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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 topology methods. 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. © 2009 Elsevier B.V. All rights reserved.

Original publication




Journal article


Physica A: Statistical Mechanics and its Applications

Publication Date





4736 - 4762