Structural modeling and phylogenetic analysis for infectious disease transmission pattern based on maximum likelihood...

Area of Science:

• Epidemiology
• Molecular Biology
• Genetics

Background:

• Contagious disease outbreaks pose significant global health risks, leading to pandemics and substantial human casualties.
• Understanding infectious disease transmission patterns is crucial for mitigating outbreaks and managing public health.
• Viral mutations and environmental factors can exacerbate disease virulence and spread, necessitating robust monitoring.

Purpose of the Study:

• To investigate infectious disease transmission patterns using molecular epidemiology.
• To develop and validate a novel method for analyzing molecular evolutionary genetics to reduce medical risks.
• To characterize patient profiles, variants, symptoms, geographic locations, and treatment responses for better disease pattern analysis.

Main Methods:

• Proposed Evolutionary Tree Analysis (ETA) for molecular evolutionary genetic analysis.
• Utilized the Maximum Likelihood Tree Method (MLTM) to analyze selective pressure and identify mutations influencing disease transmission.
• Employed ETA combined with Markov Chain Bayesian Statistics (MCBS) for reconstructing transmission trees using sequence data.

Main Results:

• The proposed ETA-MCBS method demonstrated high accuracy at 97.55%.
• Achieved a prediction accuracy of 99.56% and an overall performance of 98.55%.
• Outperformed existing methods in analyzing infectious disease transmission patterns and molecular evolution.

Conclusions:

• The ETA-MCBS approach provides an effective tool for understanding and predicting infectious disease transmission dynamics.
• This method aids in identifying critical mutations and genetic factors influencing disease spread and clinical progression.
• The study highlights the importance of molecular epidemiology and advanced statistical methods in managing public health crises.