Machine Learning for Multiscale Analysis of Biomedical Data
Bridging within-host infection to between hosts transmission
Overview: A central paradigm in public health is to predict to what extent does the within-host infection dynamics influence the transmission process between individuals and to what is the effect of population dynamics of disease transmission at the individual level.
State of the art: While single models between the infection dynamics within a host and the population-level transmission dynamics are well-known, a comprehensive framework that would allow full integration of the two scales is still lacking. Although multiscale models can often be obtained by coupling different model classes (Hybrid models), “scale bridging” is a very delicate process where theoretical and computational tools are radically limited for this task.
Our research in Multiscale Modelling aims to:
maturate a within-host and between-host modelling framework as a virtual disease tool for the better prediction of epidemics and outbreaks
discover underlying shapes of immune cells at within-host level and social contact patterns between age groups that could influence the spread of infectious diseases
evaluate therapies, vaccines and public health policies in multiscale models
Relevant Publications in this Field:
A.E.S. Almocera, E.A. Hernandez-Vargas. Coupling multiscale within-host dynamics and between-host transmission with recovery (SIR) dynamics. Mathematical Bioscience. 309:34-41, 2019 [Link]
V.K. Nguyen, Rafael Mikolajczyk, E.A. Hernandez-Vargas. High-Resolution epidemic simulation using within-host infection and contact data. BMC Public Health. 2018[Preprint] [PDF]
A.E.S. Almocera, V.K. Nguyen, E.A. Hernandez-Vargas. Multiscale model within-host and between-host for viral infectious diseases. Journal of Mathematical Biology. 76 (404), 1-23, 2018 [Link] [Preprint]
V.K. Nguyen, C. Parra-Rojas, E.A. Hernandez-Vargas. The 2017 plague outbreak in Madagascar: data descriptions and epidemic modelling. Epidemics. 2018 [Link]