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Big Data Analytics Enables Scientists to Model COVID-19 Spread

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Public health efforts depend heavily on predicting how diseases like COVID-19 spread across the globe. Researchers from ����Vlog’s in collaboration with , a global data technology and advanced analytics leader, have received a rapid research (RAPID) grant from the National Science Foundation (NSF) to develop a model of COVID-19 spread using innovative big data analytics techniques and tools. The project leverages prior experience in modeling Ebola spread to successfully model the spread of COVID-19.

Researchers will use big data analytics techniques to develop computational models to predict the spread of the disease utilizing forward simulation from a given patient and the propagation of the infection into the community; and backward simulation tracing a number of verified infections to a possible patient “zero.” Users of the models and algorithms developed by ����Vlog and LexisNexis Risk Solutions will conform to all applicable requirements of HIPAA and other privacy regulations.

The project also will provide quick and automatic contact tracing and is expected to help reduce the number of patients infected with COVID-19 and virus-related deaths. This new methodology, which includes coalition-building efforts, will also support solutions for a wide range of other public health issues.

“This National Science Foundation grant will enable our researchers to advance knowledge within the field of big data analytics as well as across different fields including medical, health care, and public applications,” said , Ph.D., dean of ����Vlog’s College of Engineering and Computer Science. “Through our collaboration with LexisNexis Risk Solutions, we will jointly address public health concerns of national and global significance using cutting-edge computer science, big data analytics, data visualization techniques, and decision support systems.”

The era of “big data” is quickly changing how models are used to understand the dynamics of disease propagation. The ����Vlog project, led by , Ph.D., principal investigator, a professor in the , and director of the NSF Industry/University Cooperative Research Center for Advanced Knowledge Enablement (CAKE), ����Vlog’s College of Engineering and Computer Science, will use an innovative risk score approach in modeling and predicting COVID-19 spread.

“The HPCC Systems team at LexisNexis Risk Solutions has an outstanding relationship with Dr. Furht and ����Vlog,” said Flavio Villanustre, vice president, Technology and CISO, LexisNexis Risk Solutions. “����Vlog and LexisNexis Risk Solutions have been collaborating on several projects over the last five years. Our most recent work involved the NSF grant for modeling Ebola using the HPCC Systems platform and big data analytics. We are grateful to the NSF, ����Vlog and Dr. Furht for their continued investment in research that helps the community.” 

For the project, COVID-19 spread patterns will be fed into a decision support system (DSS), which also contains information about social groups or individual people. Social groups could include nurses and doctors who had contact with a patient infected with COVID-19, passengers who travelled on the same plane with an individual diagnosed with COVID-19, or family members living with someone who contracted COVID-19, among others. Based on spread patterns, the DSS would then calculate probabilities for a social group or a given person to become infected with COVID-19. Data will be provided as reports to appropriate state and government agencies so that they can immediately contact and test people who have a high score related to the person who is infected with COVID-19.

“The data analytics expertise we will receive from LexisNexis Risk Solutions will enable us to develop a model that will automatically and quickly identify every contact of an infected person,” said Furht, who received an NSF RAPID grant for modeling Ebola spread using big data analytics. “Our approach will be much faster and more efficient than methods that are done manually and we expect it to significantly reduce infection rates and the number of deaths in the United States and around the world.” 

Members of the ����Vlog team for “Modeling Coronavirus Spread Using Big Data Analytics,” include , Ph.D., Motorola Professor; , Ph.D., an associate professor; , Ph.D., a professor; , Ph.D., an associate professor and a fellow of ����Vlog’s Institute for Sensing and Embedded Network Systems Engineering (I-SENSE); and , Ph.D., an instructor, all within ����Vlog’s Department of Computer and Electrical Engineering and Computer Science.

The LexisNexis Risk Solutions team includes Villanustre; Arjuna Chala, senior director, Operations; Roger Dev, senior architect; and Jesse Shaw, principal statistical modeler.

“Because of a lack of actual social network data, mathematical compartmental modeling has been restricted to hypothetical populations. However, emerging LexisNexis Risk Solutions technologies could accelerate the accumulation of knowledge around disease propagation in the United States,” said Furht. “For our research, we plan to calculate various scores related to COVID-19 spread including population density rank, household mortality risk, street level mortality risk, and county mortality risk.”

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Tags: technology | engineering | faculty and staff | coronavirus | research