Skip to main content

Modeling of space–time infectious disease spread under conditions of uncertainty

Buy Article:

$60.90 plus tax (Refund Policy)


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.

Keywords: geostatistics; spatiotemporal data modeling; uncertainty

Document Type: Research Article


Affiliations: 1: Department of Statistics and Operations Research,University of Granada, Granada, Spain 2: Department of Bioenvironmental Systems Engineering,National Taiwan University, Taipei, Taiwan 3: Department of Civil Engineering,University of Patras, Patras, Greece 4: Department of Geography,San Diego State University, San Diego,CA, USA

Publication date: October 1, 2012

More about this publication?

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more