A theoretical model for the spread of infectious diseases in a composite space–time domain is developed. The model has a general form that enables it to account for the basic mechanisms of disease distribution and to incorporate the considerable multisourced uncertainty (caused
by physiographic features, disease variability, meteorological conditions, etc.). Starting from the general model formulation regarding the specification of transmission and recovery rates, as well as the population migration dynamics, several subsequent assumptions are introduced that simplify
analytical tractability and practical implementation. In particular, linearization involving a deterministic functional representation for the average evolution of the fraction of susceptible individuals allows the formulation of an extended Kalman filter approach for estimation based on the
time series observed at a finite set of locations. Different aspects of interest derived from the epidemic space–time model proposed, as well as the performance of the extended Kalman filter procedure, are illustrated through simulations.
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spatiotemporal data modeling;
Document Type: Research Article
Department of Statistics and Operations Research,University of Granada, Granada, Spain
Department of Bioenvironmental Systems Engineering,National Taiwan University, Taipei, Taiwan
Department of Civil Engineering,University of Patras, Patras, Greece
Department of Geography,San Diego State University, San Diego,CA, USA
Publication date: 01 October 2012
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