Abstract

Respiratory infections caused by coronaviruses have posed serious and unpredictably public health threats; reliable animal models continue to be essential for advancing our understanding of the virus's transmission, pathophysiology, and immunological mechanisms. In response to the critical need for centralized resources in coronavirus research, the COVID-AMD database (Coronavirus Disease Animal Model Database, https://www.uc-med.net/CoV-AMD ) has been developed as an integrated platform. Data was gathered from public literature databases, refined and integrated using ETL (Extract, Transform, Load) methodology. After data conversion and cleaning, COVID-AMD was implemented using MySQL relational database with jQuery and JBoss. COVID-AMD database consolidates comprehensive data on animal models infected with various CoVs, including MERS-CoV, SARS-CoV, and SARS-CoV-2, featuring methodologies for establishing infection models, clinical features, and phenotypic data. It catalogs 869 animal models across 29 species and 312 virus strains, covering five diseases and ten infection routes. With global and advanced search capabilities, it facilitated data preprocessing, integration, analysis, and visualization, and provided tools for comparative analysis, model recommendation and omics analysis based on model and phenotype data. The open access to this rich repository aims to enable rapid identification of animal models for CoVs, thereby accelerating the development and clinical trial progression of prospective therapeutics and vaccines.