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COVID-19 and gender-specific difference: Analysis of public surveillance data in Hong Kong and Shenzhen, China, from January 10 to February 15, 2020

Published online by Cambridge University Press:  09 March 2020

Shi Zhao*
Affiliation:
Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, China
Peihua Cao
Affiliation:
Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, China
Marc K.C. Chong
Affiliation:
Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, China
Daozhou Gao
Affiliation:
Department of Mathematics, Shanghai Normal University, Shanghai, China
Yijun Lou
Affiliation:
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
Jinjun Ran
Affiliation:
School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
Kai Wang
Affiliation:
Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
Weiming Wang*
Affiliation:
School of Mathematics and Statistics, Huaiyin Normal University, Huaian, China
Lin Yang
Affiliation:
School of Nursing, Hong Kong Polytechnic University, Hong Kong, China
Daihai He*
Affiliation:
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
Maggie H. Wang
Affiliation:
Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, China
*
Author for correspondence: Shi Zhao, E-mail: zhaoshi.cmsa@gmail.com. Or Weiming Wang, E-mail: weimingwang2003@163.com. Or Daihai He, E-mail: daihai.he@polyu.edu.hk
Author for correspondence: Shi Zhao, E-mail: zhaoshi.cmsa@gmail.com. Or Weiming Wang, E-mail: weimingwang2003@163.com. Or Daihai He, E-mail: daihai.he@polyu.edu.hk
Author for correspondence: Shi Zhao, E-mail: zhaoshi.cmsa@gmail.com. Or Weiming Wang, E-mail: weimingwang2003@163.com. Or Daihai He, E-mail: daihai.he@polyu.edu.hk
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Abstract

Type
Letter to the Editor
Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.

To the Editor—An outbreak of coronavirus disease (COVID-19), which began in Wuhan, China in the end of 2019,Reference Li, Guan, Wu and Wang1 has now reached over 100 countries and poses a huge threat to the global public health and economy.Reference Wu, Leung and Leung2 Given the risk of human-to-human transmission, the serial interval, which refers to the time interval from symptom onset of a primary case (ie, the infector) to that of a secondary case (ie, the infectee),Reference Fine3 is an essential quantity, in addition to the basic reproduction number, that drives the speed of spread.

We examined the publicly available materials and collected the records of COVID-19 transmission events in 2 neighboring large cities, Hong Kong4 and Shenzhen,5 in south China from January 10 to February 15, 2020, and we extracted the serial interval data. We identified 48 transmission events (21 in Hong Kong and 27 in Shenzhen), among which 40 events contained the gender information of the primary cases. The last onset date of the primary cases among all collected transmission events was February 2, 2020. The data were collected via public domain; thus, neither ethical approval nor individual consent was applicable. All data used in this work were publicly available from press releases from the Centre for Health Protection (CHP) of Hong Kong4 and the COVID-19 outbreak situation reports of the Shenzhen Municipal Health Commission,5 and the key R code is provided as a supplementary file online.

To explore the temporal patterns and the gender-specific difference of serial intervals, we adopted two regression models. Model 1 is a log-linear form for the percentage change, E[ln(SIi,t)] = α 1G i + α 2t + α 0, and model 2 is a linear form for the unit change, E[SIi,t] = β 1G i + β 2t + β 0, where E[·] is the expectation and α and β are the regression coefficients. The SIi,t represents the serial interval of the ith primary case whose onset date is the tth day. G i denotes the gender of the ith primary case. Hence, the [exp(α 2) – 1] × 100% quantifies the percentage change, and β 2 quantifies the unit change (day) in the serial interval, namely change per day in the calendar date. The gender-specific difference can be interpreted similarly. We fit both models using the standard least-squares approach.

As shown in Figure 1, the serial interval decreased by 0.4 (95% CI, 0.1−0.7), or 6.2% per day (95% CI, 0.4%−11.6%) from January 10 to February 2 in Hong Kong and Shenzhen. The Pearson correlation coefficient between the serial interval and calendar date is estimated at −0.37 (P < .01). The serial interval of male primary cases was 3.5 days (95% CI, 1.2−5.7) shorter than that of female primary cases, or 49.7% (95% CI, 15.3−70.1%) lower in percentage. To verify this finding, we additionally conducted a Cox proportional hazard modeling analysis using a similar formula as in models 1 and 2 to calculate the hazard ratio estimates. The association between serial interval and calendar date as well as gender-specific difference held consistently and significantly.

