An online spatiotemporal prediction model for dengue fever epidemic in Kaohsiung (Taiwan)

The emergence and re-emergence of disease epidemics is a complex question that may be influenced by diverse factors, including the space-time dynamics of human populations, environmental conditions, and associated uncertainties. This study proposes a stochastic framework to integrate space-time dynamics in the form of a Susceptible-Infected-Recovered (SIR) model, together with uncertain disease observations, into a Bayesian maximum entropy (BME) framework. The resulting model (BME-SIR) can be used to predict space-time disease spread. Specifically, it was applied to obtain a space-time prediction of the dengue fever (DF) epidemic that took place in Kaohsiung City (Taiwan) during 2002. In implementing the model, the SIR parameters were continually updated and information on new cases of infection was incorporated. The results obtained show that the proposed model is rigorous to user-specified initial values of unknown model parameters, that is, transmission and recovery rates. In general, this model provides a good characterization of the spatial diffusion of the DF epidemic, especially in the city districts proximal to the location of the outbreak. Prediction performance may be affected by various factors, such as virus serotypes and human intervention, which can change the space-time dynamics of disease diffusion. The proposed BME-SIR disease prediction model can provide government agencies with a valuable reference for the timely identification, control, and prevention of DF spread in space and time.

[1]  N. Ferguson,et al.  Planning for smallpox outbreaks , 2003, Nature.

[2]  Peter J Diggle,et al.  Spatio-temporal point processes, partial likelihood, foot and mouth disease , 2006, Statistical methods in medical research.

[3]  Frank Ball,et al.  A general model for stochastic SIR epidemics with two levels of mixing. , 2002, Mathematical biosciences.

[4]  Jacques Demongeot,et al.  Demography in epidemics modelling , 2011 .

[5]  George Christakos,et al.  BME analysis of spatiotemporal particulate matter distributions in North Carolina , 2000 .

[6]  M. Miller,et al.  MOVEMENT PATTERNS AND SPATIAL EPIDEMIOLOGY OF A PRION DISEASE IN MULE DEER POPULATION UNITS , 2004 .

[7]  Shu-Chiung Lin,et al.  Exploring the spatial and temporal relationships between mosquito population dynamics and dengue outbreaks based on climatic factors , 2012, Stochastic Environmental Research and Risk Assessment.

[8]  F. Barreto,et al.  Stochastic dynamics of dengue epidemics. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  George Christakos,et al.  On the assimilation of uncertain physical knowledge bases: Bayesian and non-Bayesian techniques. , 2002 .

[10]  S. Riley.  Large-Scale Spatial-Transmission Models of Infectious Disease , 2007, Science.

[11]  Frank Ball,et al.  An Sir Epidemic Model on a Population with Random Network and Household Structure, and Several Types of Individuals , 2012, Advances in Applied Probability.

[12]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[13]  M. Pascual,et al.  Stochastic amplification in epidemics , 2007, Journal of The Royal Society Interface.

[14]  Michael Höhle,et al.  A Space–Time Conditional Intensity Model for Invasive Meningococcal Disease Occurrence , 2012, Biometrics.

[15]  George Christakos,et al.  A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan , 2011 .

[16]  Space–time changes in hydrological processes in response to human activities and climatic change in the south China , 2012, Stochastic Environmental Research and Risk Assessment.

[17]  Joshua M Tebbs,et al.  Empirical Bayesian Estimation of the Disease Transmission Probability in Multiple‐Vector‐Transfer Designs , 2005, Biometrical journal. Biometrische Zeitschrift.

[18]  Zhen Jin,et al.  TRAVELING PATTERN INDUCED BY MIGRATION IN AN EPIDEMIC MODEL , 2009 .

[19]  Leonhard Held,et al.  Assessing the Impact of a Movement Network on the Spatiotemporal Spread of Infectious Diseases , 2012, Biometrics.

[20]  R. Mikolajczyk,et al.  Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases , 2008, PLoS medicine.

[21]  Michael Y. Li,et al.  Impacts of migration and immigration on disease transmission dynamics in heterogeneous populations , 2012 .

[22]  E. Lyons,et al.  Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings , 2009, Science.

[23]  Gail E. Potter,et al.  The Transmissibility and Control of Pandemic Influenza A (H1N1) Virus , 2009, Science.

[24]  Leonhard Held,et al.  Modeling seasonality in space‐time infectious disease surveillance data , 2012, Biometrical journal. Biometrische Zeitschrift.

[25]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[26]  Claudia Czado,et al.  Predictive Model Assessment for Count Data , 2009, Biometrics.

[27]  Daqing Jiang,et al.  The Behavior of an SIR Epidemic Model with Stochastic Perturbation , 2012 .

[28]  L. Allen,et al.  Comparison of deterministic and stochastic SIS and SIR models in discrete time. , 2000, Mathematical biosciences.

[29]  Jinn-Guey Lay,et al.  Higher temperature and urbanization affect the spatial patterns of dengue fever transmission in subtropical Taiwan. , 2009, The Science of the total environment.

[30]  Michael Höhle,et al.  Additive‐Multiplicative Regression Models for Spatio‐Temporal Epidemics , 2009, Biometrical journal. Biometrische Zeitschrift.

[31]  Zhi-Hong Guan,et al.  A stochastic SIR epidemic on scale-free network with community structure , 2013 .

[32]  S. Riley,et al.  Smallpox transmission and control: Spatial dynamics in Great Britain , 2006, Proceedings of the National Academy of Sciences.

[33]  R. Eggo,et al.  Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States , 2010, Journal of The Royal Society Interface.

[34]  C P Farrington,et al.  Measures of Disassortativeness and their Application to Directly Transmitted Infections , 2009, Biometrical journal. Biometrische Zeitschrift.

[35]  G. Christakos,et al.  Duration of Urban Mortality for the 14th-Century Black Death Epidemic , 2005, Human biology.

[36]  Pei-Chih Wu,et al.  Weather as an effective predictor for occurrence of dengue fever in Taiwan. , 2007, Acta tropica.

[37]  Hwa-Lung Yu,et al.  Modeling of space–time infectious disease spread under conditions of uncertainty , 2012, Int. J. Geogr. Inf. Sci..

[38]  Paola Sebastiani,et al.  Coherent dispersion criteria for optimal experimental design , 1999 .

[39]  Russell D Wolfinger,et al.  Spatial prediction of counts and rates , 2003, Statistics in medicine.