Faculty at the University of South Florida has developed a simulation-based tool to find the optimal way to mitigate the impact of an influenza pandemic across multiple regions. It considers multiple mitigation strategies, including vaccination, antivirals, voluntary quarantines, and social distancing. A key feature is that it can be used in real-time as a pandemic progresses. From the article:
(a) the model is capable of re-allocating resources remaining from the previous allocations and thus achieves a more efficient resource utilization; (b) the model incorporates the costs of the resources and aims to allocate a total available budget, as opposed to allocating available quantities of individual resources, which vary in their relative cost and effectiveness.
Snapshot of the decision-aid simulation GUI
The complete citation is Andrés Uribe-Sánchez, Alex Savachkin, Alfredo Santana, Diana Prieto-Santa and Tapas K. Das, A predictive decision-aid methodology for dynamic mitigation of influenza pandemics, OR Spectrum, Volume 33, Number 3, 751-786, DOI: 10.1007/s00291-011-0249-0
The May-June 2011 issue of Interfaces has an article by Dionne M. Aleman, Theodorus G. Wibisono, and Brian Schwartz (all from Toronto, Ontario) that describes a agent based simulation model of an influenza pandemic in greater Toronto (which has a population of almost 5 million people). The model is used to evaluate the impact of mitigation strategies on the number of people infected. The model estimates the extent to which an outbreak is less severe as more infected persons stay home.
The citation is Dionne M. Aleman, Theodorus G. Wibisono, and Brian Schwartz, A Nonhomogeneous Agent-Based Simulation Approach to Modeling the Spread of Disease in a Pandemic Outbreak, INTERFACES 2011 41: 301-315.
Led by Professor Sunderesh Heragu, A group at the University of Louisville effectively used simulation models to help the city of Louisville plan for mass vaccinations during the H1N1 pandemic. The National Institute for Hometown Security has an article here. See also their video about the model.
Researchers at Johns Hopkins University used a computer simulation model to study the spread of drug-resistant tuberculosis (MDR-TB).
The simulation model was developed in NetLogo.
The complete citation is Bishai JD, Bishai WR, Bishai DM, 2010 Heightened Vulnerability to MDR-TB Epidemics after Controlling Drug-Susceptible TB. PLoS ONE 5(9): e12843. doi:10.1371/journal.pone.0012843