Jamerlan C M, Prokopenko M
Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, New South Wales, Australia.
Sydney Institute for Infectious Diseases, The University of Sydney, Sydney, New South Wales, Australia.
R Soc Open Sci. 2024 Oct 2;11(10):240457. doi: 10.1098/rsos.240457. eCollection 2024 Oct.
Spatial contagions, such as pandemics, opinion polarization, infodemics and civil unrest, exhibit non-trivial spatio-temporal patterns and dynamics driven by complex human behaviours and population mobility. Here, we propose a concise generic framework to model different contagion types within a suitably defined contagion vulnerability space. This space comprises risk disposition, considered in terms of bounded risk aversion and adaptive responsiveness and a generalized susceptibility acquisition. We show that resultant geospatial contagion configurations follow intricate Turing patterns observed in reaction-diffusion systems. Pattern formation is shown to be highly sensitive to changes in underlying vulnerability parameters. The identified critical regimes (tipping points) imply that slight changes in susceptibility acquisition and perceived local risks can significantly alter the population flow and resultant contagion patterns. We examine several case studies using Australian datasets (COVID-19 pandemic; crime incidence; conflict exposure during COVID-19 protests; real estate businesses and residential building approvals) and demonstrate that these spatial contagions generated Turing patterns in accordance with the proposed model.
空间传染病，如大流行病、观点两极分化、信息疫情和内乱，呈现出由复杂人类行为和人口流动驱动的非平凡时空模式和动态。在此，我们提出一个简洁的通用框架，以在适当定义的传染脆弱性空间内对不同类型的传染病进行建模。这个空间包括风险倾向，从有界风险厌恶和适应性反应以及广义易感性获取的角度来考虑。我们表明，由此产生的地理空间传染配置遵循在反应扩散系统中观察到的复杂图灵模式。模式形成对潜在脆弱性参数的变化高度敏感。确定的关键状态（临界点）意味着易感性获取和感知到的局部风险的轻微变化会显著改变人口流动和由此产生的传染模式。我们使用澳大利亚数据集（新冠疫情；犯罪发生率；新冠疫情抗议期间的冲突暴露；房地产企业和住宅建筑审批）研究了几个案例研究，并证明这些空间传染病按照所提出的模型产生了图灵模式。
