Stochastic method to control Mycobacterium tuberculosis epidemic.

Area of Science:

• Epidemiology
• Genetics
• Mathematical Biology

Background:

• Tuberculosis (TB) poses a significant global health challenge.
• Understanding the origin and complex population dynamics of TB epidemics is crucial for effective control.
• Stochastic processes at both population and genomic levels may influence TB transmission.

Purpose of the Study:

• To investigate the origins and population distributions of TB epidemics using a stochastic framework.
• To explore the link between genomic stochasticity, specifically single nucleotide polymorphism (SNP) distributions in hotspot genes, and TB infection patterns.
• To analyze the drivers of TB epidemic emergence and recurrence.

Main Methods:

• Stochastic modeling of TB infection dynamics.
• Analysis of single nucleotide polymorphism (SNP) distributions in hotspot driver genes.
• Multifractal analysis of time series data for species populations.

Main Results:

• The model demonstrated the emergence of random clusters and discrete domains in species populations, driven by demographic stochasticity and species interactions.
• Multifractal analysis indicated that TB epidemics are primarily driven by directional contact communication.
• The study identified potential for approximately periodic recurrence of TB epidemics.

Conclusions:

• TB epidemic origins are linked to stochastic behaviors at genomic and population levels.
• Contact communication is a key directional driver of TB spread.
• Optimizing model parameters offers a pathway for controlling TB epidemic recurrence.