VaxPulse
Leveraging machine learning to combat vaccine misinformation and improve public health outcomes.
Background
Vaccine misinformation is a growing threat to immunisation systems worldwide. VaxPulse is an innovative machine learning-based platform designed to tackle this issue by monitoring and analysing multi-lingual online content from various sources such as social media, search engines, and news media. This allows us to produce accurate vaccine infodemic risk assessments and better understand misinformation trends. VaxPulse is a collaborative effort with partners in the Asia Pacific region, the World Health Organization, and Canada.
What we’re doing
VaxPulse continuously analyses online content to identify and assess the risk of vaccine misinformation. As a learning health system, VaxPulse will develop and test targeted response strategies in simulated environments to ensure they are effective in real-world scenarios. These responses are deployed to combat misinformation through various channels, including social media, and their impact is analysed to refine the platform’s capabilities. This ongoing cycle of analysis and improvement ensures that VaxPulse provides government agencies and vaccine uptake groups with real-time insights and effective strategies to respond swiftly and effectively to vaccine-related concerns.
Impact
VaxPulse will deliver the following outcomes:
- Improved vaccine uptake in collaborating nations through the innovative use of machine learning and targeted communication strategies.
- Advanced scientific discovery and technological development to manage the infodemic, a growing global threat to immunisation efforts.
- Capacity building across partner sites, including electronic media surveillance strategies, response development, and the training of the next generation of researchers.
- Reduced impact of vaccine-preventable morbidity and mortality on communities and regional economies.
Publications
G.L. Dimaguila, M. Javed, H. Clothier, J. Hickman, D. Petrovic, F. Machingaifa, J. Kaufman, S.K. Habibabadi, C. Palmer, and J. Buttery, Interdisciplinary Learning Health System Response to Public Vaccine Concerns, Interdisciplinary Learning Health System Response to Public Vaccine Concerns 310 (2024), 1146-1150.
C. Palmer, S.K. Habibabadi, M. Javed, G.L. Dimaguila, and J. Buttery, Fertility-Related Conversations in the Context of COVID-19 and Vaccinations, Studies in Health Technology and Informatics 310 (2024), 644-648.
M. Javed, G.L. Dimaguila, S.K. Habibabadi, C. Palmer, and J. Buttery, Learning from Machines? Social Bots Influence on COVID-19 Vaccination-Related Discussions: 2021 in Review, in: Proceedings of the 2023 Australasian Computer Science Week, Association for Computing Machinery, Melbourne, VIC, Australia, 2023, pp. 190–197.
S. Khademi Habibabadi, C. Palmer, G.L. Dimaguila, M. Javed, H.J. Clothier, and J. Buttery, Australasian Institute of Digital Health Summit 2022–Automated Social Media Surveillance for Detection of Vaccine Safety Signals: A Validation Study, Appl Clin Inform14 (2023), 01-10.
Contact
Dr Gerardo Luis Dimaguila
Informatics Lead, Data Innovation Lead
Dr Muhammad Javed
Research Officer
Professor Jim Buttery
Chair of Health Informatics
Logo design by Holli Hickman