Modelling SARS-CoV-2 Dynamics: Implications for Therapy

Kwang Su Kim; Keisuke Ejima; Yusuke Ito; Shoya Iwanami; Hirofumi Ohashi; Yoshiki Koizumi; Yusuke Asai; Shinji Nakaoka; Koichi Watashi; Robin N Thompson; Shingo Iwami

The copyright holder for this preprint (which was not peer-reviewed) is . https://doi.org/10.1101/2020.03.23.20040493 doi: medRxiv preprint patient variability. Including a random effect amounts to a partial pooling of the data 323 between individuals to improve estimates of the parameters applicable across the 324 population of patients. By using this approach, the differences between viral 325 dynamics in different patients were not estimated explicitly, nor did we fully pool the 326 data which would bias estimates towards highly sampled patients. In this method of 327 estimation, each parameter estimate (= × ) depends on the individual where 328 is fixed effect, and is random effect with an assumed Gaussian distribution with 329 mean 0 and standard deviation Ω. Population parameters and individual parameters 330 were estimated using the stochastic approximation expectation-maximisation 331 algorithm and empirical Bayes' method, respectively. Individual estimated 332 parameters and initial values for patients are summarized in Table S1 . Using parameters were compared and tested using the bootstrap t-test. Due to the small 340 sample size for viral loads in sputum/tracheal aspirate for MERS-CoV, we assumed 341 the fixed effect was the same for mild and severe MERS-CoV cases. Otherwise, the 342 process was exactly the same as that described for the throat swab data. 343