Spatio-temporal chaos and clustering induced by nonlocal information and vaccine hesitancy in the SIR epidemic model

Human behavior, and in particular vaccine hesitancy, is a critical factor for the control of childhood infectious disease. Here we propose a spatio-temporal behavioral epidemiology model where the vaccine propensity depends on information that is non-local in space and in time. The properties of the...

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Veröffentlicht in:Chaos, solitons and fractals solitons and fractals, 2023-05, Vol.170, p.113339, Article 113339
Hauptverfasser: Banerjee, Malay, Ghosh, Samiran, Manfredi, Piero, d’Onofrio, Alberto
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Sprache:eng
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Zusammenfassung:Human behavior, and in particular vaccine hesitancy, is a critical factor for the control of childhood infectious disease. Here we propose a spatio-temporal behavioral epidemiology model where the vaccine propensity depends on information that is non-local in space and in time. The properties of the proposed model are analyzed under different hypotheses on the spatio-temporal kernels tuning the vaccination response of individuals. As a main result, we could numerically show that vaccine hesitancy induces the onset of many dynamic patterns of relevance for epidemiology. In particular we observed: behavior-modulated patterns and spatio-temporal chaos. This is the first known example of human behavior-induced spatio-temporal chaos in statistical physics of vaccination. Patterns and spatio-temporal chaos are difficult to deal with, from the Public Health viewpoint, hence showing that vaccine hesitancy can cause them could be of interest. Additionally, we propose a new simple heuristic algorithm to estimate the Maximum Lyapunov Exponent. •Human behavior-based spatio-temporal SIR type epidemic model is considered.•The model includes vaccine hesitancy in relation to childhood immunization.•The behavioral response is assumed to be fully non-local over space and time.•Resulting patterns are triggered by the vaccination response and the spatio-temporal information kernels.•Spatio-temporal chaos is among the observed patterns.
ISSN:0960-0779
1873-2887
DOI:10.1016/j.chaos.2023.113339