Machine Learning for Multiscale Analysis of Biomedical Data
Research to tackle COVID-19 Pandemic
Overview: The year 2020 has uncovered one of the biggest pandemics in history, the novel coronavirus (COVID-19) that was first reported in Wuhan, Hubei Province, China in December 2019. Thus far, about 267013 confirmed cases and about 11201 deaths were reported worldwide. While China has made a large effort to shrink the outbreak, COVID-19 has developed into a pandemic in 185 countries. Case numbers are alarming as the virus spreads in Europe, Iran, South Korea, and Japan.
State of the art:Epidemiological mathematical models have been developed to help policy makers to take the right decisions during COVID-19 pandemic. While there are many mathematical models developed at epidemiological level for COVID-19, there is none so far at within-host level to understand COVID-19 replication cycle, and its interactions with the immune system.
Our research in COVID-19 aims to:
derive mathematical models to disentangle the multifactorial during COVID-19
propose control theoretical approached to tailor therapies in COVID-19
develop multiscale epidemiliogical models as a virtual disease tool to evaluate therapies and public health policies during COVID-19 pandemic.
Relevant Links in this Field:
Special Issue in ANNUAL REVIEWS IN CONTROL: Systems&Control Research Efforts Against COVID-19 and Future Pandemics [Link]
C. L. Azanza Ricardo and E.A. Hernandez-Vargas. The Risk of Lifting COVID-19 Confinement in Mexico.[Preprint]
A. E. Saldua Almocera and E.A. Hernandez-Vargas. Stability Analysis in COVID-19 Within-Host Model with Immune Response. [Preprint]
E.A. Hernandez-Vargas and Jorge X. Velasco-Hernandez. In-host Modelling of COVID-19 Kinetics in Humans.[Preprint] .