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Predictive Analytics Model Examines Droplets to Map COVID-19 Spread

A predictive analytics model can help researchers better understand how respiratory droplets contribute to the spread of viruses like COVID-19.

Predictive analytics model examines droplets to map COVID-19 spread

Source: Thinkstock

By Jessica Kent

- A predictive analytics model showed that without masks, six feet of social distance may not be enough to keep one person’s respiratory droplets from reaching someone else, which could contribute to the spread of viruses like COVID-19.

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In a study published in the journal Physics of Fluids, a team of international researchers set out to better understand the role that droplet clouds play in the spread of respiratory viruses. Respiratory droplets released when people sneeze, cough, or talk contribute to the spread of COVID-19 and other viruses, the researchers noted.

The team applied existing models for chemical reactions and physics principles to droplets of a salt water solution, which they studied in an ultrasonic levitator to determine the size, spread, and lifespan of these particles in various environmental conditions.

Researchers found that respiratory droplets travel farther and last longer in humid, cold climates than in hot, dry ones. At 95 degrees Fahrenheit and 40 percent relative humidity, a droplet can travel about eight feet. However, at 41 degrees Fahrenheit and 80 percent humidity, a droplet can travel up to 12 feet.

Additionally, the results showed that droplets in the range of 14-48 microns possess higher risk because they take longer to evaporate and travel greater distances. Smaller droplets, conversely, evaporate within a fraction of a second, while droplets larger than 100 microns quickly settle to the ground because of their weight.

Using this information, researchers developed a predictive analytics model to forecast the early spread of respiratory viruses. The model is the first to be based on a fundamental approach taken to study chemical reactions called collision rate theory, which examines the interaction and collision rates of a droplet cloud exhaled by an infected person with healthy people.

“The basic fundamental form of a chemical reaction is two molecules are colliding. How frequently they’re colliding will give you how fast the reaction progresses,” said Abhishek Saha, a professor of mechanical engineering at the University of California San Diego, and one of the authors of the paper. “It’s exactly the same here; how frequently healthy people are coming in contact with an infected droplet cloud can be a measure of how fast the disease can spread.”

The team found that, depending on weather conditions, some respiratory droplets travel between eight feet and 13 feet away from their source before evaporating, without even accounting for wind. This means that without masks, six feet of social distance may not be enough to keep one person’s exhalated particles from reaching someone else.

“Droplet physics are significantly dependent on weather,” said Saha. “If you’re in a colder, humid climate, droplets from a sneeze or cough are going to last longer and spread farther than if you’re in a hot dry climate, where they’ll get evaporated faster. We incorporated these parameters into our model of infection spread; they aren’t included in existing models as far as we can tell.”

Many current pandemic models use fitting parameters to be able to apply the data to an entire population. The new predictive model developed by researchers in this study aims to change that.

“Our model is completely based on ‘first principles’ by connecting physical laws that are well understood, so there is next to no fitting involved,” said Swetaprovo Chaudhuri, professor at University of Toronto and a co-author.

“Of course, we make idealized assumptions, and there are variabilities in some parameters, but as we improve each of the submodels with specific experiments and including the present best practices in epidemiology, maybe a first principles pandemic model with high predictive capability could be possible.”

The team hopes that their model can help inform public health policies at the local level, and can be used in the future to better understand the role of environmental factors in virus spread. The results also further demonstrate the importance of wearing masks to stop the spread of COVID-19. Researchers plan to refine the model and increase its versatility.

"Our next step is to relax a few simplifications and to generalize the model by including different modes of transmission,” said Saptarshi Basu, professor at the Indian Institute of Science and a co-author. "A set of experiments are also underway to investigate the respiratory droplets that settle on commonly touched surfaces.”