Spatio-temporal modelling of zero-truncated disease patterns

This paper focuses on the spatio-temporal pattern of Leishmaniasis incidence in Afghanistan. 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 (e:g:, Yasui and Lele, 1997) [3]. 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. The model was built on a foundation of the generalized estimating equations (Liang and Zeger, 1986) [1]. We illustrate the method using data from Afghanistan between 2003-2009. Since the data covers a period that overlaps with the US invasion of Afghanistan, the zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, we use a model truncated at zero.