From localized to well mixed: How commuter interactions shape disease spread

Area of Science:

• Epidemiology
• Mathematical Biology
• Network Science

Background:

• Commuting facilitates the large-scale spread of infectious diseases.
• Disease dynamics are influenced by population commuting distributions, which describe travel probabilities based on distance.
• Understanding these dynamics is crucial for effective epidemic control.

Purpose of the Study:

• To demonstrate qualitatively different infection dynamics based on population commuting distributions.
• To analyze the impact of commuting patterns on epidemic wave propagation.
• To investigate the role of an initial dispersal phase and its implications for disease detection.

Main Methods:

• Modeling epidemic spread using reaction-diffusion systems for localized commuting.
• Analyzing long-tail commuting distributions to identify deviations from standard wave models.
• Investigating the initial dispersal-dominated regime and its effect on detection time.

Main Results:

• Exponentially localized commuting distributions result in Fisher waves with speeds proportional to commuting distance.
• Long-tail commuting distributions prevent finite-velocity wave formation and exhibit nontrivial spatial dependence.
• An initial dispersal phase creates an 'offset time' before exponential growth, impacting early detection.

Conclusions:

• Commuting distribution is a critical factor in determining epidemic spread patterns.
• The 'offset time' is a vital, yet often overlooked, metric for epidemic surveillance.
• Tailored surveillance strategies are needed to account for diverse commuting behaviors and their impact on disease detection.