New York City COVID-19 data, with reported first- and second-wave data, and corrected first-wave data. Courtesy of Talib Dbouk and Dimitris Drikakis
The number of total daily infections reported during the first wave is much lower than that of the second, but the total number of daily deaths reported during the first wave is much higher than the second wave. This contradiction inspired researchers from the University of Nicosia in Cyprus to explore the uncertainty in the daily number of infections reported during the first wave, caused by insufficient contact tracing between March and April 2020.
In Physics of Fluids, Talib Dbouk and Dimitris Drikakis report using environmental fluid dynamics -- advanced computational multiscale multiphysics modeling and simulations -- to develop a constitutive relationship between weather seasonality conditions, such as temperature, relative humidity, and wind speed, and having two pandemic curves per year.
"We integrated a new physics-based relationship into a pandemic forecast model that accurately predicted, as it was later observed, a second COVID-19 pandemic wave within many cities around the world, including New York," said Drikakis.
Most, if not all, of the data for the daily number of total new infections reported during the first wave of the pandemic were underestimated and used incorrectly.
"Within the city of New York, our work shows that the daily number of new infections reported during the first wave was underestimated by a factor of four," Dbouk said. "So, the uncertainty of first-wave data mixed with second-wave data means the general conclusions drawn can be misleading, and everyone should be aware of this."
The researchers' work is the first known case of deriving an advanced uncertainty quantification model for the infected cases of the pandemic's first wave based on fluid dynamic simulations of weather effects.
"Our model is physics-based and can rectify first-wave data inadequacies by using second-wave data adequacy within a pandemic curve," said Drikakis. "Our proposed approach combines an environmental weather seasonality-driven virus transmission rate with pandemic multi-wave phenomena to improve the data accuracy of statistical predictions."
In the future, the researchers' proposed uncertainty quantification model may help correct the worldwide total number of daily coronavirus infections reported by many cities during the first wave of a pandemic.
The article, "Correcting pandemic data analysis through environmental fluid dynamics," is authored by Talib Dbouk and Dimitris Drikakis. It will appear in Physics of Fluids on June 22, 2021 at: https://aip.scitation.org/doi/10.1063/5.0055299