Fig. 1. The observed (dots and bars) and fitted (curves) serial interval of COVID-19. The results of model (1) are shown in panel (a), and those of model (2) are shown in panel (b). In both panels, the red represents the female primary cases, and the blue represents the male primary cases. The dots are the observed (or median) serial interval, and the bars are the ranges of serial intervals for multiple primary cases. The bold curves are the fitting results and the dashed curves are the 95% confidence intervals.

The shortening in serial interval over time is likely due to the strengthening of the public health control measures. The contact tracing and timely isolation of confirmed COVID-19 infections could lead to shorter observed serial interval due to right censoring ‘bias’.Reference Zhao, Gao and Zhuang6,Reference Nishiura, Linton and Akhmetzhanov7 As such, we call the observed serial interval under the effects of control measures the effective serial interval, which has a mean of 5.2 days from our data set. This result appears slightly but not significantly shorter than the previous estimated ‘intrinsic’ serial interval, with a mean of 7.5 days.Reference Li, Guan, Wu and Wang1 The mechanism behind the gender difference remains unknown, but it may be partly due to the fact that male cases are more severe than female cases (ie, “officials recorded a 2.8% fatality rate for male patients versus 1.7% for female patients”8). The findings regarding the serial intervals of COVID-19 in Hong Kong and Shenzhen, and their implications, warrant further investigation.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/ice.2020.64

Acknowledgments

We acknowledge the assistance of Cindy Y. Tian, Chinese University of Hong Kong, with the reference list. The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; nor in the decision to submit the manuscript for publication.

Financial support

D.H. was supported by General Research Fund (grant no. 15205119) of Research Grants Council of Hong Kong, China and an Alibaba (China) – Hong Kong Polytechnic University Collaborative Research project. W.W. was supported by National Natural Science Foundation of China (grant no. 61672013) and Huaian Key Laboratory for Infectious Diseases Control and Prevention (grant no. HAP201704), Huaian, Jiangsu, China.

Conflicts of interest

D.H. was supported by an Alibaba (China) – Hong Kong Polytechnic University Collaborative Research project. Other authors declare no competing interests.

References

Li, Q, Guan, X, Wu, P, Wang, X, et al.Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med 2020. doi: 10.1056/NEJMoa2001316.CrossRefGoogle ScholarPubMed
Wu, JT, Leung, K, Leung, GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 2020;395:689697.10.1016/S0140-6736(20)30260-9CrossRefGoogle ScholarPubMed
Fine, PEM. The interval between successive cases of an infectious disease. Am J Epidemiol 2003;158:10391047.10.1093/aje/kwg251CrossRefGoogle ScholarPubMed
The collection of press releases. Centre for Health Protection of Hong Kong website. https://www.chp.gov.hk/en/media/116/index.html. Accessed March 9, 2020.Google Scholar
The collection of outbreak situation reports of COVID-19 in Shenzhen. Shenzhen Municipal Health Commission website. http://wjw.sz.gov.cn/yqxx/. Accessed March 9, 2020.Google Scholar
Zhao, S, Gao, D, Zhuang, Z, et al. Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020. medRxiv 2020:2020.2002.2021.20026559.10.1101/2020.02.21.20026559CrossRefGoogle Scholar
Nishiura, H, Linton, NM, Akhmetzhanov, AR. Serial interval of novel coronavirus (2019-nCoV) infections. medRxiv 2020:2020.2002.2003.20019497.10.1101/2020.02.03.20019497CrossRefGoogle Scholar
Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi. 2020;41:145151.Google Scholar
Figure 0

Fig. 1. The observed (dots and bars) and fitted (curves) serial interval of COVID-19. The results of model (1) are shown in panel (a), and those of model (2) are shown in panel (b). In both panels, the red represents the female primary cases, and the blue represents the male primary cases. The dots are the observed (or median) serial interval, and the bars are the ranges of serial intervals for multiple primary cases. The bold curves are the fitting results and the dashed curves are the 95% confidence intervals.

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