Prediction of coronavirus infections and complications at the individual and the population levels from genomic, proteomic, clinical and behavioral data sources
Approved Research ID: 62946
Approval date: July 16th 2020
As of mid-April 2020, 2 million people are infected worldwide with the novel coronavirus that first appeared in Wuhan, China in December of 2019. Now, the USA is at the epicenter of this pandemic where it has already killed 20,000 people. Approaches to slow down the progression are urgently needed. This requires a better fundamental understanding of the factors affecting not only virus spread, but also who develops complications and ultimately dies from the infection.
It is becoming clear that there is a multitude of factors at play, including molecular, physiological, life-style, behavioral, demographic and socio-economic ones. In particular, co-morbidities such as diabetes and high blood pressure are known risk factors for COVID-19 complications and death, but are likely only the tip of the iceberg. Molecular data indicates that as many as a hundred co-morbidities exist. When many factors are involved, integration of these factors through statistical approaches is needed to assist with taking all of these factors into account when predicting and assessing the health risks arising from coronavirus spread and infection. This project will create computational tools that will assist individuals and healthcare professionals in coronavirus-related decision-making, and thereby alleviate exhaustion of human and material resources. To decrease the numbers of people suffering from this pandemic, these tools are needed urgently.