Rapid estimation of viral emission source location via genetic algorithm

Area of Science:

• Environmental science
• Epidemiology
• Computational modeling

Background:

• Indoor infectious disease spread is well-documented.
• Outdoor transmission is critical for highly infectious diseases like SARS-CoV-2, especially in areas with high infection rates or variants.
• Managing outdoor spread is essential in specific community contexts.

Purpose of the Study:

• To develop a computationally efficient simulation framework for estimating outdoor infectious disease transmission risks.
• To analyze the impact of factors like population density and wind on outdoor infection.
• To identify potential super-spreader scenarios in outdoor settings.

Main Methods:

• A simulation framework using probabilistic distributions for viral particles, decay, and infection.
• Reduced computational cost compared to high-fidelity simulation methods.
• Application to scenarios like small outdoor gatherings and public speaking events.

Main Results:

• Infection risk is sensitive to population density in outdoor environments.
• Outdoor transmission of infectious diseases is demonstrated as plausible.
• Potential super-spreader locations were identified in virtual scenarios.

Conclusions:

• Outdoor gatherings pose a risk for infectious disease transmission.
• Precautions should be implemented to mitigate infection spread in outdoor settings.
• The simulation framework provides rapid estimations for various outdoor scenarios.