has led to lower numbers of opossums (which are poor hosts for the pathogen
that causes Lyme disease) and higher numbers of white-footed mice (which are
excellent hosts), leading to more Lyme disease cases in humans.
Figure 2: Mathematical models have helped officials manage foot-and-mouth disease in the U.K.
and in particular led to a series of ring culling strategies that helped control the potentially
devastating 2001 epidemic. (Getty images)
This points to another mathematical need: Mathematical models are a keyway to plan effective responses to disease outbreaks. The need could be even
greater if diseases emerge in new locations or re-emerge in old locations
because of changing climates. (See for instance the map in Figure 3, which
shows places where malaria might re-emerge in the U.S.) Suppose, for example,
that a deadly new virus emerges in Africa: Would we be better off sending our
national stockpiles of medicines to Africa in the hope of containing it, or should
we hang onto them for our own use? Or if terrorists were to release the plague in
Chicago, would it be more effective to administer antibiotics widely, or to impose
mass quarantines? These questions are at the heart of mathematical approaches
to epidemiology. Mathematical models guided the response to cholera in Haiti as
it unfolded after the recent earthquake, helping decide where to put treatment
centers, where to provide palliative care, and how to distribute the very scarce
resources among a huge number of sick people. Models have also been
essential to planning immunization strategies and managing foot-and-mouth
disease in the U.K.