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The goal is to predict the final extent of the Ebola epidemic in West Africa, 2014–2015, well before its end. Our models are based on the nature of the reported data, and the social, medical, and technological conditions that existed in real time during the course of the epidemic. The spatial and temporal nature of Ebola transmission is considered. A modified, classical compartmental model is used to develop variations of Gompertz and logistic‐type predictive models. A map analysis strongly hints at the existence of an initial rural component of the transmission of the disease followed by an urban component. Cumulative and weekly data for the three countries illustrate how the disease intensified and spread over the 90 weeks of the epidemic. A spatial autocorrelation study shows the clustering locational changes of the disease over time. A cross‐correlation study gives credence to both a rural and an urban transmission pattern. Our early estimates of the toll of the disease, crude as they are, stand in marked contrast to some of the official estimates at the time, which greatly inflated the number of new cases. [ABSTRACT FROM AUTHOR]

Moran's Index for spatial autocorrelation and localized index for spatial association have been widely applied in many research fields as the first step to explore and assess the spatial dependency in a set of geographic events. This article presents extensions to the equations for calculating global and localized spatial autocorrelation so to include the temporal attribute values of the geographic events being analyzed. The extended equations were successfully implemented and tested with a real world data set. In addition, simulated data sets were used to reveal how the extended equations performed. Beyond the usefulness of the extended equations, we suggest that care be taken with regard to assessing spatiotemporal patterns under the normality and randomization assumptions as different outcomes from different assumptions would require different approaches for interpretation. [ABSTRACT FROM AUTHOR]

The article responds to comments on the article "Extending Moran’s Index for Measuring Spatiotemporal Clustering of Geographic Events. It mentions an issue to consider for any further research in the realm of spatiotemporal autocorrelation is the very different ways events are related to each other spatially and temporally. It states that the study would stimulate others to further devise spatiotemporal analytics to benefit Geographic Information System (GIS) community.

The article comments on the article "Extending Moran’s Index for Measuring Spatiotemporal Clustering of Geographic Events" by J. Lee and S. Li. It formalizes the extended Moran coefficient (MC) space-time weights matrix for the two cases of contemporaneous and spatially lagged autocorrelation. It concludes that the Lee and Li paper should have had a much more comprehensive contextualization.