What drives antigenic drift in a single influenza season?
Maciej F. BoniStanford University
Department of Biological Sciences
DIMACS Workshop on Evolutionary Considerations in Vaccine UseRutgers University, June 29, 2005
Antigenic drift
Defined as the accumulation of point mutations in influenza surface proteins (haemagglutinin and neuraminidase)
Antigenic drift enables influenza to escape host immunity and re-infect populations with previously acquired immunity
Russell Kightley Media rkm.com.au
HA1 987nt
Flu epidemics and antigenic drift
1996 1997 1998
20
weekly illnesses/10,000 inhabitants (NL)
NOV APR
epidemic strain
de Jong et al (2000)
Strains have accumulated mutations. But how many?
mean pairwise distance out of 329 amino acids (4%)
14
max pwd (7%)
24
HA1 polymorphism – within-year
HA1 polymorphism – local datasets
Coiras et al, Arch. Vir. (2001)
Schweiger et al, Med. Microbiol. Immunol. (2002)
Pyhälä et al, J. Med. Virol. (2004)
Neutral Epidemic Model
Number of infections with epidemic-originating strain
Number of infections with a strain k mutations away
Neutral Epidemic Model
Exiting a population class via mutation
Strain frequencies are Poisson-distributed
Frequency of strain k :
Mean number of mutations per virus moves forward in time according to a molecular clock
Modeling antigenic drift and immunity
the epidemic-originating strain
-2 -1 0 1 2 3 4
you may have conferred immunity from a previous season to one of these strains.
Modeling antigenic drift and immunity
the epidemic-originating strain
-2 -1 0 1 2 3 4
Distance between immunizing strain and challenging strain determines level of cross-immunity.
We model this as an infectivity reduction and say it wanes exponentially with distance:
Non-neutral model
Amino-acid replacements in influenza surface proteins confer a fitness benefit via increased transmissibility
Hosts have some immunity structure from vaccination or previous infections
( need both )
Keeping track of hosts
q0 completely immune ( to I0 )
q30 completely naive
j+k is distance between immunizing and challenging (infecting) strain
Keeping track of variables
infectivity reduction by previous infectionwith a strain j amino acids away
force of infection of strain k
total force of infection
Equations
Equations
cross-immunity between strains m amino acids apart
total immunity in population
Equations
fitness of strain k
mean fitness of strain population: W
Population genetics
Define mean antigenic drift in virus population as:
Fisher’s Fundamental Theorem
This is the Price Equation, thus, the basic influenza population dynamicscan be viewed in a standard population genetic framework.
Under neutrality
I(t)
Takes 7 aa-changes to escape 50% immunity
Define the excess antigenic drift as:
How do you know when the epidemic ends?
I(t)
slow immune escape
medium immune escape
fast immune escape
Very few mutations required to escape immunity, so little drift occurs
Little immune escape per mutation, thus little fitness variation for natural selection to act on.
In general, how do the parameters affect the model results?
Partial correlations
immunity :
immune-escape/mutation :
Partial correlations
immunity :
immune-escape/mutation :
Partial correlations
immunity :
immune-escape/mutation :
controllable by vaccination
When sampling from parameter space …
1. if goal is to map out a parameter space, choice of distribution does not matter
2. be careful summarizing relationships between parameters, because choice of distribution may be quite significant
3. non-monotonicity may make PCCs meaningless (e.g. PCC=0 does not imply independence)
4. PCCs assume linear relationships between parameters (PRCCs do not)
5. Remember that you are calculating statistics on deterministic quantities
Host immunity drives antigenic drift
Public health implications
Vaccination strategies: under-vaccination or imperfect vaccination may cause much excess antigenic drift.
Pandemic implications: need to consider the effects of vaccination during the 2nd year after a pandemic, and their effects on the 3rd year after a pandemic.
Thanks
Marcus W. FeldmanStanford University, Department of Biological Sciences
Julia R. GogCambridge University, Department of Zoology
Viggo AndreasenUniversity of Roskilde, Department of Mathematics and Physics
Freddy B. ChristiansenUniversity of Aarhus, Department of Biology
( and for funding to NIH grant GM28016, NSF, and Stanford University )