Optimal intervention campaign to combat anti-vaccine social contagion and contain epidemic spread: Impact of network structure
Optimal intervention campaign to combat anti-vaccine social contagion and contain epidemic spread: Impact of network structure
Vaccine misinformation fuels vaccine hesitancy, spreading through social networks and can thus lead to the formation of unprotected communities, increasing the risk of larger-scale disease outbreaks. In this study, through an agent-based model that integrates coupled diffusion processes—vaccine opinions and disease diffusion—we design counter-campaigns that counteract vaccine misinformation to promote vaccine uptake aiming to curb the spread of an epidemic. We frame this as an optimization problem, developing adaptive targeting strategies that respond to evolving vaccine attitudes subject to budget constraints. We find that the efficiency of campaigns depends on both the network structure and the timing of the intervention. For early intervention, we demonstrate that targeting neutral individuals connected to anti-vaccine opinion adopters within their social networks can effectively limit the spread of negative influence. Moreover, we find that targeting agents that have the potential to propagate the positive influence in their neighbourhoods is significantly more effective than solely protecting the most vulnerable agents from negative influence. For late intervention, as large anti-vaccine communities begin to emerge, shielding bridging regions in smallworld and regular lattice networks becomes a more effective containment strategy. However, this approach is less effective in scale-free and random networks due to the distinct clustering patterns observed there. We also find that controlling negative opinion diffusion becomes more challenging the longer the intervention is delayed. However, it can be controlled more efficiently with fewer resources in small-world and regular lattice networks than in others.
Alahmadi, Sarah Hamed
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Hoyle, Rebecca B.
e980d6a8-b750-491b-be13-84d695f8b8a1
Head, Michael
67ce0afc-2fc3-47f4-acf2-8794d27ce69c
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Alahmadi, Sarah Hamed
1553acb6-3d5e-4c5b-bb3e-551927419348
Hoyle, Rebecca B.
e980d6a8-b750-491b-be13-84d695f8b8a1
Head, Michael
67ce0afc-2fc3-47f4-acf2-8794d27ce69c
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Alahmadi, Sarah Hamed, Hoyle, Rebecca B., Head, Michael and Brede, Markus
(2025)
Optimal intervention campaign to combat anti-vaccine social contagion and contain epidemic spread: Impact of network structure.
Applied Network Science.
(In Press)
Abstract
Vaccine misinformation fuels vaccine hesitancy, spreading through social networks and can thus lead to the formation of unprotected communities, increasing the risk of larger-scale disease outbreaks. In this study, through an agent-based model that integrates coupled diffusion processes—vaccine opinions and disease diffusion—we design counter-campaigns that counteract vaccine misinformation to promote vaccine uptake aiming to curb the spread of an epidemic. We frame this as an optimization problem, developing adaptive targeting strategies that respond to evolving vaccine attitudes subject to budget constraints. We find that the efficiency of campaigns depends on both the network structure and the timing of the intervention. For early intervention, we demonstrate that targeting neutral individuals connected to anti-vaccine opinion adopters within their social networks can effectively limit the spread of negative influence. Moreover, we find that targeting agents that have the potential to propagate the positive influence in their neighbourhoods is significantly more effective than solely protecting the most vulnerable agents from negative influence. For late intervention, as large anti-vaccine communities begin to emerge, shielding bridging regions in smallworld and regular lattice networks becomes a more effective containment strategy. However, this approach is less effective in scale-free and random networks due to the distinct clustering patterns observed there. We also find that controlling negative opinion diffusion becomes more challenging the longer the intervention is delayed. However, it can be controlled more efficiently with fewer resources in small-world and regular lattice networks than in others.
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applied_network_science_paper_accepted
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Accepted/In Press date: 16 June 2025
Identifiers
Local EPrints ID: 505015
URI: http://eprints.soton.ac.uk/id/eprint/505015
PURE UUID: 9fd74109-8496-42dd-8bda-8c452554ec8e
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Date deposited: 24 Sep 2025 16:31
Last modified: 25 Sep 2025 01:48
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Author:
Sarah Hamed Alahmadi
Author:
Markus Brede
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