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Bovine spongiform encephalopathy (BSE), widely known as "mad cow disease", has virtually crippled the British livestock industry. Even though, no cases of BSE have been reported in the United States (US), a similar epidemic in the US would be catastrophic. The added concern for the risk of introduction of the human disease called variant Creutzfeldt-Jacob disease that has been linked to eating meat of BSE infected cattle compounds the risk of BSE. Systems dynamics models based on the underlying transmission pathways of BSE can help to anticipate the spread of this disease in different cattle populations and assist in the evaluation of potential risk mitigations for preventing its introduction or controlling its spread if it was introduced. With this in mind, an age and health status structured systems dynamics model was developed. By making assumptions and setting up feasible scenarios, the model can be used to examine potential prevalence and incidences rates of BSE; the effect of mitigations including changes in feeding habits or rendering processes and/or other policies and regulations designed to prevent the introduction of BSE. The systems dynamics simulation model enabled us to create virtual experiments whose real-world analogues would otherwise be expensive, dangerous, or even impossible to carryout.

Computational models can facilitate the understanding of complex biomedical systems such as in HIV/AIDS. Untangling the dynamics between HIV and CD4+ cellular populations and molecular interactions can be used to investigate the effective points of interventions in the HIV life cycle. With that in mind, we have developed a state transition systems dynamics and stochastic model that can be used to examine various alternatives for the control and treatment of HIV/AIDS. The specific objectives of our study were to use a cellular/molecular model to study optimal chemotherapies for reducing the HIV viral load and to use the model to study the pattern of mutant viral populations and resistance to drug therapies. The model considers major state variables (uninfected CD4+ lymphocytes, infected CD4+ cells, replicated virions) along with their respective state transition rates (viz. CD4+ replacement rate, infection rate, replication rate, depletion rate). The state transitions are represented by ordinary differential equations. The systems dynamics model was used for a variety of computational experimentations to evaluate HIV mutations, and to evaluate effective strategies in HIV drug therapy interventions.

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