Real-time Identification of the Covid-19 Pandemic: Intelligent Toolset to Predict and Optimally Control Viral Outbreaks
Mathematical modeling of infectious diseases received increased attention in the 1980s with the spread of the global pandemic Human Immunodeficiency Virus (HIV/AIDS). Pandemic outbreaks such as the Severe Acute Respiratory Syndrome (SARS), in 2003, and swine flu, in 2009, have highlighted the need and importance of mathematical and computer modeling as valuable tools that can help public health policy makers in making decisions about vaccination strategies, response plans, and control measures. The recent outbreak of Coronavirus pandemic throughout the world has shown the volatility of public health response plans and that the world was not prepared enough to face this kind of pandemic.
In this project, we will develop computational models and identification algorithms for real-time estimation of the dynamic parameters of the Coronavirus pandemic. The goal is to develop improved computational modeling and identification algorithms for real-time estimation of the dynamic parameters of a pandemic infection such as the Coronavirus. The project will utilize published data from multiple sources along with control measures taken by government and health officials worldwide over the past few months regarding the evolution of the pandemic. The ultimate goal is to develop a software suite that can be used by policy makers and health officials to detect such outbreaks at early stages by estimating their parameters and predicting their evolution based on real-time data. The supporting company will look into further development and commercialization of the toolset for the health informatics market by developing a software package that can be quickly deployed for a range of custom use cases.
