Issues in COVID-19 research and statistical analyses (Part XVIV) December 9, 2021
The new variant for Omicron is causing a lot of stir around the world. Most of the data analysis conducted thus far has been preliminary and other planned data analyses are taking weeks in order to understand the potential severity of the variant and its effect on efficacy. A preprint article coming out of South Africa has been a primary source of information from the data analysis and results they reported on this new variant. They conducted retrospective analysis of routine epidemiological surveillance data on SARS-CoV-2 that has specimen receipt dates between 04 March 2020 and 27 November 2021, collected through South Africa’s National Notifiable Medical Conditions Surveillance System. Their statistics section is very confusing in that they don’t actually state what models they used but we are lead to infer through various terms they dropped. They do not actually state how they decided to calculate the probability of infection. They use some reinfection hazard coefficient, lambda. They state they used a model fitted to observed reinfection incidence through 30 September 2020 or 28 February 2021, assuming data are negative binomially-distributed with a mean. They then fitted the lambda and the inverse of the negative binomial dispersion parameter to the data using a Metropolis-Hastings Monte Carlo Markov Chain (MCMC) estimation procedure implemented in the R Statistical Programming Language. Next they discuss convergence of the model, but never once stated what model. Even if they are using Bayesian techniques, there is still some model to be stated here which they do not. Then once they fit the joint posterior distribution they were able to simulate a time series for time to reinfection.