Improving Computational Models and Practices

Abstract

Since infectious diseases pose a significant risk to human health many countries aim to control their spread. Public health bodies faced with a disease threat must understand the disease’s progression and its transmission process. From this understanding it is possible to evaluate public health interventions intended to decrease impacts on the population. Commonly, contingency planning has been achieved through epidemiological studies and the use of relatively simple models. However, computational methods increasingly allow more complex, and potentially more realistic, simulations of various scenarios of the control of the spread of disease. However, understanding computational results from more sophisticated models can pose considerable challenges. A case study of a system combining a complex infectious disease model with interactive visualization and computational steering tools shows some of the opportunities this approach offers to infectious disease control.