Ofori, S.K.; Muniz-Rodriguez, K.; Cheung, C.-H.; Lai, P.-Y.; Liu, M.; Chowell, G. Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020. Epidemiologia 2021, 2, 95-113. https://0-doi-org.brum.beds.ac.uk/10.3390/epidemiologia2010009

AMA Style

Fung IC-H, Zhou X, Cheung C-N, Ofori SK, Muniz-Rodriguez K, Cheung C-H, Lai P-Y, Liu M, Chowell G. Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020. Epidemiologia. 2021; 2(1):95-113. https://0-doi-org.brum.beds.ac.uk/10.3390/epidemiologia2010009

Chicago/Turabian Style

Fung, Isaac C.-H., Xiaolu Zhou, Chi-Ngai Cheung, Sylvia K. Ofori, Kamalich Muniz-Rodriguez, Chi-Hin Cheung, Po-Ying Lai, Manyun Liu, and Gerardo Chowell. 2021. "Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020" Epidemiologia 2, no. 1: 95-113. https://0-doi-org.brum.beds.ac.uk/10.3390/epidemiologia2010009

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