Heterogeneous population and its resilience to misinformation in vaccination uptake: A dual ODE and network approach
Komal Tanwar, Viney Kumar, Jai Prakash Tripathi

TL;DR
This study models how misinformation impacts vaccination rates using dual ODE and network approaches, revealing that population heterogeneity and network topology significantly influence vaccine uptake and disease spread.
Contribution
It introduces a combined ODE and network modeling framework to analyze misinformation effects on vaccination in heterogeneous populations, highlighting the role of network topology.
Findings
Misinformation reduces vaccination rates more in homogeneous populations.
Small-world networks maintain higher vaccination levels despite misinformation.
Scale-free networks show decreased vaccine coverage with increased misinformation.
Abstract
Misinformation about vaccination poses a significant public health threat by reducing vaccination rates and increasing disease burden. Understanding population heterogeneity can aid in recognizing and mitigating the effects of such misinformation, especially when vaccine effectiveness is low. Our research quantifies the impact of misinformation on vaccination uptake and explores its effects in heterogeneous versus homogeneous populations. We employed a dual approach combining ordinary differential equations (ODE) and complex network models to analyze how different epidemiological parameters influence disease spread and vaccination behaviour. Our results indicate that misinformation significantly lowers vaccination rates, particularly in homogeneous populations, while heterogeneous populations demonstrate greater resilience. Among network topologies, small-world networks achieve higher…
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Taxonomy
TopicsCOVID-19 epidemiological studies
