Time span: December 2023 - November 2025.
In March 2020, COVID-19 was declared a global pandemic, spurring intense research efforts. Genomic
surveillance emerged as a crucial defense against the virus.
The SENSIBLE project, supported by the Italian Ministry of University and Research
(through NextGeneration EU funding), aims to develop methods for analyzing viral genomes and
implement
early warning information systems based on data-driven analysis, exploiting data from past
epidemics
for validation.
SENSIBLE will create an integrated framework for genomic surveillance of viral pathogens,
utilizing data-driven and
knowledge-based analyses. By leveraging knowledge from COVID-19, it seeks to enhance understanding
of viral pathogens and aid healthcare decision-making.
Derive effective methods for data-driven identification of emerging viral pathogens
Build an objective framework for genomic surveillance in current and future epidemics
Implement an early warning system, to assist decision-making in healthcare
SENSIBLE will explore and harness data from different domains of interest, including: analyses of available data, mapping of equivalent/matched information from similar pathogens, computation or prediction of novel features and properties of the virus under study. The framework developed by SENSIBLE will feature four tasks:
The project partners will leverage previously developed methods and tools for the genomic surveillance. The range of applications of these methods wil be extended to identify minimal subsets of actionable data, and evaluate their validity and robustness.
To translate viral evolutionary dynamics into a collection of “epidemiologically-relevant” annotations of the viral genome, different sources of information will be gathered. Annotations available from existing resources will be retrieved and integrated into an internal knowledge base; missing data will be computed (via bioinformatics tools) or predicted (via automatic learning procedures).
Based on the evolutionary observations and the detailed functional annotations from previous tasks, a prioritization score will be computed to assign a “level of concern” to emerging viral pathogens and or to novel viral lineages. The score will be exploited to develop a ranking system, which will be evaluated according to the heuristics to be developed.
The initial development and setup of SENSIBLE will be performed on a selection of use cases from the COVID-19 pandemic. Possible candidates to showcase the system and provide an unbiased evaluation are Monkeypox (2022), Zika (2015-2016), and Swine Influenza (2009).
We will tackle two key epidemiological questions and identify key metrics for raising alerts and early warnings in both scenarios:
What is the minimal amount of data production/availability required to set up an effective surveillance system? Can genomic surveillance be applied even in scarce/low resources settings?
If a new mutation or pattern of mutations arise in a human pathogen, how does this impact its epidemiological features?
How can a genomic surveillance tool based on small viral genomes dataset help inform decisions of public health institutions?
Our meetings have already begun!
Here, we present RecombinHunt, a novel, automated method for the identification of recombinant genomes purely based on a data-driven approach
After a long journey, our paper describing the RecombinHunt approach, has been published on the prestigious Nature Communication journal!
We presented SENSIBLE in the context of the research projects of the 36th International Conference on Advanced Information Systems Engineering June 03-07 2024 Limassol, Cyprus. Check out our poster here.
The news about our research on recombinant viruses for improving
warning systems is out.
See
Polimi
website
and
Unimi
website,
X post,
Linkedin
post,
DEIB
department post.
Il Sole 24ore is speaking about us! See the article.
PoliMi has reached a consolidated international reputation in data modeling/integration and search tools/user interfaces for genomic data management and query answering. UniMi has long-standing experience and expertise in the development and application of bioinformatics tools for genomic research. The two units will co-design methods in the project. PoliMi will be more focused on the implementation of information technologies and user interfaces, while UniMi will contribute expertise in biology and comparative genomics.
Assistant Professor
(Politecnico di Milano)
Associate Professor
(Università degli Studi di Milano)
Full Professor
(Politecnico di Milano)
Postdoc Researcher
(Politecnico di Milano)
Assistant Professor
(Politecnico di Milano)
Master Student
(Politecnico di Milano)