Infectious diseases are still a major source of human mortality throughout the world, contributing to over a quarter of global mortality, of which 90% are caused by acute respiratory infections, diarrheal diseases, malaria, AIDS, tuberculosis, and measles. COVID-19 pandemic has caused >7 million deaths worldwide. To ultimately control infectious diseases, effective and safe vaccines are required. For rational vaccine design, it is critical to have a deep understanding of infectious disease mechanisms and protective immunity. In the era of big data, various Omics and AI technologies have been developed to generate large amounts of data in the areas of vaccines and infectious diseases. To better use these data, it is crucial to develop standards and tools to collect, standardize, annotate, and analyze these data for addressing various scientific issues in the important field.

The goal of this Research Topic is to publish research papers in the domain of developing and/or applying informatics strategies, methods, and tools to support infectious disease and vaccine research and development. Research on various studies is welcome. The scope of the research topic is broad and covers various areas including basic infectious disease and immune mechanism studies, translational vaccine design and development, clinical trial research, electronic health record annotation and analysis, and vaccine safety surveillance, leading to our enhanced understanding of infectious diseases and vaccine mechanisms and effective control of infectious diseases. We also wish to use this Research Topic as a platform to share research progress, discuss new results and ideas, and promote collaborations.

We accept papers in the wide scope of applying informatics for infectious disease and vaccine studies. The following items are examples of the papers fit in our scope:
1) Literature mining, using natural language processing (NLP), AI, and Large language model (LLM) (e.g., ChatGPT).
2) Omics data collection and analysis. Omics technologies include genomics, transcriptomics, proteomics, metabolomics, interactomics, and microbiomics, etc.
3) Clinical data analysis. Example clinical data include electronic health records (EHR) and clinical trial data.
4) Reverse and structural vaccinology. Proteomic sequences and structures are popularly used for vaccine design.
5) Immunoinformatics. Example topics are immune epitope prediction, HLA gene analysis, MHC characterizing, in silico vaccine design, mathematical modeling of host-pathogen interactions, and immune network analysis.
6) Post-licensure vaccine surveillance, such as vaccine adverse events data analysis.
7) Data and knowledge standardization and integration: Data requires FAIRness (Findable, Accessible, Interoperable and Reusable), which can be supported by standardized terminologies and ontologies.
8) Molecular modelling of antibody-antigen interactions.
9) Mathematical modeling.

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Frontiers in Cellular and Infection Microbiology

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