Analyses to predict the efficacy and waning of vaccines and previous infection against transmission and clinical outcomes of SARS-CoV-2 variants.

This repo contains analyses to:

• fit a Bayesian model of the relationship between neutralising antibody titres and protective efficacies of vaccines and prior infection
• use this model to predict VEs for other outcomes, vaccines, and waning
• infer the plausible range of immune escape and intrinsic transmissibility for Omicron

I'm planning to extend it to:

• use a next generation matrix approach to predict the reduction in transmission achievable by different levels of vaccine efficacy against acquisition and onward transmission.

See the Methods Description for a technical explanation of the analyses.

This model is a Bayesian implementation of the model described by Khoury et al. 2020 Nature Medicine and used in Cromer et al. 2021 Lancet Microbe. The code for that work can be found here and here. If you want to refer to the analyses in this repo, you should probably be citing them.

The omicron analysis relies on detailed, rapid analyses of the ratio of reproduction numbers for Omicron and Delta (here), and the increasing risk of reinfection (here) in South Africa, by South African scientists as part of SACMC.

It is also informed by vaccine efficacy estimates against Delta and Omicron from the UK. VE estimates for Delta are presented in: Andrews et al. (2021a) for the population-level efficacy of the Pfizer and AstraZeneca vaccines (two doses) against clinical outcomes (death, severe disease, symptomatic infection) from the Delta variant over different periods of time post-administration, Pouwels et al. (2021) and Eyre et al. (2021) for the efficacy against acquisition (symptomatic or asymptomatic) and onward transmission of breakthrough infections, respectively, of the Delta variant. VE estimates against Omicron are given in: Andrews et al. (2021b) and the UK variant analyses.

To run the code in this repo, you will need to install the packages listed in packages.R, including the most recent Github version of greta. greta links to python and depends on TensorFlow so can be tricky to install. It's best to follow the instructions greta gives you, and look for help on the greta forum if you have any trouble.

Then you run the whole analysis using the targets package function tar_make() to run the whole analysis; fittng the model, producing validation statistics, producing plots, and outputting predictions.