Deep Poisson process pathogen phylodynamics to accelerate understanding in disease transmission
Lead Research Organisation:
Imperial College London
Department Name: Mathematics
Abstract
The discovery of deciphering the human genetic code has been a landmark scientific achievement, leading to the development of personalized medicine, gene therapies, or modern vaccines. Today, the genetic codes of major organisms, and viral or bacterial pathogens that compromise human health are identified (or sequenced) at low cost and at industrial scale, including for the purpose of reconstructing how infectious diseases spread in human populations, and how to stop spread.
The genetic relationships of pathogen variants provide objective data about who infected who, information that is otherwise hard to obtain. The mathematical and statistical theory that underlies the analysis of these data is called 'phylodynamics'. This theory has made possible to reconstruct and quantify how anti-microbial resistant pathogens have spread worldwide, in communities, or in hospital wards, or how novel COVID-19 variants emerge and replace each other.
This EPSRC project aims to develop a novel class of statistical phylodynamic theory, grounded in deep Poisson point processes, that are substantially more flexible and computationally faster than existing methods. Our preliminary findings indicate this approach has the potential to unlock the analysis of important questions about the age, behavioural characteristics, locations, mobility patterns or other characteristics of population groups that are the sources of pathogenic spread, and which to date are very challenging or impossible to address. We will develop the statistical theory and provide open-access and computationally scalable code for flexible and reproducible analyses. This project will benefit from close ties to the Machine Learning & Global Health network (development of deep non-parametric methods), the international PANGEA-HIV consortium (access to large-scale, rich and globally important data collected over the past 10 years), the UK Health Security Agency (aiming to use our methods in the UK) and to Oxford Nanopore (transitional industry impact).
The genetic relationships of pathogen variants provide objective data about who infected who, information that is otherwise hard to obtain. The mathematical and statistical theory that underlies the analysis of these data is called 'phylodynamics'. This theory has made possible to reconstruct and quantify how anti-microbial resistant pathogens have spread worldwide, in communities, or in hospital wards, or how novel COVID-19 variants emerge and replace each other.
This EPSRC project aims to develop a novel class of statistical phylodynamic theory, grounded in deep Poisson point processes, that are substantially more flexible and computationally faster than existing methods. Our preliminary findings indicate this approach has the potential to unlock the analysis of important questions about the age, behavioural characteristics, locations, mobility patterns or other characteristics of population groups that are the sources of pathogenic spread, and which to date are very challenging or impossible to address. We will develop the statistical theory and provide open-access and computationally scalable code for flexible and reproducible analyses. This project will benefit from close ties to the Machine Learning & Global Health network (development of deep non-parametric methods), the international PANGEA-HIV consortium (access to large-scale, rich and globally important data collected over the past 10 years), the UK Health Security Agency (aiming to use our methods in the UK) and to Oxford Nanopore (transitional industry impact).
Publications
Bu F
(2024)
Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes.
in Biometrics