Spatiotemporal Infectious Disease Modeling: A BME-SIR Approach
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AuthorAngulo Ibáñez, José Miguel; Yu, Hwa-Lung; Langousis, Andrea; Kolovos, Alexander; Wang, Jinfeng; Madrid García, Ana Esther; Christakos, George
Public Library of Science
Population dynamicsInfectious diseaseDisease susceptibilityModelingControlCovarianceSpatial distributionEvolutionary modeling
Angulo, J.; et al. Spatiotemporal Infectious Disease Modeling: A BME-SIR Approach. Plos One, 8(9): e72168 (2013). [http://hdl.handle.net/10481/29026]
SponsorshipJ.M. Angulo and A.E. Madrid have been partially supported by grants MTM2009-13250 and MTM2012-32666 of SGPI, and P08-FQM-3834 of the Andalusian CICE, Spain. H-L Yu has been partially supported by a grant from National Science Council of Taiwan (NSC101-2628-E-002-017-MY3 and NSC102-2221-E-002-140-MY3). A. Kolovos was supported by SpaceTimeWorks, LLC. G. Christakos was supported by a Yongqian Chair Professorship (Zhejiang University, China).
This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multi-sourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the specification of transmission and recovery rates, the model studies the functional formulation of the evolution of the fractions of susceptible-infected-recovered individuals. The suggested approach is capable of: a) modeling population dynamics within and across localities, b) integrating the disease representation (i.e. susceptible-infected-recovered individuals) with observation time series at different geographical locations and other sources of information (e.g. hard and soft data, empirical relationships, secondary information), and c) generating predictions of disease spread and associated parameters in real time, while considering model and observation uncertainties. Key aspects of the proposed approach are illustrated by means of simulations (i.e. synthetic studies), and a real-world application using hand-foot-mouth disease (HFMD) data from China.