This stock-flow simulation model is to show Covid-19 virus spread rate, sources of spreading and safety measures followed by all the countries affected around the world.
The simulation also aims at predicting for how much more period of time the virus will persist, how many people could recover at what kind of rate and also about the virus toughness dependence based on its excessive speed, giving rise to bigger numbers day-by-day.
Data from two rounds of using Disease Participatory Simulation in class. Participants + Androids = 39. By adjusting Rate Constant, stocks and flows representation can be used to match data from either Trial 1 or Trial 2. An example of matching Trial 1 is shown when this simulation is run. Graph of "Area" (Well * Sick) has the same shape as Rate Catching graph. The Rate Catching graph is much smaller because the Well * Sick values are multiplied by a small constant that is the Rate Constant.
This model simulates a waterborne illness spread from a central reservoir. It illustrates the combination of System Dynamics (modeling pathogen levels in the reservoir) and Agent Based Modeling.
Make sure to check out the Map display to see the geographic clustering of disease incidence around the reservoir.
Dosage per day, Doses per day, Every ? hours, Medicine in Intestines, Drug absorption, Plasma level, Blood volume, Plasma concentration, Toxic level, Medicinal level, Drug excretion, Excretion rate, Half-Life
This systems model will help students understand the different systems that make up our body and how choices we make can impact how those systems work. Factors are based on daily choices.
A spatially aware, agent based model of disease spread. There are three classes of people: susceptible (healthy), infected (sick and infectious), and recovered (healthy and temporarily immune).
