度相关和距离相关接触率在爆炸性、指数和多项式流行病增长之间的插值
摘要
估计、模型和预测疫情增长率是流行病学中的一个基本问题。类似地，分析创新、（虚假）新闻、 meme 和谣言的扩散在社会科学中至关重要。 resulting epidemic growth curves can be classified according to their growth rates. These have been found to range from exponential to both faster super-exponential curves and slower subexponential or polynomial curves. Previous research has lacked a unified explanatory framework capable of accommodating super-exponential, (stretched) exponential, and polynomial growth patterns within the same contact network. In this paper we propose a simple agent-based network model that can capture all these phases. We provide such a framework by modelling how transmission rates depend on spatial distance and on individuals' numbers of contacts. By comparing the growth rate of spreading processes with or without degree-dependent and/or distance-dependent contact rates through data-driven and synthetic simulations on real and modelled networks with underlying geometry, we find evidence that even a 'sublinear presence' of these causes may cause a significant slow down of the growth rate on the same underlying network. We find that the growth rate is governed by a combination of three factors: geometry, the prevalence of weak ties, and superspreaders. We confirm our results with rigorous proofs in a theoretical model, using a spatial multiscale-argument in long-range heterogeneous first passage percolation. Our results give a plausible explanation of why the consecutive waves of a single pandemic can differ in their growth even if their spreading mechanisms are similar.
引用
@article{arxiv.2604.26939,
title = {Degree-dependent and distance-dependent contact rates interpolate between explosive, exponential and polynomial epidemic growth},
author = {Zylan Benjert and Júlia Komjáthy and Johannes Lengler and John Lapinskas and Ulysse Schaller},
journal= {arXiv preprint arXiv:2604.26939},
year = {2026}
}