Abstract
In an increasingly connected world, epidemic modeling techniques are an important tool for understanding epidemic phenomenon and forecasting future pathogen spread. While an extensive literature concerning the application of these techniques to a variety of pathogen life cycles and population contexts has arisen, general-purpose epidemic modeling software has not been forthcoming, especially for computationally challenging stochastic epidemic models. We introduce a general-purpose spatial epidemic modeling framework applicable to many pathogens and describe an open source package for the R statistical computing environment designed to perform such analyses.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brown GD (2014) spatialSEIR: a framework for fitting Bayesian spatial SEIR epidemic models. R package. https://github.com/grantbrown/libSpatialSEIR
Brown GD, Oleson JJ, Porter AT (2015) An empirically adjusted approach to reproductive number estimation for stochastic compartmental models: a case study of two Ebola outbreaks. Biometrics. doi:10.1111/biom.12432
Cauchemez S, Carrat F, Viboud C, Boëlle P (2004) A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data. Stat Med 23(22):3469–3487
Chis Ster I, Singh BK, Ferguson NM (2009) Epidemiological inference for partially observed epidemics: the example of the 2001 foot and mouth epidemic in Great Britain. Epidemics 1(1):21–34
Chowell G, Hengartner N, Castillo-Chavez C, Fenimore P, Hyman J (2004) The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda. J Theor Biol 229(1):119–126
Cook AR, Otten W, Marion G, Gibson GJ, Gilligan CA (2007) Estimation of multiple transmission rates for epidemics in heterogeneous populations. PNAS 104(51):20392–20397
Deardon R, Brooks SP, Grenfell BT, Keeling MJ, Savill NJ et al (2010) Inference for individual-level models of infectious diseases in large populations. Stat Sinica 20(1):239–261
Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev 42(2):599–653
Hooten MB, Anderson J, Waller LA (2011) Assessing North American influenza dynamics with a statistical SIRS model. Spat Spatiotemporal Epidemiol 1(2):77–185
Jacquez JA, O’Neill P (1991) Reproduction numbers and thresholds in stochastic epidemic models. 1. Homogeneous populations. Math Biosci 107(2):161–186
Jewell CP, Keeling MJ, Roberts GO (2009) Predicting undetected infections during the 2007 foot and mouth disease outbreak. J R Soc Interface 6(41):1145–1151
Kermack W, McKendrick A (1927) A contribution to the mathematical theory of epidemics. Proc R Soc A 115:700–721
Lekone PE, Finkenstädt BF (2006) Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study. Biometrics 62(4):1170–1177
Porter AT, Oleson JJ (2013) A path-specific SEIR model for use with general latent and infectious time distributions. Biometrics 69(1):101–108
Van Boven M, Donker T, van der Lubben M, van Gageldonk-Lafber RB, te Beest DE, Koopmans M, Meijer A, Timen A, Swaan C, Dalhuijsen A, Hahné S, van den Hoek A, Teunis P, van der Sande MAB, WallingaHahne J (2010) Transmission of novel influenza A (H1N1) in households with post-exposure antiviral prophylaxis. PLoS ONE 5(7):e11442
Verdasca J, da Gama T, Nunes A, Bernardino NR, Pacheco JM, Gomos MC (2005) Recurrent epidemics in small world networks. J Theor Biol 233(4):553–561
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Brown, G.D., Oleson, J.J. (2017). Spatiotemporal Epidemic Modeling with libSpatialSEIR: Specification, Fitting, Selection, and Prediction. In: Griffith, D., Chun, Y., Dean, D. (eds) Advances in Geocomputation. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-22786-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-22786-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22785-6
Online ISBN: 978-3-319-22786-3
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)