UC Berkeley

Escape mutations (EM) to SARS-Cov-2 have been detected and are spreading. Vaccines may need adjustment to respond to these or future mutations. We designed a population level model integrating both waning immunity and EM. We also designed a set of criteria for elaborating and fitting this model to cross-neutralization and other data in a manner that minimizes vaccine decision errors. We formulated four model variations. These define criteria for which prior infections provide immunity that can be escaped. They also specify different sequences where one EM follows another. At all reasonable parameter values, these model variations led to patterns where: 1) EM were rare in the first epidemic, 2) rebound epidemics after the first epidemic were accelerated more by increasing drifting than by increasing waning (with some exceptions), 3) the long term endemic level of infection was determined mostly by waning rates with small effects of the drifting parameter, 4) EM caused loss of vaccine effectiveness and under some conditions, vaccines induced EM that caused higher levels of infection with vaccines than without them. The differences and similarities across the four models suggest paths for developing models specifying the epitopes where EM act. This model is a base on which to construct epitope specific evolutionary models using new high-throughput assay data from population samples to guide vaccine decisions.

Highlights

This model is the first to integrate both antigenic drifting from escape mutations and immunity waning in continuous time. Tiny amounts of only waning or only escape mutation drifting have small or no effects. Together, they have large effects. There are no or few escape mutations during the first epidemic peak and no effect of drifting parameters on the size of that wave. After the first epidemic peak, escape mutations accumulate rapidly. They increase with increases in waning rates and with increases in the drifting rate. Escape mutations then amplify other escape mutations since these raise the frequency of reinfections. Escape mutations can completely negate the effects of vaccines and even lead to more infections with vaccination than without, especially at very low waning rates. The model generates population level cross-neutralization patterns that enable the model to be fitted to population level serological data. The model can be modified to use laboratory data that determine the epitope specific effects of mutations on ACE2 attachment strength or escape from antibody effects. The model, although currently unable to predict the effects of escape mutations in the real world, opens up a path that can guide model incorporation of molecularly studied escape mutations and improve predictive value. We describe that path. Model analysis indicates that vaccine trials and serological surveys are needed now to detect the effects of epitope specific escape mutations that could cause the loss of vaccine efficacy.