Abstract
Background: The 2019 novel Coronavirus (COVID-19) emerged in Wuhan, China in December 2019 and has been spreading rapidly in China. Decisions about its pandemic threat and the appropriate level of public health response depend heavily on estimates of its basic reproduction number and assessments of interventions conducted in the early stages of the epidemic. Methods: We conducted a mathematical modeling study using five independent methods to assess the basic reproduction number (R0) of COVID-19, using data on confirmed cases obtained from the China National Health Commission for the period 10th January – 8th February. We analyzed the data for the period before the closure of Wuhan city (10th January – 23rd January) and the post-closure period (23rd January – 8th February) and for the whole period, to assess both the epidemic risk of the virus and the effectiveness of the closure of Wuhan city on spread of COVID-19. Findings: Before the closure of Wuhan city the basic reproduction number of COVID-19 was 4.38 (95% CI: 3.63 – 5.13), dropping to 3.41 (95% CI: 3.16 – 3.65) after the closure of Wuhan city. Over the entire epidemic period COVID-19 had a basic reproduction number of 3.39 (95% CI: 3.09 – 3.70), indicating it has a very high transmissibility. Interpretation: COVID-19 is a highly transmissible virus with a very high risk of epidemic outbreak once it emerges in metropolitan areas. The closure of Wuhan city was effective in reducing the severity of the epidemic, but even after closure of the city and the subsequent expansion of that closure to other parts of Hubei the virus remained extremely infectious. Emergency planners in other cities should consider this high infectiousness when considering responses to this virus. Funding Statement: JL is supported by the National Natural Science Foundation of China (grant ID 81803334) and China Medical Board (grant ID 18-301). JG is supported by the National Natural Science Foundation of China (grant ID 71774178). CH is supported by the National Natural Science Foundation of China (grant ID 71974212). YH is supported by the National Natural Science Foundation of China (grant ID 81973150) and the National Science and Technology Major Project of China (grant ID 2018ZX10715004). Declaration of Interests: The authors have no conflict of interests to declare.
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