Modeling the Evolution of Viral Epidemics.
All RNA viruses have
roughly the same underlying mutation rate (~3 x 10^-5 errors per replication
per base). However, some viruses, like
Measles or Dengue, appear trapped in a genetic or antigenic
“corner”, such that one time exposure of a host results in
near-lifetime immunity. Others, like HIV
and Influenza, exhibit great genomic variability with sufficient fitness to
allow them to evade immune and drug pressure, frequently jump across species barriers,
and undergo significant neutral drift.
Newly emerging viruses may fall anywhere in between these two
extremes. Given previous pandemics and
panzoonotics, and the potential for emerging viruses to adversely affect the
health of human and other animal populations it is clearly essential to understand
the factors that allow viruses to enter and spread through new host populations.
It may ultimately be possible to predict what type of virus may emerge in
populations in the future, where such emergence is likely to occur, and what
species are most likely to act as reservoirs.
The antigenic landscape
of Influenza virus is complex, and so is the distribution of existing
protective immunity. Host jumping to
humans is a stochastic process that cannot be predicted with certainty, but
some jumps are more likely than others, and some intermediate steps can be
detected. Strains recently introduced
into humans from other species tend to be less efficient, and hence less
infectious, than strains that have had ample time to adapt to a human
host. During a typical annual epidemic
multiple strains dominate, and many more circulate at a lower intensity. The high mutation rate of Influenza, coupled
with its enormous antigenic and genomic plasticity, results in significant
responsiveness of the virus to environmental, seasonal, behavioral and policy
factors, well within the timespan of the pandemic.
The tremendous recent
growth in monitoring, reporting and sequencing of influenza, coupled with the
growth in computational power and advances in agent-based simulation methods,
make it possible for the first time to attempt to understand the impact the
evolutionary dynamics of Influenza (and by extension of other infectious
agents) has on the course of typical annual epidemics, and on the timing and
course of a future pandemic. This may
influence policy recommendation for possible public health interventions. In turn, any intervention may strongly impact
the future evolution of influenza.