How surface and fomite infection affect contagion dynamics: a study with self-propelled particles.

Area of Science:

• Epidemiology
• Computational Physics
• Mathematical Biology

Background:

• Self-propelled particle models are used to study disease spread.
• Infectious diseases like COVID-19 can spread through direct and indirect routes.
• The impact of indirect transmission (surfaces, airborne particles) is not fully understood.

Purpose of the Study:

• To investigate epidemic spreading dynamics incorporating both direct and indirect infection routes.
• To quantify the impact of indirect transmission on disease propagation.
• To analyze real-world disease scenarios, such as COVID-19 waves.

Main Methods:

• Developed a simulation model of an artificial society with realistic human movement.
• Incorporated indirect infection mechanisms via contaminated surfaces and airborne particles ('infectious tail').
• Utilized mathematical formulation and extensive computational studies for analysis.

Main Results:

• Calculated analytical expressions for survival distance and infection probability.
• Demonstrated the significant role of indirect infections across various dynamical parameters.
• Successfully interpreted multiple waves in COVID-19 infection statistics using the model.

Conclusions:

• Indirect transmission is a critical factor in epidemic dynamics and cannot be overlooked.
• Ignoring indirect infection routes can lead to misinterpretation of epidemiological parameters and control strategies.
• The developed framework provides a more comprehensive understanding of infectious disease spread.