Population dynamics are almost inevitably associated with two predominant sources of variation: the first, demographic variability, a consequence of chance in progenitive and deleterious events; the second, initial state uncertainty, a consequence of partial observability and reporting delays and errors. Here we outline a general method for incorporating randominitialconditions in population models where a deterministic model is sufficient to describe the dynamics of the population. Additionally, we show that for a large class of stochastic models the overall variation is the sum of variation due to randominitialconditions and variation due to random dynamics, and thus we are able to quantify the variation not accounted for when random dynamics are ignored. Our results are illustrated with reference to both simulated and real data.
Pollett, P. K., Dooley, A. H., & Ross, J. V. (2010). Modelling population processes with random initial conditions. Mathematical Biosciences, 223(2), 142-150. https://doi.org/10.1016/j.mbs.2009.11.008