Author
Listed:
- Deeth Lorna E.
(Brock University, St. Catharines, ON, Canada)
- Deardon Rob
(University of Guelph, Guelph, ON, Canada)
Abstract
Individual-level models (ILMs) have previously been used to model the spatiotemporal spread of infectious diseases. These models can incorporate individual-level covariate information, to account for population heterogeneity. However, incomplete or unreliable data are a common problem in infectious disease modeling, and models that are explicitly dependent on such information may not be robust to these inherent uncertainties. In this investigation, we assess an adaptation to a spatial ILM that incorporates a latent grouping structure based on some trait heterogeneous in the population. The resulting latent conditional ILM is then only dependent upon a discrete latent grouping variable, rather than precise covariate information. The posterior predictive ability of this proposed model is tested through a simulation study, in which the model is fitted to epidemic data simulated from a true model that utilizes explicit covariate information. In addition, the posterior predictive ability of the proposed ILM is also compared to that of an ILM that assumes population homogeneity. The application of these models to data from the 2001 UK foot-and-mouth disease epidemic is also explored. This study demonstrates that the use of a discrete latent grouping variable can be an effective alternative to utilizing covariate information, particularly when such information may be unreliable.
Suggested Citation
Deeth Lorna E. & Deardon Rob, 2013.
"Latent Conditional Individual-Level Models for Infectious Disease Modeling,"
The International Journal of Biostatistics, De Gruyter, vol. 9(1), pages 1-19, August.
Handle:
RePEc:bpj:ijbist:v:9:y:2013:i:1:p:19:n:6
DOI: 10.1515/ijb-2013-0026
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:ijbist:v:9:y:2013:i:1:p:19:n:6. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.