A Retrospective Analysis of the Spatial and Temporal Patterns of the West African Ebola Epidemic, 2014–2015

Abstract

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.

Modeling region based regimes for COVID‐19 mitigation: An inverse Gompertz approach to coronavirus infections in the USA, New York, and New Jersey

“…We decided to use the Gompertz curve following Ord & Getis (2018) as the inverse fitting function to the monotonically nondecreasing cumulative infection data. The Gompertz function shown in the equation below (The White House, 2020b) is a special case of the sigmoid function and is suitable to describe a phenomenon that seems to unexpectedly emerge and grow over short time periods and then appears to asymptotically reach a plateau.…”

“…As mentioned above, disease transmission and spread models are based on the classical SIR model (Kermack & McKendrick, 1927) that uses sigmoid functions. Others (Ord & Getis, 2018) have successfully used SIR models with sigmoid and Gompertz functions (Mitchen, 2020). Dr. Levitt believed that the Gompertz function was a better fit for the cumulative infection data than a logistic function.…”

Airbnb and COVID-19: SPACE-TIME vulnerability effects in six world-cities

“…Clearly, the current corona crisis is not the first time the hospitality sector has been affected by an unexpected shock (see, e.g., Hystad & Keller, 2006 ; Brown et al, 2017 ; Shao & Xu, 2017 ; Ord & Getis, 2018 ; Xu & Shao, 2019 ; Chang et al, 2020 ). Many of these perturbations have severely influenced the revenues and socio-economic performance in the hospitality markets.…”

Meningococcal Meningitis and Coal Mining in Provincial England: Geographical Perspectives on a Major Epidemic, 1929–33

“…The ways in which spatial analytical methods and techniques can illuminate the geographical dimensions of infectious disease events have long been of interest to geographers, public health scientists, medical statisticians, and others working in the broadly defined area of spatial epidemiology (Cliff and Haggett 1988). Numerous examples feature in the pages of Geographical Analysis , from historical studies of the spatial diffusion of cholera epidemics (Pyle 1969) to contemporary investigations of the burden of Ebola virus disease (Ord and Getis 2018) and the environmental correlates of COVID‐19 (Paez et al 2020). Framed by this broader literature, the present article explores an important matter in the spatial epidemiology of one epidemic‐prone disease (meningococcal meningitis) in interwar Britain: the role of coal mining as a socio‐spatial determinant of the meningococcal meningitis epidemic of 1929–33.…”