Prediction (early recognition) of emerging flu strain clusters

X. Li and J.C. Phillips

Physica A: Statistical Mechanics and its Applications, 2017, vol. 479, issue C, 371-378

Abstract: Early detection of incipient dominant influenza strains is one of the key steps in the design and manufacture of an effective annual influenza vaccine. Here we report the most current results for pandemic H3N2 flu vaccine design. A 2006 model of dimensional reduction (compaction) of viral mutational complexity derives two-dimensional Cartesian mutational maps (2DMM) that exhibit an emergent dominant strain as a small and distinct cluster of as few as 10 strains. We show that recent extensions of this model can detect incipient strains one year or more in advance of their dominance in the human population. Our structural interpretation of our unexpectedly rich 2DMM involves sialic acid, and is based on nearly 6000 strains in a series of recent 3-year time windows. Vaccine effectiveness is predicted best by analyzing dominant mutational epitopes.

Keywords: Flu; Mutation; Prediction; Pandemic; Map; Vaccine (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437117302157
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:479:y:2017:i:c:p:371-378

DOI: 10.1016/j.physa.2017.02.073

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().