A shock to the system

About

The global, multifarious challenge posed by the COVID-19 pandemic has scientists tapping their wide-ranging fields of expertise to attack the problem on many fronts. Answering the call from KAUST President Tony Chan, and coordinated by the University's leadership team, KAUST researchers making up the Rapid Research Response Team (R3T) are turning this crisis into an opportunity to innovate.

At the center of public health challenges posed by the pandemic is the stress that the spread of the virus has put on hospitals and clinical staff. Some researchers are using forecasting models to help prepare by projecting future waves of hospitalization.

"Preparedness is key to reducing fatality," said KAUST Professor of statistics Hernando Ombao. "If a country, or if a community, is able to anticipate the need for hospitalization, then maybe they can reduce fatality rates. One of the main reasons for high fatality rates is because the hospitals are just completely overwhelmed."

Modeling the pandemic

Professor Ombao heads up the Biostatistics Group within the University's Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE). The KAUST Biostatistics Group develops novel statistical methods and models for biological processes with complex dependence structures. They use tools like time series analysis, spectral analysis, computational statistics and data visualization.

One of the areas Ombao and his group have been focused on is how the brain responds to stimuli. A sound statistical approach is needed to understand how the different regions of the brain communicate with each other. The research team observe brain signals of rats and attempt to predict their decisions.

Specifically, the statistical analysis is being used to understand and model how a rat's brain can respond to certain types of events such as a stroke. Using techniques like stationary subspace analysis (SSA), researchers might be able to reduce the dimension of the data and improve prediction performance.

How can this be related to COVID-19 research?

"For me, I've been very interested in how a pandemic does a shock to the entire system," Ombao explained. "So with the entire system, I can focus on the impact of hospitalization."

Ombao aims to develop statistical models that are geared towards obtaining accurate estimates of these projections. "If we are able to give our public health officials and the hospital administrators an accurate set of scenarios, from the optimistic to the pessimistic, then they'll be able to use it for preparedness," he said.

Models can be adjusted by tweaking mitigation factors such as infection rate, susceptibility rate, as well as recovery rate. Then, based on these inputs, it's possible to generate curves of plausible scenarios. The data can be visualized, replicated in graphs and saved as inputs into further analysis.

"Knowing about the mathematical models for epidemics is new, so we're learning as we go," Ombao said.

Collaborating to develop reliable statistical models

Ombao and his team of researchers, however, are not alone in navigating these new waters of mathematically modeling pandemics like COVID-19. The Biostatistics Group is very grateful for the input of David Ketcheson, an associate professor of applied mathematics and computational science at KAUST.

Listen to an interview with Dr. David Ketcheson

"With David, I learned this SIR model, the susceptible and infectious and recovery model and also through David's codes, we were able to implement an R Shiny toolbox," Ombao explained. 

An SIR model is essentially an epidemiological model used to compute a theoretical number of people infected with a virus in a closed population over time in order to better understand the spread of the infectious disease (Susceptible/Infected/Recovered). Toolbox development is in an integral part of the research and is used to improve the impact of the work.

The R Shiny toolbox is quite interactive. An SIR simulator is available on Ombao's website. It provides a rough estimate of factors such as transmission rate and infection period.

"We can input that into our simulator and then it will give you a broad range of plausibilities with regards to what is the expected number of infection on each day," as he outlines.

Read the full article