Designing vaccines is an expensive and time consuming process. This talk demonstrates how we can exploit automatic differentiation of ODEs, parallelization, stochastic search and Bayesian optimization to minimize post-vaccination invasive pneumococcal disease and antibiotic resistant strains in a bacteria population using a novel computational model of the bacterial population dynamics that integrates epidemiological and genomic data.
Streptococcus pneumoniae (the pneumococcus) is a common nasopharyngeal bacterium that can cause invasive pneumococcal disease (IPD). Each component of current vaccines generally induce immunity to one of the approximately 100 pneumococcal types. Overall carriage rates remain similar to pre-vaccination as the serotypes not affected by the vaccine will replace the affected ones. Selecting which serotypes to target to minimize the post-vaccine IPD burden is a challenging combinatorial problem involving a large ODE system describing the population dynamics of the bacteria in response to each proposed vaccine. This talk describes how I have approached this problem using automatic differentiation, parallelized evaluation of the ODEs, stochastic search and Bayesian optimization. Here is a link to the paper this work is based on: https://www.nature.com/articles/s41564-019-0651-y.