Decay rate estimation of respiratory viruses in aerosols and on surfaces under different environmental conditions

Majority of the viral outbreaks are super-spreading events established within 2-10 h, dependent on a critical time interval for successful transmission between humans, which is governed by the decay rates of viruses. To evaluate the decay rates of respiratory viruses over a short span, we calculated their decay rate values for various surfaces and aerosols. We applied Bayesian regression and ridge regression and determined the best estimation for respiratory viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), severe acute respiratory syndrome coronavirus (SARS-CoV), middle east respiratory syndrome coronavirus (MERS-CoV), influenza viruses, and respiratory syncytial virus (RSV); the decay rate values in aerosols for these viruses were 4.83 +/- 5.70, 0.40 +/- 0.24, 0.11 +/- 0.04, 2.43 +/- 5.94, and 1.00 +/- 0.50 h-1, respectively. The highest decay rate values for each virus type differed according to the surface type. According to the model performance criteria, the Bayesian regression model was better for SARS-CoV-2 and influenza viruses, whereas ridge regression was better for SARS-CoV and MERS-CoV. A simulation using a better estimation will help us find effective non -pharmaceutical interventions to control virus transmissions.

Automated summary

The majority of viral outbreaks are fast-spreading events established within 2-10 hours, depending on the decay rates of viruses, which determine the critical time interval for successful transmission between humans. By calculating the decay rate values for different surfaces and aerosols, we determined the best estimations for respiratory viruses, including SARS-CoV-2, SARS-CoV, MERS-CoV, influenza viruses, and RSV. The decay rate values in aerosols for these viruses were found to be 4.83 +/- 5.70, 0.40 +/- 0.24, 0.11 +/- 0.04, 2.43 +/- 5.94, and 1.00 +/- 0.50 h-1, respectively. The choice of regression model varied based on virus type, with Bayesian regression performing better for SARS-CoV-2 and influenza viruses, and ridge regression performing better for SARS-CoV and MERS-CoV. Simulation using improved estimations can facilitate the discovery of effective non-pharmaceutical interventions for virus control.