معرفی
Professor Vitali Sintchenko is a public health microbiologist and informatician at The University of Sydney, affiliated with the School of Medical Sciences, Sydney Infectious Diseases Institute, and Westmead Institute for Medical Research. As a Clinical Microbiologist with NSW Health Pathology, he develops genomic epidemiology tools for communicable disease surveillance, focusing on tuberculosis, pertussis, and Salmonella. His work integrates pathogen genomics with clinical data to improve outbreak detection and control.
- MBBS, PhD, FRCPA, FACHI
- Member, Commonwealth Public Health Laboratory Network and Communicable Diseases Genomics Network
- Leadership in Mycobacterium Reference Laboratory and Enteric & Pneumococcal Reference Laboratories
Research spans:
- Genomic surveillance of epidemic bacterial pathogens
- Development of decision support systems for outbreak response
- Integration of syndromic surveillance with molecular epidemiology
- Microbiome analysis in respiratory infections
- Antimicrobial resistance genomics
- Machine learning applications in pathogen tracking
Recent publications demonstrate leadership in Salmonella and Tuberculosis genomic epidemiology, with Nature, Lancet, and PLoS contributions. His team's work on SARS-CoV-2 variants and Shigella sonnei evolution highlights real-time surveillance applications.
Scientific contributions include:
- World's first book on Infectious Disease Informatics (Springer, 2010)
- Development of MGTdb genomic database
- Creation of DODGE outbreak detection algorithm
- Advancements in CRISPR-based diagnostics
- Key role in Australian Pathogen Genomics Program
As an academic leader, he supervises students working on:
- Ellen Donnan: TB control strategies in NSW
- David Pham: Metagenomic pathogen detection
- Sarah Smith: Pneumococcal disease genomics
- Michael Wehrhahn: Dientamoeba fragilis epidemiology
His laboratory at Westmead Hospital develops:
- Genomic outbreak detection systems
- AI-driven surveillance tools
- Pathogen transmission mapping
- Drug resistance prediction models
