COVID-19: Scientists develop new method to estimate risk of airborne spread

COVID-19: Scientists develop new method to estimate risk of airborne spread

PTIUpdated: Wednesday, October 28, 2020, 05:53 PM IST
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Washington, Oct 28 (PTI) Scientists, including one of Indian-origin, have used a new mathematical method to understand airborne transmission of the novel coronavirus and found that protection from the spread of the virus increases almost proportionally with physical distancing.

According to the study, published in the journal Physics of Fluids, even simple cloth masks provide significant protection and could reduce the spread of COVID-19.

"If you double your distance, you generally double your protection. This kind of scaling or rule can help inform policy," said study co-author Rajat Mittal from Johns Hopkins University in the US.

"We also show that any physical activity that increases the breathing rate and volume of people will increase the risk of transmission. These findings have important implications for the reopening of schools, gyms, or malls," Mittal said.

The scientists noted that the new method calculates relative risk of different scenarios based on the idea that airborne spread of the virus occurs if a susceptible person inhales a viral dose that exceeds the minimum infectious quantity of the virus.

"The model employs basic concepts from fluid dynamics and incorporates the known scope of factors involved in the airborne transmission of such diseases," the researchers wrote in the study.

They said the model uses variables that can be added at each of the three stages of airborne transmission such as during the generation, expulsion, and aerosolisation of the virus-containing droplets from the mouth and nose of an infected host.

In subsequent studies, the researchers hope to look more closely at face mask efficiency and the transmission details in high-density outdoor spaces.

Beyond COVID-19, they said the model could apply to the airborne transmission of other respiratory infections, such as flu, tuberculosis, and measles.

"Ongoing studies will close these gaps in our understanding and provide better quantification of all the variables involved in this model," the scientists wrote.

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