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The following variables identified a priori were included in the logistic regression model: age, parasite density, study population (asymptomatic or symptomatic) and molecular markers.
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In both models, age, sex, parasite status (infected or not), time of day and month were included as predictor variables, along with two way interactions between parasite status and each of age, sex and year, to determine whether any effects of parasitism on immunological variables varied between ages, sexes or years.
This would be called a model age.
aMinimally adjusted model: age.
A parasite population growth model comprising parasite multiplication, decline in erythrocyte count with increasing parasitaemia and parasite clearance after drug administration was developed.
We tested two different models: one excluding (linear model) and one including (non-linear model) squared parasite intensity.
Age and parasite densities had an inverse relationship.
Modeling plasmodium parasite arrival in the Galapagos Penguin (Spheniscus mendiculus).
The development and design of mathematical models of parasite transmission can contribute to parasite control monitoring programmes.
They showed that the model exhibits parasite-induced host extinction.
In contrast, P. chabaudi is generally thought to indiscriminately infect RBCs of all ages, but parameter estimates from a recent model show that parasites invade older RBCs at a rate that is an order of magnitude higher than for reticulocytes.
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