In nature many pathogens and in particular strains of pathogens with negative effects on species coexists. This is for simplicity often ignored in many epidemiological models. It is however still of interest to get a deeper understanding how this coexistence affects the dynamics of the disease. There are several ways at which coexistence can influence the dynamics. Coinfection which is the simultaneous infection of two or more pathogens can cause increased detrimental health effects on the host. Pathogens can also limit each others growth by the effect of cross immunity as well as promoting isolation. On the contrary one pathogen can also aid another by making the host more vulnerable to as well as more inclined to spread disease.
Spread of disease is dependent on the density of the population. If a pathogen is able to spread or not, is strongly correlated with how many times individuals interact with each other. This in turn depends on how many individuals live in a given area.
The aim of papers I-III is to provide an understanding how different factors including the carrying capacity of the host population affect the dynamics of two coexisting diseases.
In papers I-III we investigate how the parameters effects the long term solution in the form of a stable equilibrium point. In particular we want to provide an understanding of how changes in the carrying capacity affects the long term existence of each disease as well as the occurrence of coinfection.
The model that is studied in papers I-III is a generalization of the standard susceptible, infected, recovered (SIR) compartmental model. The SIR model is generalized by the introduction of the second infected compartment as well as the coinfection compartment. We also use a logistic growth term à la Verhulst with associated carrying capacity K. In paper I and II we make the simplifying assumption that a coinfected individual has to, if anything, transmit both of the disease and simply not just one of them. This restriction is relaxed in paper III. In all papers I-III however we do restrict ourselves by letting all transmission rates, that involves scenarios where the newly infected person does not move to same compartment as the infector, to be small. By small we here mean that the results at least hold when the relevant parameters are small enough.
In all paper I-III it turns out that for each set of parameters excluding K there exist a unique branch of mostly stable equilibrium points depending continuously on K. We differentiate the equilibrium points of the branch by which compartments are non-zero which we refer to as the type of the equilibrium. The way that the equilibrium point changes its type with K is made clear with the use of transition diagrams together with graphs for the stable susceptible population over K.
In paper IV we consider a model for a single age-structured population á la Mckendric-von-Foerster with the addition of differing density dependence on the birth and death rates. Each vital rate is a function of age as well as a weighing of the population also referred to as a size. The birth rate influencing size and the death rate influencing size can be weighted differently allowing us to consider different age-groups to influence the birth and death rate in different proportions compared to other age groups.
It is commonly assumed that an increase of population density is detrimental to the survival of each individual. However, for various reasons, it is know that for some species survival is positively correlated with population density when the population is small. This is called the Allee effect and our model includes this scenario.
It is shown that the trivial equilibrium, which signifies extinction, is locally stable if the basic reproductive rate $R_0$ is less then 1. This implies global stability with certain extinction if no Allee effect is present. However if the Allee effect is present we show that the population can persist even if R_{0 }< 1.