Pathogen diversity in meta-population networks.

Area of Science:

• Epidemiology
• Mathematical Biology
• Network Science

Background:

• Pathogen diversity, characterized by coexisting strains, is a significant biological phenomenon, exemplified by SARS-CoV-2 mutations.
• While pathogen mutation is a known driver of diversity, the roles of traffic and gene networks in shaping pathogen diversity remain theoretically underexplored.

Purpose of the Study:

• To investigate the primary factors inducing pathogen diversity.
• To theoretically study the impact of traffic and gene networks on pathogen diversity dynamics.

Main Methods:

• A reaction-diffusion model for pathogens incorporating mutation on meta-population networks was proposed.
• The Microscopic Markov Chain Approach (MMCA) was extended to analyze the model dynamics.
• The study examined the influence of population movement, mutation rates, and network structures (traffic and gene) on pathogen diversity.

Main Results:

• Traffic networks promote pathogen diversity in cities with lower initial infection densities.
• Pathogen diversity is less likely in cities with short effective distances within the traffic network.
• Star-type gene networks are more conducive to pathogen diversity than lattice or chain-type networks.
• Increased movement and mutation probabilities enhance pathogen diversity.
• Population movement towards cities with short effective distances leads to higher overall infection densities.

Conclusions:

• Traffic and gene network structures significantly influence pathogen diversity.
• Movement and mutation are key drivers that can be modulated by network properties to control pathogen diversity.
• Understanding these network dynamics is crucial for managing infectious disease spread and evolution.