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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

Overview of attention for article published in Frontiers in Public Health, March 2018
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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6 X users

Citations

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58 Dimensions

Readers on

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159 Mendeley
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Title
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
Published in
Frontiers in Public Health, March 2018
DOI 10.3389/fpubh.2018.00062
Pubmed ID
Authors

Nicola Luigi Bragazzi, Vincenza Gianfredi, Milena Villarini, Roberto Rosselli, Ahmed Nasr, Amr Hussein, Mariano Martini, Masoud Behzadifar

Abstract

Vaccines are public health interventions aimed at preventing infections-related mortality, morbidity, and disability. While vaccines have been successfully designed for those infectious diseases preventable by preexisting neutralizing specific antibodies, for other communicable diseases, additional immunological mechanisms should be elicited to achieve a full protection. "New vaccines" are particularly urgent in the nowadays society, in which economic growth, globalization, and immigration are leading to the emergence/reemergence of old and new infectious agents at the animal-human interface. Conventional vaccinology (the so-called "vaccinology 1.0") was officially born in 1796 thanks to the contribution of Edward Jenner. Entering the twenty-first century, vaccinology has shifted from a classical discipline in which serendipity and the Pasteurian principle of the three Is (isolate, inactivate, and inject) played a major role to a science, characterized by a rational design and plan ("vaccinology 3.0"). This shift has been possible thanks to Big Data, characterized by different dimensions, such as 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. The current mini-review aims at exploring the potential roles as well as pitfalls and challenges of Big Data in shaping the future vaccinology, moving toward a tailored and personalized vaccine design and administration.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 159 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 22 14%
Student > Ph. D. Student 20 13%
Researcher 18 11%
Student > Bachelor 14 9%
Other 11 7%
Other 23 14%
Unknown 51 32%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 13%
Medicine and Dentistry 20 13%
Immunology and Microbiology 13 8%
Agricultural and Biological Sciences 10 6%
Nursing and Health Professions 7 4%
Other 35 22%
Unknown 53 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 November 2020.
All research outputs
#14,283,318
of 25,385,509 outputs
Outputs from Frontiers in Public Health
#3,625
of 14,066 outputs
Outputs of similar age
#167,577
of 347,366 outputs
Outputs of similar age from Frontiers in Public Health
#72
of 116 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,066 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 347,366 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.