Classical epidemiology has generally relied on the description and explanation of the
occurrence of infectious diseases in relation to time occurrence of events rather than
to place of occurrence. In recent times, computer generated dot maps have facilitated
the modeling of the spread of infectious epidemic diseases either with classical statistics
approaches or with artificial ``intelligent systems''. Few attempts, however, have been made
so far to identify the origin of the epidemic spread rather than its evolution by mathematical
We report on the use of a new artificial intelligence method (the H-PST Algorithm)
and we compare this new technique with other well known algorithms to identify the
source of three examples of infectious disease outbreaks derived from literature. The
H-PST algorithm is a new system able to project a distances matrix of points (events)
into a bi-dimensional space, with the generation of a new point, named hidden unit.
This new hidden unit deforms the original Euclidean space and transforms it into a new
space (cognitive space). The cost function of this transformation is the minimization of
the differences between the original distance matrix among the assigned points and the
distance matrix of the same points projected into the bi-dimensional map (or any different
set of constraints). For many reasons we will discuss, the position of the hidden unit
shows to target the outbreak source in many epidemics much better than the other classic
algorithms specifically targeted for this task.
Compared with main algorithms known in the location theory, the hidden unit was
within yards of the outbreak source in the first example (the 2007 epidemic of Chikungunya
fever in Italy). The hidden unit was located in the river between the two village epicentres
of the spread exactly where the index case was living. Equally in the second (the 1967 foot
and mouth disease epidemic in England), and the third (1854 London Cholera epidemic)
examples, the algorithm was able to match the known source of outbreak.
Our results are consistent with the idea that the spread of infectious disease is not
random but follows a progression which is based on inherent but as yet undiscovered
mathematical laws based on probabilistic density function. These methods, which require
further field evaluation and validation, could provide an additional powerful tool for the
investigation of the early stages of an epidemic, and constitute the basis of new simulation
methods to understand the process through which a disease is spread.