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Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China

Tao LiuiD, Jianxiong Hu, Jianpeng Xiao, Guanhao He, Min Kang, Zuhua Rong, Lifeng Lin, Haojie Zhong, Qiong Huang, Aiping Deng, Weilin Zeng, Xiaohua Tan, Siqing Zeng, Zhihua Zhu, Jiansen Li, Dexin Gong, Donghua Wan, Shaowei Chen, Lingchuan Guo, Yan LiiD, Limei Sun, Wenjia Liang, Tie Song, Jianfeng He, Wenjun Ma

Abstract

RationaleSeveral studies have estimated basic production number of novel coronavirus pneumonia (NCP). However, the time-varying transmission dynamics of NCP during the outbreak remain unclear. ObjectivesWe aimed to estimate the basic and time-varying transmission dynamics of NCP across China, and compared them with SARS. MethodsData on NCP cases by February 7, 2020 were collected from epidemiological investigations or official websites. Data on severe acute respiratory syndrome (SARS) cases in Guangdong Province, Beijing and Hong Kong during 2002-2003 were also obtained. We estimated the doubling time, basic reproduction number (R0) and time-varying reproduction number (Rt) of NCP and SARS. Measurements and main resultsAs of February 7, 2020, 34,598 NCP cases were identified in China, and daily confirmed cases decreased after February 4. The doubling time of NCP nationwide was 2.4 days which was shorter than that of SARS in Guangdong (14.3 days), Hong Kong (5.7 days) and Beijing (12.4 days). The R0 of NCP cases nationwide and in Wuhan were 4.5 and 4.4 respectively, which were higher than R0 of SARS in Guangdong (R0=2.3), Hongkong (R0=2.3), and Beijing (R0=2.6). The Rt for NCP continuously decreased especially after January 16 nationwide and in Wuhan. The R0 for secondary NCP cases in Guangdong was 0.6, and the Rt values were less than 1 during the epidemic. ConclusionsNCP may have a higher transmissibility than SARS, and the efforts of containing the outbreak are effective. However, the efforts are needed to persist in for reducing time-varying reproduction number below one. At a Glance CommentaryO_ST_ABSScientific Knowledge on the SubjectC_ST_ABSSince December 29, 2019, pneumonia infection with 2019-nCoV, now named as Novel Coronavirus Pneumonia (NCP), occurred in Wuhan, Hubei Province, China. The disease has rapidly spread from Wuhan to other areas. As a novel virus, the time-varying transmission dynamics of NCP remain unclear, and it is also important to compare it with SARS. What This Study Adds to the FieldWe compared the transmission dynamics of NCP with SARS, and found that NCP has a higher transmissibility than SARS. Time-varying production number indicates that rigorous control measures taken by governments are effective across China, and persistent efforts are needed to be taken for reducing instantaneous reproduction number below one.

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Claude Opus 5.5 · xhigh effort · 17 Oct 2026 · from the full text of version 2 (13 Feb 2020)

Nationwide data to 7 February 2020 give R0 4.5 and 2.4-day doubling, faster on both counts than SARS, with Rt falling from 8.8 to 1.59 after mid-January.

Earlier reproduction-number estimates for the new coronavirus came from very early epidemic data or pure modelling, when case ascertainment was poor, and none tracked how transmissibility changed as controls were imposed. Here onset dates are reconstructed for the whole country and used to estimate a time-varying reproduction number, benchmarked against SARS data from Guangdong, Hong Kong and Beijing analysed with the same methods.

Key results

  1. ShownThe new outbreak spread faster than SARS on both headline measures: a higher basic reproduction number and a markedly shorter epidemic doubling time.

    R0 4.5 (95%CI 4.4-4.6) nationwide and 4.4 (4.3-4.6) in Wuhan, against 2.3 (2.0-2.7) in Guangdong, 2.3 (2.0-2.5) in Hong Kong and 2.6 (2.4-2.8) in Beijing for SARS. Doubling time 2.4 days nationwide, 2.8 in Wuhan and 3.6 in Guangdong, versus 14.3, 5.7 and 12.4 days for SARS in the three regions before their peaks. · Description of the outbreak; Estimation of basic reproduction number (R0)

  2. SuggestedThe instantaneous reproduction number rose until mid-January and then fell steadily, with reported cases turning down in early February, which the authors read as control measures taking effect.

