Title: Novel machine learning models for real time pathogen management
Funder: The Academy of Medical Sciences

Abstract: High disease-burden pathogens are a major public health concern that must be responded to by clinicians and epidemiologists in real-time. These pathogens are routinely sequenced by Public Health England (PHE) but currently there is no way to translate this data into actionable advice for clinicians. Automation of translation of sequencing data into bitesize information will greatly aid clinical and epidemiological responses. I aim to do this through: - Machine learning (ML) models that will be trained on unique access to PHEâ€™s large database of previous cases and reported metadata to provide actionable information straight off the sequencing machine. - Classifiers will inform clinicians of disease severity likelihoods through virulence factors and transmission history through country of origin association. - My approach will use random forests for which classifiers can be created based on genomic features of large sets of characterised bacteria that will be trialled on test sets for which classifiers are â€˜unknownâ€™. Since 2014, PHE has been using in silico genotyping methods that require significant compute time and provide results that are sometimes discrepant to phenotypic typing. In silico genotyping needs to be more accurate, accessible and faster to inform clinicians with actionable reports quickly. Information about the infection provided quickly gives the clinician opportunity to choose the best treatment/intervention immediately. This will be the first time that ML models have been developed in collaboration with government to automate classifications and will greatly enhance the ability to report actionable points to clinicians and epidemiologists without expertise in bioinformatics.

Lay abstract: The ability to respond to infectious diseases is dependent on the level of information that clinicians and epidemiologists have on the infection and bacteria. This information needs to be succinct, bitesize, informative and actionable but also readily and rapidly available. I propose to: - Build Machine Learning algorithms to be able to predict this information based on whole genome sequencing of bacteria in collaboration with Public Health England for foodborne bacteria. - The models will be able to predict what country the bacteria is likely to have originated from (either as an imported food, returning traveller or domestic case) and what dangerous genes are present to inform treatment options. -The ML algorithms will learn the genetic features associated with previous cases as well as their associated information and will be able to test whether new strains have the same features and therefore likely the same associations with that information. At the moment, current methods are not always accurate in predicting metadata information and discrepancies between results occur. This project will investigate those discrepancies and aim to provide improved algorithms for this. Also currently, to be able to predict county of origin, time for population structure analysis is required. It vital that accurate predictions of actionable information are automated and fast. This will be the first time that reports of this kind of actionable information are produced and will be essential to both biologists and public health practitioners who are using sequencing technology for surveillance and diagnostics.

Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE;INFECTION