Towards Global Biopathogens Surveillance System
Alina Frolova
Lecture:
March 06 (FRI)
16:10-17:30
Practice:
March 06 (FRI)
17:50-19:20
Language:
Ukrainian
Public health surveillance is the continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice according to World Health Organization. Major part of this surveillance is dedicated to identifying biological pathogens that can pose serious threat to humanity, such as antimicrobial resistance. Yet, such systems are not developed to the point where emerging infectious threats can be better anticipated. Moreover,pathogens often spread undetected for some time before being diagnosed in a population.
Many sources of pathogens were studied before, including hospitals or life stock farms, however, little is known about urban environments and mass-transit systems, which have been shown to have distinct genetic profiles and might play role in pathogens transfer.
Here we will discuss the challenges of building global genetic map of microorganisms and their antimicrobial resistance in the built human environment with respect to data analysis and provenance.
Practicals
Practicals are based on the recent work published by MetaSUB consortium “Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance”, which created a global metagenomic and antimicrobial resistance (AMR) atlas of urban mass transit systems from 58 cities, spanning 3,741 samples and 4,424 taxonomically-defined microorganisms collected for from 2015-2017.
Participants task would be to explore the data and try to build the classifier, which would predict the city-of-origin of the sample over taxa using machine learning techniques.
Pre-requisites:
- be familiar with published work Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance
- have RStudio installed with R version 3.5 or higher
- understand the concept of plotting with ggplot2
- know the basics of linear regression
- read about supervised learning models (e.g. Support Vector Machine).