Abstract

Introduction

RNA viruses continue to cause unpredictable outbreaks, challenging public health systems worldwide. While high-throughput sequencing enables detailed viral analysis, integrating genomic data with temporal and geographic metadata to generate actionable insights for public health decision-making remains a practical challenge, especially in resource-limited settings. To address this, we propose a scalable, reproducible, and automated computational pipeline for tracking RNA virus mutations across time and regions, using Zika virus as a model. The pipeline emphasizes rapid interpretation of genomic data to support outbreak preparedness and timely public health interventions.

Methods

The pipeline automates RNA virus genomic analysis, including sequence quality assessment, read preprocessing, reference-based genome mapping, consensus sequence generation, variant detection, multiple sequence alignment, and phylogenetic reconstruction. Temporal and geographic metadata are incorporated to stratify sequences by sampling date and location. Downstream analyses will provide visual summaries of mutation frequencies, lineage distributions, and recurrent variants, with outputs designed for interpretation and actionable use by public health authorities.

Results

Expected outputs include high-resolution phylogenetic trees, identification of mutations showing temporal or geographic patterns, and interactive dashboards summarizing lineage dynamics. These outputs can serve as early-warning signals for emerging outbreaks, informing targeted interventions, vaccination strategies, and resource allocation. By combining genomic and epidemiological data in a reproducible framework, the pipeline is designed to enhance timely, evidence-based public health responses.

Conclusion

This proposed genomic surveillance pipeline offers a reproducible and scalable approach for RNA virus monitoring. Its implementation could strengthen early detection of emerging variants, support targeted outbreak responses, and inform public health decision-making, particularly in resource-limited contexts. By translating genomic data into actionable insights, the pipeline has the potential to improve epidemic preparedness, guide allocation of health resources, and inform policy strategies to mitigate viral outbreaks.