Doubling Time of the COVID-19 Epidemic by Chinese Province

Abstract

COVID-19 epidemic doubling time by Chinese province was increasing from January 20 through February 9, 2020. The harmonic mean of the arithmetic mean doubling time estimates ranged from 1.4 (Hunan, 95% CI, 1.2–2.0) to 3.1 (Xinjiang, 95% CI, 2.1–4.8), with an estimate of 2.5 days (95% CI, 2.4–2.6) for Hubei.

Our ability to estimate the basic reproduction number of emerging infectious diseases is often hindered by the paucity of information about the epidemiological characteristics and transmission mechanisms of new pathogens (1). Alternative metrics could synthesize real-time information about the extent to which the epidemic is expanding over time. Such metrics would be particularly useful if they rely on minimal and routinely collected data that capture the trajectory of an outbreak (2).

Epidemic doubling times characterize the sequence of intervals at which the cumulative incidence doubles (3). If an epidemic is growing exponentially with a constant growth rate r, the doubling time remains constant and equals to (ln 2)/r. An increase in the doubling time indicates a slowdown in transmission if the underlying reporting rate remains unchanged (Technical Appendix) (4).

Here we analyzed by province the number of times COVID-19 cumulative incidence doubled and the evolution of the doubling times in mainland China (5), from January 20 (when nationwide reporting began) through February 9, 2020. Province-level daily cumulative incidence data were retrieved from provincial health commissions’ websites. Two sensitivity analyses based on a longer and a shorter time period respectively were conducted (Technical Appendix). Tibet was excluded from further analysis because there had only been one case reported during the study period.

From January 20 through February 9, the harmonic mean of the arithmetic means of the doubling times estimated from cumulative incidence ranged from 1.4 (95% CI, 1.2, 2.0) days (Hunan) to 3.1 (95% CI, 2.1, 4.8) days (Xinjiang). In Hubei, it was estimated as 2.5 (95% CI, 2.4, 2.6) days. The cumulative incidence doubled 6 times in Hubei. The harmonic mean of the arithmetic means of doubling times in all of mainland China except Hubei was 1.8 (95% CI, 1.5, 2.3) days. Provinces with a harmonic mean of the arithmetic means of doubling times <2d included Fujian, Guangxi, Hebei, Heilongjiang, Henan, Hunan, Jiangxi, Shandong, Sichuan, and Zhejiang (Figures 1 and S1).

Figure 1.

The map of the harmonic mean of the arithmetic means of doubling time estimates (Panel A) and the number of times the COVID-19 epidemic cumulative incidence has doubled (Panel B) by province in mainland China, from January 20 through February 9, 2020.

As the epidemic progressed, it took longer for the cumulative incidence in mainland China (except Hubei) to double itself, which indicated an overall sub-exponential growth pattern outside Hubei (Figures S1, S2). In Hubei, the doubling time decreased and then increased. A gradual increase in the doubling time coincided with the social distancing measures and intra-and-inter-provincial travel restrictions imposed across China since the implementation of quarantine of Wuhan on January 23 (6).

Our estimates of doubling times are shorter than prior estimates of 7.4 days (95% CI, 4.2–14) (5), 6.4 days (95% CrI, 5.8–7.1) (7), and 7.1 days (95% CI, 3.0–20.5) (8) respectively. Li et al. covered cases reported by January 22 (5). Wu et al. statistically inferred case counts in Wuhan by internationally exported cases as of January 25 (7). Volz et al. identified a common viral ancestor on December 8, 2019 using Bayesian phylogenetic analysis and fitted an exponential growth model to provide the epidemic growth rate (8). Our estimates are based on cumulative confirmed case count by reporting date by province from January 20 through February 9.

Our study is subject to limitations, including underreporting of cases (9). One reason for underreporting is underdiagnosis, due to lack of diagnostic tests, healthcare workers, and other resources. Further, underreporting is likely heterogeneous across provinces. As long as reporting remains invariant over time within the same province, the calculation of doubling times remains reliable; however, this is a strong assumption. Growing awareness of the epidemic and increasing availability of diagnostic tests might have strengthened reporting over time, which could have artificially shortened the doubling time. Nevertheless, apart from Hubei and Guangdong (first case reported on January 19), nationwide reporting only began on January 20, and at this point, Chinese authorities had openly acknowledged the magnitude and severity of the epidemic. Due to a lack of detailed case data describing incidence trends for imported and local cases, we focused our analysis on the overall trajectory of the epidemic without adjusting for the role of imported cases on the local transmission dynamics. Indeed, it is likely that the proportion of imported cases was significant for provinces that only reported a few cases; their short doubling times in the study period could simply reflect rapid detection of imported cases. However, with the data until February 9, only two provinces had a cumulative case count of <40 (Table S1). It would be interesting to investigate the evolution of the doubling time after accounting for case importations if more detailed data becomes available.

To conclude, we observed an increasing trend in the epidemic doubling time of COVID-19 by Chinese province from January 20 through February 9, 2020. The harmonic mean of the arithmetic means of doubling times of cumulative incidence in Hubei during the study period was estimated at 2.5 (95% CI, 2.4, 2.6) days.

Author Bio

Kamalich Muniz-Rodriguez, MPH, is a doctoral student at the Jiann-Ping Hsu College of Public Health, Georgia Southern University. Her research interests include infectious disease epidemiology, digital epidemiology and disaster epidemiology.

Gerardo Chowell, PhD, is Professor of Epidemiology and Biostatistics, and Chair of the Department of Population Health Sciences at Georgia State University School of Public Health. As a mathematical epidemiologist, Prof Chowell studies the transmission dynamics of emerging infectious diseases, such as Ebola, MERS and SARS.