Prediction of post-PCV13 pneumococcal evolution using invasive disease data enhanced by inverse-invasiveness weighting.

Background: After introduction of pneumococcal conjugate vaccines (PCVs), serotype replacement occurred in the population of Predicting which pneumococcal clones and serotypes will become more common in carriage after vaccination can enhance vaccine design and public health interventions, while also improving our understanding of pneumococcal evolution. We sought to use invasive disease data to assess how well negative frequency-dependent selection (NFDS) models could explain pneumococcal carriage population evolution in the post-PCV13 epoch by weighting invasive data to approximate strain proportions in the carriage population.

Methods: Invasive pneumococcal isolates were collected and sequenced during 1998-2018 by the Active Bacterial Core surveillance (ABCs) from the Centers for Disease Control and Prevention (CDC). To predict the post-PCV13 population dynamics in the carriage population using a NFDS model, all genomic data were processed under a bioinformatic pipeline of assembly, annotation, and pangenome analysis to define genetically similar sequence clusters (i.e., strains) and a set of accessory genes present in 5% to 95% of the isolates. The NFDS model predicted the strain proportion by calculating the post-vaccine strain composition in the weighted invasive disease population that would best match pre-vaccine accessory gene frequencies. To overcome the biases of invasive disease data, serotype-specific inverse-invasiveness weights were defined as the ratio of the proportion of the serotype in the carriage data to the proportion in the invasive data, using data from 1998-2001 in the United States, before conjugate vaccine introduction. The weights were applied to adjust both the observed strain proportion and the accessory gene frequencies.

Results: Inverse-invasiveness weighting increased the correlation of accessory gene frequencies between invasive and carriage data with reduced residuals in linear or logit scale for pre-vaccine, post-PCV7, and post-PCV13. Similarly, weighting increased the correlation of accessory gene frequencies between different time periods in the invasive data. By weighting the invasive data, we were able to use the NFDS model to predict strain proportions in the carriage population in the post-PCV13 epoch, with the adjusted R-squared between predicted and observed strain proportions increasing from 0.176 to 0.544 after weighting.

Conclusions: The weighting system adjusted the invasive disease surveillance data to better represent the carriage population of . The NFDS mechanism predicted the strain proportions in the projected carriage population as estimated from the weighted invasive disease frequencies in the post-PCV13 epoch. Our methods enrich the value of genomic sequences from invasive disease surveillance, which is readily available, easy to collect, and of direct interest to public health.