Spatial multi-criteria decision analysis to predict suitability for African swine fever endemicity in Africa

Abstract

BackgroundAfrican swine fever (ASF) is endemic in several countries of Africa and may pose a risk to all pig producing areas on the continent. Official ASF reporting is often rare and there remains limited awareness of the continent-wide distribution of the disease.In the absence of accurate ASF outbreak data and few quantitative studies on the epidemiology of the disease in Africa, we used spatial multi-criteria decision analysis (MCDA) to derive predictions of the continental distribution of suitability for ASF persistence in domestic pig populations as part of sylvatic or domestic transmission cycles. In order to incorporate the uncertainty in the relative importance of different criteria in defining suitability, we modelled decisions within the MCDA framework using a stochastic approach. The predictive performance of suitability estimates was assessed via a partial ROC analysis using ASF outbreak data reported to the OIE since 2005.ResultsOutputs from the spatial MCDA indicate that large areas of sub-Saharan Africa may be suitable for ASF persistence as part of either domestic or sylvatic transmission cycles. Areas with high suitability for pig to pig transmission (‘domestic cycles’) were estimated to occur throughout sub-Saharan Africa, whilst areas with high suitability for introduction from wildlife reservoirs (‘sylvatic cycles’) were found predominantly in East, Central and Southern Africa. Based on average AUC ratios from the partial ROC analysis, the predictive ability of suitability estimates for domestic cycles alone was considerably higher than suitability estimates for sylvatic cycles alone, or domestic and sylvatic cycles in combination.ConclusionsThis study provides the first standardised estimates of the distribution of suitability for ASF transmission associated with domestic and sylvatic cycles in Africa. We provide further evidence for the utility of knowledge-driven risk mapping in animal health, particularly in data-sparse environments.

Closed cervix is associated with more severe illness in dogs with pyometra

“…That in the present study only a few dogs with the disease were extremely ill and had elevated serum creatinine concentrations is in agreement with findings in other studies of dogs with the disease [9, 11, 15, 18]. None of the other clinical parameters analyzed differed significantly between the two groups in the present study.…”

“…In the present study, sepsis was diagnosed in 59% of the dogs, i.e. the majority of pyometra cases, which is in agreement with other studies of dogs with the disease using the same criteria [3, 18, 19]. Furthermore, sepsis was more common in dogs with closed cervix pyometra compared to open cervix pyometra.…”

Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil

“…Vector habitat and risk maps were established using available entomological and epidemiological data. De Glanville et al [28] predicted the suitability for African swine fever (ASF) endemicity in Africa using multi-criteria decision analysis, and validated its performance via statistical methods using available ASF outbreak data. Stefani et al did a systematic review of the literature [14], and proposed a consensual knowledge-based model that qualitatively estimates the level of malaria transmission risk in the Amazon in relation to deforestation patterns.…”

“…Such models are often either very specific or very general, as they are generated from data characterizing either local scale contexts at high resolutions, preventing obtaining reproducible results and to describe or predict large scale phenomena [23], or large scale contexts at low resolutions, preventing the ability to precisely describe disease transmission mechanisms [5,12,16]. Moreover, the necessary data are often incomplete and highly biased and, consequently, insufficient quantitatively and qualitatively for data-driven approaches [27,28], especially in these remote areas where epidemiological, entomological, and environmental Anopheles darlingi is considered as the principal vector of malaria in the Amazon region [5]. This species plays an evident role in malaria transmission in the Amapá State and in French Guiana, even if other species could be involved locally [6][7][8].…”

Prokaryotic expression, purification and antigenicity analysis of African swine fever virus pK205R protein

“…The virus was next reported in Portugal, France, Belgium and other European countries in the 1980s (Wieland et al 2011, Sampoli Benitez et al 2013, Uttenthal et al 2013. Currently, ASFV has crossed the Atlantic Ocean and outbreaks have been reported on some Caribbean islands, including Cuba and the Dominican Republic (de Glanville et al 2014). The recent outbreak of ASFV outside of Africa was in Georgia at the beginning of 2007, and it has since spread to Azerbaijan and Russia (Diaz et al 2012, Uttenthal et al 2013).…”