A simple ecological model captures the transmission pattern of the coronavirus COVID-19 outbreak in China

  1. Feng Zhang
  2. Jinmei Zhang
  3. Menglan Cao
  4. Cang Hui

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Abstract

The rapid spread of the 2019 novel coronavirus (COVID-19), initially reported in the city of Wuhan in China, and quickly transmitted to the entire nation and beyond, has become an international public health emergency. Estimating the final number of infection cases and the turning point (time with the fastest spreading rate) is crucial to assessing and improving the national and international control measures currently being applied. In this paper we develop a simple model based on infectious growth with a time-varying infection rate, and estimate the final number of infections and the turning point using data updated daily from 3 February 2020, when China escalated its initial public health measures, to 10 February. Our model provides an extremely good fit to the existing data and therefore a reasonable estimate of the time-varying infection rate that has largely captured the transmission pattern of this epidemic outbreak. Our estimation suggests that (i) the final number of infections in China could reach 78,000 with an upper 95% confidence limit of 88,880; (ii) the turning point of the fastest spread was on the 4 th or the 5 th of February; and (iii) the projected period for the end of the outbreak (i.e., when 95% of the final predicted number of infection is reached) will be the 24 th of February, with an upper 95% confidence limit on the 19 th of March. This suggests that the current control measures in China are excellent, and more than sufficient to contain the spread of this highly infectious novel coronavirus, and that the application of such measures could be considered internationally for the global control of this outbreak.

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    SciScore for 10.1101/2020.02.27.20028928: (What is this?)

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    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  2. Version published to 10.1101/2020.02.27.20028928v1 on medRxiv