MOOvPOP is designed to simulate population dynamics (abundance, sex-age composition and distribution in the landscape) of white-tailed deer (Odocoileus virginianus) for a selected sampling region.
The purpose of this model is to better understand the dynamics of a multihost pathogen in two host system comprising of high densities of domestic hosts and sympatric wildlife hosts susceptible to the pathogen.
Leptospirosis is a neglected, bacterial zoonosis with worldwide distribution, primarily a disease of poverty. More than 200 pathogenic serovars of Leptospira bacteria exist, and a variety of species may act as reservoirs for these serovars. Human infection is the result of direct or indirect contact with Leptospira bacteria in the urine of infected animal hosts, primarily livestock, dogs, and rodents. There is increasing evidence that dogs and dog-adapted serovar Canicola play an important role in the burden of leptospirosis in humans in marginalized urban communities. What is needed is a more thorough understanding of the transmission dynamics of Leptospira in these marginalized urban communities, specifically the relative importance of dogs and rodents in the transmission of Leptospira to humans. This understanding will be vital for identifying meaningful intervention strategies.
One of the main objectives of MHMSLeptoDy is to elucidate transmission dynamics of host-adapted Leptospira strains in multi-host system. The model can also be used to evaluate alternate interventions aimed at reducing human infection risk in small-scale communities like urban slums.
An agent-based model is used to simulate legislators’ behavior under secret voting rules, as influenced by the power of the accused politician, the composition of the voting body, and the publicity of the accusations.
MOOvPOPsurveillance was developed as a tool for wildlife agencies to guide collection and analysis of disease surveillance data that relies on non-probabilistic methods like harvest-based sampling.
An agent-based simulation of a game of basketball. The model implements most components of a standard game of basketball. Additionally, the model allows the user to test for the effect of two separate cognitive biases – the hot-hand effect and a belief in the team’s franchise player.
The model is an agent-based artificial stock market where investors connect in a dynamic network. The network is dynamic in the sense that the investors, at specified intervals, decide whether to keep their current adviser (those investors they receive trading advise from). The investors also gain information from a private source and share public information about the risky asset. Investors have different tendencies to follow the different information sources, consider differing amounts of history, and have different thresholds for investing.
An artifcal stock market model that allows users to vary the number of risky assets as well as the network topology that investors forms in an attempt to understand the dynamics of the market.