Version 1
: Received: 12 March 2017 / Approved: 13 March 2017 / Online: 13 March 2017 (09:30:11 CET)
How to cite:
Adegboye, O.; Leung, D.; Wang, Y. Modelling of Spatio-temporal Zero Truncated Patterns in Infectious Disease Surveillance Data. Preprints2017, 2017030065. https://doi.org/10.20944/preprints201703.0065.v1
Adegboye, O.; Leung, D.; Wang, Y. Modelling of Spatio-temporal Zero Truncated Patterns in Infectious Disease Surveillance Data. Preprints 2017, 2017030065. https://doi.org/10.20944/preprints201703.0065.v1
Adegboye, O.; Leung, D.; Wang, Y. Modelling of Spatio-temporal Zero Truncated Patterns in Infectious Disease Surveillance Data. Preprints2017, 2017030065. https://doi.org/10.20944/preprints201703.0065.v1
APA Style
Adegboye, O., Leung, D., & Wang, Y. (2017). Modelling of Spatio-temporal Zero Truncated Patterns in Infectious Disease Surveillance Data. Preprints. https://doi.org/10.20944/preprints201703.0065.v1
Chicago/Turabian Style
Adegboye, O., Denis Leung and You-Gan Wang. 2017 "Modelling of Spatio-temporal Zero Truncated Patterns in Infectious Disease Surveillance Data" Preprints. https://doi.org/10.20944/preprints201703.0065.v1
Abstract
This paper is motivated by spatio-temporal pattern in the occurrence of Leishmaniasis in Afghanistan and the relatively high number of zero counts. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses. Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. We identify three challenges in the modelling of a spatio-temporal process: (1) accommodation of covariances that arise from spatial and temporal sources; (2) choosing the correct covariance structure and (3) extending to situations where a covariance is not the natural measure of association. Moreover, because the data covers a period that overlaps with the US invasion of Afghanistan, the high number of zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, a model truncated at zero built on a foundation of the generalized estimating equations was proposed.
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
