| dc.description.abstract | African Swine Fever (ASF) is a viral disease of both domestic pigs and wild boars, with no cure or vaccine, causing economic devastations to farmers. Previous studies in Uganda have not fully addressed the spatial dimension in the epidemiology of the disease. Attempts using traditional methods do provide some spatial insights at district or regional level. However, they are limited in provision of precise spatial patterns and relationships where the disease manifests, hence limiting the full understanding of the spatial diffusion of the disease. To this effect, spatial analysis was conducted using Average Nearest Neighbour (ANN), Moran’s I and Hot spot analysis to characterize the spatial distribution of ASF in Uganda from 2013-2019. A retrospective space-time permutation scan statistic was then used to assess the spatiotemporal clustering of ASF. The socio-environmental and demographic factors such as land use land cover, human population density, density of roads, rivers and water bodies were incorporated to assess the likely causes of clustering. ANN index of 0.45 was less than 1 thus revealing a clustering pattern in the distribution of ASF cases. Moran’s index of 0.52 was greater than 0 thus revealing a positive spatial relationship where-by, ASF cases of similar values were closer to one another than ASF cases with dissimilar values. Hot spot analysis revealed maximum spatial clustering distance of 11.4km (p<0.01). Spatial clustering of high infection rates (hot spots) was mostly observed in Tororo, Busia and Manafwa districts, and spatial clustering of low infection rates (cold spots) was mostly observed in Kole, Omoro and Otuke districts. Space-time clusters highlighting the center of infection, period and size of ASF outbreaks were observed across the study area. The most likely space-time cluster was observed in Alebtong district represented by a log likelihood ratio with the highest value of 1745 (p<0.001) among others. Temporal clustering was observed throughout the whole year; with most outbreak trends increasing from January to April and October to November. A combination of socio-environmental and demographic factors was associated with clustering pattern of ASF observed in various areas. The study concludes that the spatial processes describing clustering of ASF (e.g., animal movements and trade related activities) with high infection rates occur within a maximum distance of 11.4km. January to April and October to November are high prevalent periods. Spatial analysis offers a more informative approach to define the difference or relationship between cases in close proximity and those far-away from each other –which is useful in decision making for better management of epidemics. It is therefore recommended that targeted intervention be adopted; in high prevalent periods like January to April and October to November. Secondly, a buffer zone of not more than 11.4km be established in hot spot areas –during surveillance and imposing quarantines as opposed to quarantining the whole district. | en_US |
