These parameters are obtained by fitting the model to population-level data; their relation to the individual-level processes may be quite complex and is generally unknown.
This parameter can be obtained through fitting an ODE model to population-level data as mentioned above and will signal epidemic growth whether or not it is equal to the average number of secondary infections.
It is important to note that β and μ are obtained from fitting the model to population-level data, with no clear association to the causal individual-level processes.
The question of whether the R0 obtained by calculating the average number of secondary infections matches the threshold parameter obtained from fitting the epidemiological model to population-level data has been previously studied [8], [9].
We apply our model to populations of individuals who are genetically distinct and equally related, phenotyped in studies where genotype assignment is exchangeable across unmodeled environmental variation.
We, therefore, extended our models to population models with the rationale that populations of neurons would be able to respond more gradually such that the latency to first spike does not dominate the competition.
We conducted a systematic review to assess the application and applicability of existing non-invasive prevalent diabetes risk models to populations within Africa.
As such, this study provides important insights into the applicability of state-dependent evolutionary models to populations facing radically changed environments.
Cisneros-mata et al. [22] used an age-structured, discrete-time model to investigate population dynamics of the Pacific sardine.
We have constructed a collisional-radiative model to calculate population densities of excited states of Be-like Fe and Ne ions, taking into account the recombination processes.
