Epidemiological scenarios#
We can use coalestr to model how different epidemiological scenarios affect parasite genetic diversity and within-host variation. For example, we can explore:
how population genetic diversity and within-host variation are affected by changes in \(N_h\), \(Q\), \(\chi\) and \(N_m\)
how transmission parameters interact in their genetic effects
how different epidemiological scenarios can lead to the same genetic effects
the time it takes for changes in transmission parameters to be reflected in genetic changes
In modelling epidemiological scenarios we are typically interested in events that are relatively local and recent, e.g. what is happening in a particular district of a malaria-endemic country at the present time, as opposed to events that have occurred in the global parasite population over the past thousand years.
The problem is that local genetic diversity is greatly affected by migration from surrounding regions and global parasite dispersal. Typical mean coalescence times in a local parasite subpopulation (based on measurements of nucleotide diversity) are around 4000-5000 years, so we cannot ignore the history of the global parasite metapopulation when we are analysing the current levels of genetic diversity in a local parasite population.
Thus before we can model a recent epidemiological scenario we need to consider the history of the global parasite metapopulation and how this is linked by migration to the local subpopulation.