Vaccines Meet Big Data: State-of the-Art and Future Prospects. From the Classical 3is ("Isolate-Inactivate-Inject") Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview

This article explores the potential roles as well as pitfalls and challenges of so-called Big Data in shaping the future of vaccinology, moving toward a tailored and personalised vaccine design and administration. It describes conventional vaccinology, born in 1796, as "vaccinology 1.0". Examples of "second generation" vaccines include vaccines against tetanus, diphtheria, anthrax, pneumonia, influenza, hepatitis B, and Lyme disease. The shift then to "vaccinology 3.0" has been possible thanks to Big Data, characterised by high volume, velocity, and variety of data. Big Data sources include new cutting-edge, high-throughput technologies, electronic registries, social media, and social networks, among others.

As explained here, the Big Data era is characterised by the widespread diffusion of new information and communication technologies (ICTs). Electronic health (eHealth) generates an enormous wealth of data. Researchers have found that, usually, digital activities correlate with offline behaviours and other variables, such as vaccination knowledge and perception of own risk.

Within the new (vaccinology 3.0) framework, Big Data hold promises and opportunities, which the article discusses. The roles of Big Data in vaccination range from vaccine discovery and development (omics technologies and wet-lab approaches) to vaccination campaign and vaccine safety monitoring (electronic registries, social media/networks, and digital epidemiology). On the latter, novel data streams, such as mobile/smartphone applications, can be utilised in the monitoring and management of vaccine-related data and in the quest to see how often people Google for vaccination and for vaccination-related adverse events.

Big Data also enable the tracking and monitoring of interest about vaccination practices. "The increasing phenomenon of vaccine hesitancy (an umbrella term that includes indecision, uncertainty, delay and reluctance) is multifactorial, and closely linked to social contexts, with different determinants, ranging from geographical area, to political situation, complacency, convenience and confidence in vaccines. Novel data streams, providing a snapshot of perceptions of vaccination in a given place and at a specific time, could be used to assess lay-people's perceptions of vaccination, enabling health-care workers to actively engage citizens and to plan ad hoc communication strategies and plans to contain vaccine hesitancy and to promote vaccine literacy..."

Several examples of the role of Big Data and vaccine literacy/vaccine hesitancy are provided, such as Project Tycho, launched by the University of Pittsburgh, United States (US). Organisers have digitised all weekly surveillance reports of notifiable diseases for US cities and states published in the period between 1888 and 2011. This data set consists of 87,950,807 reported individual cases and has been used to derive a quantitative history of disease dynamics and transmission that underlines the positive effect of vaccination programmes. This use of big data emphasises the dimension of "veracity", through which is possible to contrast vaccine-related "fake news" and "post-modern, post-factual truths", disseminated by the anti-vaccination movements.

In conclusion: "Big Data have contributed and are expected to continue contributing toward facilitating the discovery, development, production, and delivery of rationally designed vaccines....However, a number of pitfalls and challenges should be properly recognized to be addressed by future research...[E]fforts should be done to preserve and protect privacy, confidentiality, and identity."

"Currently, we are only witnessing the very beginning of the ongoing 'Big Data revolution.'"