    Nationwide Rt went from 6.9 (95%CI 5.5-8.4) before January 9 to a peak of 8.8 (8.3-9.4) before January 16, then down to 1.59 (1.57-1.61) before February 6; Wuhan fell from 6.2 (4.9-7.6) to 2.10 (2.07-2.14) over the same period. Daily confirmed cases peaked on February 4 nationwide and on January 31 in Guangdong. Rt used a 10-day moving window. · Estimation of time-varying reproduction number…; Description of the outbreak; Time-varying instantaneous reproduction number…

  3. ShownLocally acquired transmission in Guangdong stayed subcritical throughout, so the provincial epidemic was dominated by cases imported from Hubei.

    R0 for secondary cases in Guangdong was 0.6 (95%CI 0.4-0.7) and Rt stayed between 0.8 (0.6-1.1) and 0.2 (0.2-0.3); 220 of 1,075 provincial cases (20.5%) were secondary, including 2 infected via a shared elevator with neighbouring index cases. For comparison, R0 among imported cases there was 3.8 (3.4-4.2). · Estimation of basic reproduction number (R0); Estimation of time-varying reproduction number…; Description of the outbreak; Discussion

  4. ShownEstimates survive the two main analytic choices: reconstructing onset dates from reporting dates, and the assumed generation time.

    The generalized additive model mapping onset to reporting date was cross-validated within Guangdong (R2 89.7%, RMSE 4.11), and a Bayesian nowcasting step corrected for reporting delay. Varying the generation-time shape parameter moved nationwide R0 only from 4.0 (95%CI 3.9-4.1) at 7.0 days to 4.9 (4.8-5.1) at 8.0 days, around a prior with mean 7.5 days and standard deviation 3.4 days. · Estimating and nowcasting the daily incidence of…; Sensitivity analyses; Basic reproduction number (R0)

Methods, data and reproducibility

Daily reported case counts were converted to onset-date incidence with a generalized additive model fitted on individually investigated Guangdong cases, corrected for reporting delay by Bayesian nowcasting, then fitted with Poisson exponential growth for R0 and a 10-day sliding window for Rt; doubling time and R0 were computed the same way for historical SARS series.

  • Confirmed cases reported nationwide and abroad as of February 7, 2020, from official sources · human · n = 34,598 in 34 Chinese provinces and regions; 270 in 24 other countries
  • Hubei and Wuhan case counts used for the regional estimates · human · n = 24,953 in Hubei, of which 13,603 (54.5%) in Wuhan
  • Individual records with onset, hospitalisation and diagnosis dates from epidemiological investigations in Guangdong Province · human · n = 1,075 cases, 220 of them secondary
  • Historical SARS daily case series from Guangdong CDC, a published Beijing report and the WHO website (2002-2003) · human · n = 1,511 Guangdong, 1,734 Hong Kong, 2,521 Beijing
Methods & tools
R 3.6.0, NobBs, R0, EpiEstim, generalized additive model, Poisson regression
Reagents & environment
Analyses ran in R version 3.6.0 using the NobBs package for nowcasting, R0 for the basic reproduction number and EpiEstim for the time-varying reproduction number; no scripts or package versions beyond R itself are given.
Reproducibility gaps
No code is released. Data are available only from the corresponding author on request, after a proposal describing objectives and the analysis plan is approved; several supporting results sit in supplementary figures not included in this text.
Limitations (4)
  • Outside Guangdong, onset dates were imputed from reporting dates by the model rather than observed, which the authors note may introduce misclassification bias. · stated by the authors
  • The analysis covers an early stage of the outbreak, when epidemiological parameters affecting transmissibility were still uncertain. · stated by the authors
  • Case ascertainment changed over the period analysed (diagnosis protocols and surveillance were expanding), so part of the apparent rise in Rt to mid-January may reflect detection rather than transmission. · noted in this summary
  • The fall in Rt is compared with no counterfactual, and many interventions coincided, so the effect of any single measure cannot be separated. · noted in this summary

Who should read:Read this if you model outbreak transmissibility and want worked estimates of reproduction numbers over time from surveillance counts, with a same-method SARS comparison.

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