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Supporting materials for Nishiura et al "Serial interval of novel coronavirus (COVID-19) infections" Submitted 2020
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README.md

Estimation of the serial interval of novel coronavirus (COVID-19) infections

Supporting materials for Nishiura H, Linton NM, Akhmetzhanov AR 2020 "Serial interval of novel coronavirus (COVID-19) infections" Submitted

Our repository consists of the following parts:

1. Inference of the serial interval using only infector-infectee pairs identified from the literature with high certainty

2. Inference of the serial interval using only infector-infectee pairs identified as certain or probable

3. Generating Figure 1

4. Additional details

  • The folder data contains the supplementary table used for our analysis.
  • The folder results contains all constructed traceplots, as well as individual traces for the parameters of the distributions. As such, param1 & param2 in csv or pickle files are respectively the meanlog and sdlog for the lognormal distribution, the shape and inverse scale for the gamma distribution, and the shape and scale for the Weibull distribution. We followed the notation according to Stan manual (see the pages on lognormal, gamma, and Weibull distributions). The scipy.stats notation for each distribution follows the code: ss.lognorm(param2,scale=np.exp(param1)), ss.gamma(param1,scale=1/param2), and ss.weibull_min(param1,scale=param2), respectively. Here ss and np are shortcuts for scipy.stats and numpy libraries in Python.

Thank you for your interest to our work!

Few words of caution: We would like to note that our code is not supposed to work out of box, because the links used in the notebooks were user-specific, and our main intent was to show the relevance of the methods used in our paper.

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