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Understanding the interplay between viral evolution, disease spread dynamics, and human contact patterns is essential for designing effective public health interventions. Infectious diseases are rarely static; their spread is influenced by both biological factors, such as mutations that enhance transmissibility, and social factors, such as the structure of human interaction networks. Rapidly mutating viruses, which can alter their transmission probabilities during an outbreak, pose a unique challenge as they adapt dynamically to their environment, potentially outcompeting older strains. By studying how such viruses evolve and spread in different network configurations, we aim to uncover mechanisms that drive epidemic trajectories and identify intervention points to mitigate the impact of future outbreaks.
This research leverages a multi-strain epidemiological model (SI1I2I3SD) that incorporates the sequential evolution of three viral strains (V1, V2, V3) via genetic mutations. It integrates factors such as mutation rates, cross-infections, recovery, and mortality, alongside diverse network structures representing real-world human contact patterns. By systematically exploring these dynamics, we address key questions that underpin the complexity of epidemics driven by viral evolution.
Key Questions
Key Questions
Viral Evolution:ย
How do sequential genetic mutations, which increase transmissibility (๐ฝ1<๐ฝ2<๐ฝ3), shape the competition between viral strains?
What role does the mutation rate (๐) play in the timing and dominance of emergent strains?
How does viral evolution affect the overall epidemic size and peak infection proportions?
Disease Spread Dynamics (SI1I2I3SD):
How do cross-infections between individuals carrying different strains alter the spread and persistence of each strain
What are the implications of recovery (๐พ) and mortality (๐ฟ) rates on strain replacement dynamics?
Human Contact Networks: How does disease spread differ across network types, such as:
Does limiting long-range connections (regular lattice) delay the spread of the disease through the network?
Does having higher clustering localize the strains to specific clusters and thus favor co-existence over dominance?
Does having hub nodes, which are potential superspreaders level the playing field for the competition?
How does real-world network calibration impact the predictability and control of disease spread?
Metrics for Epidemic Analysis:
Metrics for Epidemic Analysis:
How does R0,effective capturing heterogeneity via degree distribution, compare across network types?
What is the relationship between network structure and the percolation threshold (P๐) for epidemic onset?
How do peak infection time (P๐ก) and proportion (PI) vary under different scenarios?
How do mutation and contact network properties jointly influence epidemic size (E)?
By addressing these questions, this work bridges the biological, epidemiological, and social dimensions of disease spread, contributing to a deeper understanding of the dynamics of rapidly evolving infectious diseases.
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