Mathematical modeling has become a mainstream approach in scientific research when it is not feasible to design adequate experimentation. Agent based modeling provides rapid assessments of possible scenarios, especially in epidemiological studies where the goal is to carry out quick and effective preventive steps when an infection breaks out. In this paper, the relationships between infectiousness, chance of recovery, duration of a micro-parasitic disease, and the population dynamics of the host via parameter sweeps in a simple model system were analyzed. A disease with a very low infection rate is unable to spread in the population, unless the population density is high and the duration of the sickness is long. In cases of low recovery rates the number of sick and immune individuals showed a maximum value at 30% infectiousness. As the size of the population decreases, the population density decreases, and therefore, the transmission rate decreases as well. This effect leads to a new equilibrium population size, where the number of births and deaths will be balanced, and a large proportion of the population has acquired immunity. Based on these simulation models, the dynamic properties of populations contribute to the resilience of illness due to infections from micro-parasitic diseases.