The models will then be used to assess the impacts of population density on transmission of different pathogens under different scenarios of sanitation interventions.
We assume low density of transmissions, where the outage probability expressions are good approximations.bThis assumes that s>R, which is the case in most networks, as it ensures that the receiver can detect its own transmitter.
However, early in infection, parasite genotype was important in determining how increases in inflammation affected the density of transmission-stage parasites; whether inflammation cued gametocytogenesis depended on P. chabaudi parasite genotype (Long et al. 2008a).
Testing the impact of parasite density on transmission potential requires quantifying the prevalence of sporozoite positive mosquitoes, the density of sporozoites in salivary glands and the survival of mosquitoes.
Typically, there are many risk factors driving the emergence of the epidemic including population density, degree of transmission, degree of contagion, contact nature, water condition, and climate and so on [13, 14].
Likewise, d b is the probability density of the transmission delay in the back channel.
In our calculations, we employ a tight-binding model determining total and local density of states, transmission function and participation number.
In particular, using a tight-binding model, we have calculated the density of states, transmission function, participation number and local density of states of the structure with and without defects.
This paper is organized as follows: In the 'Methods' section, we describe the model and theoretical tools that we used to calculate the local density of states, transmission, and current voltage curves of the system.
