After nearly a year of studying COVID-19, scientists are still grappling with fundamental questions — including understanding the dominant modes of transmission and predicting how “superspreading” events arise. A newly improved model produced by engineers and physicists could help.
Last summer, Professor Swetaprovo Chaudhuri (UTIAS) and his colleagues developed what they called a “first-principles modelling approach” to understanding the factors that impact COVID-19 spread.
The team — which also included Professor Abhishek Saha at the University of California San Diego, and Professor Saptarshi Basu of the Indian Institute of Science — outlined their findings in a paper published in the journal Physics of Fluids.
Unlike epidemiological models that predict the spread of a disease based on empirical data from past outbreaks or from estimating person-to-person contact, this model makes use of fundamental flow physics concepts, droplet and jet aerodynamics, evaporation and related thermodynamics.
The idea was to simulate the physical path of droplets expelled by infected people, along with their physical state. The model can be used to predict the probability of transmitting the infection from one person to another.