Could society itself spiral into a Lorenz-like chaos when facing an epidemic threat?
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908681073852416 |
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| author | de Carvalho, João P. S. Maurício |
| author_facet | de Carvalho, João P. S. Maurício |
| contents | Understanding how societies react to epidemic threats requires more than tracking infection curves. Public perception, collective memory and behavioural adaptation interact through feedback loops that can amplify or suppress the spread of fear, vigilance and precaution. In this work we reinterpret the classical Lorenz system in a socioepidemic context, governed by nonlinear interactions between perceived infection, social transmission behaviour and memory of past risk. We provide a qualitative analysis of the model and show that small fluctuations in perception or behaviour can trigger transitions between stable, oscillatory and chaotic collective responses. These results suggest that social reactions to epidemics may evolve according to intrinsic dynamical rules, generating complex patterns of vigilance, fatigue and renewed concern that mirror the irregular rhythms observed in real outbreaks. Our findings highlight the importance of incorporating behavioural feedbacks into epidemic modeling and reveal how chaotic dynamics may arise not only from pathogens but from society itself. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Could society itself spiral into a Lorenz-like chaos when facing an epidemic threat? de Carvalho, João P. S. Maurício Dynamical Systems Adaptation and Self-Organizing Systems Understanding how societies react to epidemic threats requires more than tracking infection curves. Public perception, collective memory and behavioural adaptation interact through feedback loops that can amplify or suppress the spread of fear, vigilance and precaution. In this work we reinterpret the classical Lorenz system in a socioepidemic context, governed by nonlinear interactions between perceived infection, social transmission behaviour and memory of past risk. We provide a qualitative analysis of the model and show that small fluctuations in perception or behaviour can trigger transitions between stable, oscillatory and chaotic collective responses. These results suggest that social reactions to epidemics may evolve according to intrinsic dynamical rules, generating complex patterns of vigilance, fatigue and renewed concern that mirror the irregular rhythms observed in real outbreaks. Our findings highlight the importance of incorporating behavioural feedbacks into epidemic modeling and reveal how chaotic dynamics may arise not only from pathogens but from society itself. |
| title | Could society itself spiral into a Lorenz-like chaos when facing an epidemic threat? |
| topic | Dynamical Systems Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2512.00192 |