Abstract: Focusing on pandemic influenza, this chapter approaches the planning
for and response to such a major worldwide health event as a complex
engineering systems problem. Action-oriented analysis of pandemics requires a
broad inclusion of academic disciplines since no one domain can cover a
significant fraction of the problem. Numerous research papers and action plans
have treated pandemics as purely medical happenings, focusing on hospitals,
health care professionals, creation and distribution of vaccines and
anti-virals, etc. But human behavior with regard to hygiene and social
distancing constitutes a first-order partial brake or control of the spread and
intensity of infection. Such behavioral options are "non-pharmaceutical
interventions." (NPIs) The chapter employs simple mathematical models to study
alternative controls of infection, addressing a well-known parameter in
epidemiology, R_{0}, the "reproductive number," defined as
the mean number of new infections generated by an index case. Values of
R_{0} greater than 1.0 usually indicate that the infection
begins with exponential growth, the generation-to-generation growth rate being
R_{0}. R_{0} is broken down into
constituent parts related to the frequency and intensity of human contacts,
both partially under our control. It is suggested that any numerical value for
R_{0} has little meaning outside the social context to which
it pertains. Difference equation models are then employed to study the effects
of heterogeneity of population social contact rates, the analysis showing that
the disease tends to be driven by high frequency individuals. Related analyses
show the futility of trying geographically to isolate the disease. Finally, the
models are operated under a variety of assumptions related to social distancing
and changes in hygienic behavior. The results are promising in terms of
potentially reducing the total impact of the pandemic.