Probability of COVID-19 infection by cough of a normal person and a super-spreader.

In this work, we estimate the probability of an infected person infecting another person in the vicinity by coughing in the context of COVID-19. The analysis relies on the experimental data of Simha and Rao ["Universal trends in human cough airflows at large distances," Phys. Fluids 32, 081905 (2020)] and similarity analysis of Agrawal and Bhardwaj ["Reducing chances of COVID-19 infection by a cough cloud in a closed space," Phys. Fluids 32, 101704 (2020)] to determine the variation of the concentration of infected aerosols with some distance from the source. The analysis reveals a large probability of infection within the volume of the cough cloud and a rapid exponential decay beyond it. The benefit of using a mask is clearly brought out through a reduction in the probability of infection. The increase in the probability of transmission by a super-spreader is also quantified for the first time. At a distance of 1 m, the probability of infection from a super-spreader is found to be 185% larger than a normal person. Our results support the current recommendation of maintaining a 2 m distance between two people. The analysis is enough to be applied to the transmission of other diseases by coughing, while the probability of transmission of COVID-19 due to other respiratory events can be obtained using our proposed approach.