Systems Analyses of Emerging Coronavirus Diseases with Big Data and Machine Learning
Abstract
The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become an unprecedented ongoing global public health and economic crisis. In order to better control the pandemic, surveillance and predictive modeling as well as the study of the nature of virus are all of paramount importance. Artificial intelligence (AI) approaches have been powerful for complex problems, which will be tremendously useful to predict outbreaks and epidemics for mitigating the threat of COVID-19. Therefore, our first major goal is to harness the power of machine learning and AI to predict the important features of the virus and the viral pandemic, such as pathogenicity, virulence, ability of contagion/spread, evolutionary rates, and population dynamics. Moreover, fundamental research to understand novel molecular and biological mechanisms of COVID-19 health impacts and identification or validation of biochemical, physiological, or combined biomarkers will be also of paramount importance. Therefore, our second major goal is to integrate big data on coronavirus multi-omics and population health to identify age-dependent molecular mechanisms of vulnerability and establish novel testable biomarkers for evaluating health impacts from COVID-19. In the age of big data and AI, machine learning provides us with powerful tools to fight the pandemic. Here, we aim to develop a state-of-the-art set of machine learning approaches and harness them to perform global, unbiased, and systems-level analyses to provide insights on emerging coronavirus diseases. We will also harness the Sargasso Sea of big data on coronavirus viromics, genomics, host transcriptomics, proteomics, and population health. In this project, we will first develop rigorous machine learning toolkits and harness them for systems level analyses on emerging coronavirus diseases to predict viral pathogenicity and epidemics. We will then integrate big data on coronavirus multi-omics and population health to identify age-dependent molecular mechanisms of vulnerability and establish novel testable biomarkers for evaluating health impacts from COVID-19. This project’s success can bring immediate impact to offer new insights on viral pathogenicity, transmissibility, epidemiology patterns, and potentially new and more accurate biomarkers that can be used immediately for improved practices of clinical care. The novel knowledge gained from the project such as new viral immunity features, vaccine design, and therapeutic candidates can offer long-term impact for our society’s preparedness against COVID-19 or other emerging viral diseases. This project is responsive and relevant to the FY20 PRMRP Topic Areas of Emerging Viral Diseases and/or Respiratory Health, as it directly addresses two COVID-19 Focus Areas: (1) Surveillance and predictive modeling tools that leverage artificial intelligence approaches to predict outbreaks and epidemics and support strategies for mitigating the threat of COVID-19. (2) Research to understand novel molecular and biological mechanisms of COVID-19 health impacts (e.g., microbiome) and identification/validation of biochemical, physiological, or combined biomarkers for evaluating short- and long-term health impacts from COVID-19.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110019
Entities
People
- Sidi Chen
Organizations
- United States Army
- Yale University