Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2502.20117

Eigenvector-Based Sensitivity Analysis of Contact Patterns in Epidemic Modeling

Evans Kiptoo Korir·Zsolt Vizi
View original source ↗Read source document ↗Export metadata ↓

Abstract

Understanding how age-specific social contact patterns and susceptibility influence infectious disease transmission is crucial for accurate epidemic modeling. This study presents an eigenvector-based sensitivity analysis framework to quantify the impact of age-structured interactions on disease spread. By applying perturbation analysis to the Next Generation Matrix, we reformulate the basic reproduction number, $\mathcal{R}_0$, as a generalized eigenproblem, enabling the identification of key age group interactions that drive transmission. Using real-world COVID-19 contact data from Hungary, we demonstrate the framework's ability to highlight critical transmission pathways. We compare these findings with results obtained earlier using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCC), validating the effectiveness of our approach. Additionally, we extend the analysis to contact structures in the UK and British Columbia, Canada, providing broader epidemiological insights. This work enhances our understanding of demographic interactions in epidemic propagation and offers a robust methodological foundation for improving infectious disease modeling and informing public health interventions.

Explore related subjects

q-bio.PEphysics.soc-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Evans Kiptoo Korir, Zsolt Vizi. 2025-11-20. Eigenvector-Based Sensitivity Analysis of Contact Patterns in Epidemic Modeling. https://arxiv.org/abs/2502.20117

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A conceptual predator-prey model with super-long transients

Drawing on the understanding of the logistic map, we propose a simple predator-prey model where predators and prey adapt to each other, leading to the co-evolution of the system. The special dynamics observed in periodic windows contribute to the coexistence of multiple time scales, adding to the complexity of the system. Typical dynamics in ecosystems, such as the persistence and coexistence of population cycles and chaotic behaviors, the emergence of super-long transients, regime shifts, and the quantifying of resilience, are encapsulated within this single model. The simplicity of our model allows for detailed analysis, reinforcing its potential as a conceptual tool for understanding ecosystems deeply.

q-bio.PE↗
arXiv2026

Mutation Order and Selection Shape Intratumor Heterogeneity in Tumor Evolution

Cancer progression often requires multiple driver mutations, but the same drivers may be acquired in different orders. How these pathways jointly shape tumor clonal structure remains unclear. We develop a multitype branching-process model in which malignant transformation requires two driver mutations, distinguishing malignant cells by mutation order and the independent transformation event that founded their clone. Under a successive exponential approximation, we establish point-process limits for pathway-specific clone sizes and derive a closed-form expression for the limiting expected Simpson's index of the combined malignant population. When both mutation orders yield malignant cells with the same net growth rate, the index decomposes into effective pathway weights, determined by mutation rates and birth-death dynamics at preceding stages, and within-pathway concentration terms, determined by intermediate-to-malignant growth-rate ratios. A driver's effect on heterogeneity thus depends critically on when it is acquired. A strong driver acquired early expands the intermediate lineage and increases the supply of independent malignant founders, whereas the same driver acquired last strengthens the growth and age advantage of early-founded malignant clones. Under additive fitness effects, these opposing mechanisms can produce a non-monotone relationship between selective advantage and clonal concentration. Threshold-like non-additive fitness effects can generate highly concentrated malignant populations, while order-dependent terminal fitness causes the faster-growing pathway to dominate asymptotically. These results show how mutation order, mutational accessibility, selection, and epistasis jointly determine lineage-level intratumor heterogeneity.

q-bio.PE↗
arXiv2026

Phase transitions in microbial lineage trees

Microbial populations exhibit high cell-to-cell variability, which fundamentally shapes population behavior. A striking consequence is the existence of phase transitions, where small genetic or environmental changes trigger abrupt shifts in population dynamics. While biological phase transitions have often been proposed, connecting observed behavior to the underlying physics has remained challenging. We combine population genetics with statistical physics to show how phase transitions arise naturally in microbial populations. We highlight the existence of a first-order transition in a model of bacterial plasmid engineering and find a strict lower bound on the number of plasmids that can be stably maintained in a population.

q-bio.PE↗