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Epidemic modelling

HT Huaiyu Tian

Last updated date: Feb 21, 2021 Views: 1028 Forks: 0

original An abbreviated version of this protocol was published in Science in May, 2020
An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China
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Dear Dr. Joey Ying Zhou,

Thanks for your letter!

We had provided the description both in supplemental material and read me files.

Please find it in:

Model fitting was performed using the Metropolis–Hastings Markov chain Monte Carlo (MCMC) algorithm with the MATLAB (version R2016b) toolbox DRAM (Delayed Rejection Adaptive Metropolis).

Notes on the code (https://github.com/huaiyutian/COVID-19_TCM-50d_China)
To run, you need a Matlab toolbox called "DRAM": DRAM is a combination of two ideas for improving the efficiency of Metropolis-Hastings type Markov chain Monte Carlo (MCMC) algorithms, Delayed Rejection and Adaptive Metropolis. This page explains the basic ideas behind DRAM and provides examples and Matlab code for the computations.(see http://helios.fmi.fi/~lainema/dram/).

Please do not hesitate to contact me if you have any questions.

Best,

Huaiyu

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How to cite：
Readers should cite both the Bio-protocol preprint and the original research article where this protocol was used:
  1. Tian, H(2021). Epidemic modelling. Bio-protocol Preprint. bio-protocol.org/prep857.
  2. Tian, H., Liu, Y., Li, Y., Wu, C., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., Xu, B., Yang, Q., Wang, B., Yang, P., Cui, Y., Song, Y., Zheng, P., Wang, Q., Bjornstad, O. N., Yang, R., Grenfell, B. T., Pybus, O. G. and Dye, C.(2020). An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China . Science 368(6491). DOI: 10.1126/science.abb6105

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