Roni Rosenfeld

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.