Additional file 1 of RBD-specific antibody responses after two doses of BBIBP-CorV (Sinopharm, Beijing CNBG) vaccine

Additional file 1: Figure S1. Effects of the age and the sex of the subject, and the time-period between the vaccination and the measurement on the probability of the lack of RBD-specific antibody production (titre below 1) after two doses of the Sinopharm vaccine using logistic regression model. 90% credible interval is shown for males, 28 days post second dose. Figure S2. Effects of the age and the sex of the subject on the probability of the lack of RBD-specific antibody production (titre below 1) after two doses of the Pfizer/BioNTech vaccine using logistic regression model. 90% credible interval is shown for males, 28 days post second dose. Figure S3. Sinopharm vaccine model, MCMC diagnostics: density plot for the hurdle-lognormal model. Figure S4. Sinopharm vaccine model, MCMC diagnostics: density plot for the logistic model. Figure S5. Sinopharm vaccine model, MCMC diagnostics: trace plot for the hurdle-lognormal model. Figure S6. Sinopharm vaccine model, MCMC diagnostics: trace plot for the logistic model. Figure S7. Sinopharm vaccine model, MCMC diagnostics: autocorrelation function for the hurdle-lognormal model. Figure S8. Sinopharm vaccine model, MCMC diagnostics: autocorrelation function for the logistic model. Figure S9. Sinopharm vaccine model, MCMC diagnostics: posterior predictive check for the hurdle-lognormal model. Figure S10. Sinopharm vaccine model, MCMC diagnostics: posterior predictive check for the logistic model. Figure S11. Pfizer/BioNTech vaccine model, MCMC diagnostics: density plot for the hurdle-lognormal model. Figure S12. Pfizer/BioNTech vaccine model, MCMC diagnostics: density plot for the logistic model. Figure S13. Pfizer/BioNTech vaccine model, MCMC diagnostics: trace plot for the hurdle-lognormal model. Figure S14. Pfizer/BioNTech vaccine model, MCMC diagnostics: trace plot for the logistic model. Figure S15. Pfizer/BioNTech vaccine model, MCMC diagnostics: autocorrelation function for the hurdle-lognormal model. Figure S16. Pfizer/BioNTech vaccine model, MCMC diagnostics: autocorrelation function for the logistic model. Figure S17. Pfizer/BioNTech vaccine model, MCMC diagnostics: posterior predictive check for the hurdle-lognormal model. Figure S18. Pfizer/BioNTech vaccine model, MCMC diagnostics: posterior predictive check for the logistic model.

附加文件1：图S1。采用逻辑回归模型（logistic regression model）分析两剂国药（Sinopharm）疫苗接种后，受试者年龄、性别以及接种至检测的时间间隔对RBD特异性抗体产生缺失（滴度低于1）概率的影响。其中展示了男性受试者在第二剂接种后28天时的90%可信区间。图S2。采用逻辑回归模型（logistic regression model）分析两剂辉瑞（Pfizer）/生物泰克（BioNTech）疫苗接种后，受试者年龄、性别对RBD特异性抗体产生缺失（滴度低于1）概率的影响。其中展示了男性受试者在第二剂接种后28天时的90%可信区间。图S3。国药疫苗模型的马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）诊断：障碍-对数正态模型（hurdle-lognormal model）的密度图。图S4。国药疫苗模型的MCMC诊断：逻辑回归模型的密度图。图S5。国药疫苗模型的MCMC诊断：障碍-对数正态模型的轨迹图（trace plot）。图S6。国药疫苗模型的MCMC诊断：逻辑回归模型的轨迹图。图S7。国药疫苗模型的MCMC诊断：障碍-对数正态模型的自相关函数（autocorrelation function）图。图S8。国药疫苗模型的MCMC诊断：逻辑回归模型的自相关函数图。图S9。国药疫苗模型的MCMC诊断：障碍-对数正态模型的后验预测检验（posterior predictive check）。图S10。国药疫苗模型的MCMC诊断：逻辑回归模型的后验预测检验。图S11。辉瑞（Pfizer）/生物泰克（BioNTech）疫苗模型的MCMC诊断：障碍-对数正态模型的密度图。图S12。辉瑞（Pfizer）/生物泰克（BioNTech）疫苗模型的MCMC诊断：逻辑回归模型的密度图。图S13。辉瑞（Pfizer）/生物泰克（BioNTech）疫苗模型的MCMC诊断：障碍-对数正态模型的轨迹图。图S14。辉瑞（Pfizer）/生物泰克（BioNTech）疫苗模型的MCMC诊断：逻辑回归模型的轨迹图。图S15。辉瑞（Pfizer）/生物泰克（BioNTech）疫苗模型的MCMC诊断：障碍-对数正态模型的自相关函数图。图S16。辉瑞（Pfizer）/生物泰克（BioNTech）疫苗模型的MCMC诊断：逻辑回归模型的自相关函数图。图S17。辉瑞（Pfizer）/生物泰克（BioNTech）疫苗模型的MCMC诊断：障碍-对数正态模型的后验预测检验。图S18。辉瑞（Pfizer）/生物泰克（BioNTech）疫苗模型的MCMC诊断：逻辑回归模型的后验预测检验。