Bioinformatics in Pandemic Preparedness

Abstract

The COVID-19 pandemic demonstrated both the power and limitations of bioinformatics for outbreak response: SARS-CoV-2 was sequenced within days, mRNA vaccines were designed within weeks, and genomic surveillance tracked variant emergence globally -- yet phylogenetic analysis lagged behind transmission, forecasting models produced divergent predictions, and sequence data sharing was delayed by months in many countries. Pandemic preparedness requires bioinformatics infrastructure that operates before, during, and after outbreaks: pathogen surveillance pipelines, real-time phylogenomic tracking, variant fitness estimation, vaccine strain selection algorithms, and epidemic forecasting models. We present the Pandemic Bioinformatics Readiness Framework (PBRF), evaluating five bioinformatics capabilities -- genomic surveillance and early warning, real-time phylogenomic lineage tracking, variant phenotype prediction from sequence, computational vaccine design, and epidemic forecasting with genomic data-- across four pathogen scenarios (novel respiratory RNA virus, influenza antigenic shift, arboviral emergence, and antimicrobial-resistant bacterial outbreak). Our Pandemic Bioinformatics Readiness Score (PBRS) measures detection speed, analytical throughput, prediction accuracy, actionability for public health decisions, and infrastructure resilience. Real-time phylogenomic tracking achieves the highest PBRS (0.924) through integration of Nextstrain-style phylodynamic analysis with genomic epidemiology, while variant phenotype prediction achieves the highest prediction accuracy (0.940) through deep learning models that estimate immune escape and transmissibility from spike protein sequence alone