Spatial Multicriteria Evaluation for Mapping the Risk of Occurrence of Peste des Petits Ruminants in Eastern Africa and the Union of the Comoros

Abstract

Peste des petits ruminants virus (PPRV), responsible for peste des petits ruminants (PPR), is widely circulating in Africa and Asia. The disease is a huge burden for the economy and development of the affected countries. In Eastern Africa, the disease is considered endemic. Because of the geographic proximity and existing trade between eastern African countries and the Comoros archipelago, the latter is at risk of introduction and spread, and the first PPR outbreaks occurred in the Union of the Comoros in 2012. The objective of this study was to map the areas suitable for PPR occurrence and spread in the Union of the Comoros and four eastern African countries, namely Ethiopia, Uganda, Kenya, and Tanzania. A Geographic Information System (GIS)-based Multicriteria Evaluation (MCE) was developed. Risk factors for PPR occurrence and spread, and their relative importance, were identified using literature review and expert-based knowledge. Corresponding geographic data were collected, standardized, and combined based on a weighted linear combination to obtain PPR suitability maps. The accuracy of the maps was assessed using outbreak data from the EMPRES database and a ROC curve analysis. Our model showed an excellent ability to distinguish between absence and presence of outbreaks in Eastern Africa (AUC = 0.907; 95% CI [0.820-0.994]), and a very good performance in the Union of the Comoros (AUC = 0.889, 95% CI: [0.694-1]). These results highlight the efficiency of the GIS-MCE method, which can be applied at different geographic scales: continental, national and local. The resulting maps provide decision support tools for implementation of disease surveillance and control measures, thus contributing to the PPR eradication goal of OIE and FAO by 2030.

Spatio‐temporal cluster analysis and transmission drivers for Peste des Petits Ruminants in Uganda

“…We tested risk factors for transmission of PPRV reported in the literature against the outcomes of interest (total number of outbreaks per district or whether a district has reported an outbreak) (A. K. M. A. Rahman et al., 2021; Ruget et al., 2019). As previously reported (Ma et al., 2019), our regression models indicated a strong negative association between annual precipitation with the likelihood of PPR outbreaks implying that lower rainfall increases the chance of PPR outbreaks.…”

Mapping the risks of the spread of peste des petits ruminants in the Republic of Kazakhstan

“…The following geographically distributed landscape, climate, and socio-economic characteristics for each administrative unit were selected as potential explanatory factors, based on an analysis of scientific publications on the spatial and temporal modelling of PPR (Cao et al, 2018;Gao et al, 2019;Lembo et al, 2013;Ma et al, 2017Ma et al, , 2019Mokhtari et al, 2017;Ruget et al, 2019): (1) Variables proxying an intensity of regional economic activity that may influence the disease spread by transport links-total road length, road density, average population density, and the Engel coefficient 1 ; (2) Average den- The performance of the obtained model was tested by predicting for the Chinese study region and comparing the observed and predicted number of PPR outbreaks. The regression residuals were tested for spatial autocorrelation using Moran's I test (Mitchell, 2005).…”

“…The following geographically distributed landscape, climate, and socio‐economic characteristics for each administrative unit were selected as potential explanatory factors, based on an analysis of scientific publications on the spatial and temporal modelling of PPR (Cao et al., 2018; Gao et al., 2019; Lembo et al., 2013; Ma et al., 2017, 2019; Mokhtari et al., 2017; Ruget et al., 2019): (1) Variables proxying an intensity of regional economic activity that may influence the disease spread by transport links— total road length, road density, average population density, and the Engel coefficient; (2) Average density of small ruminants and average cattle density as indicators of susceptible population's and other potential host's density; (3) Most general landscape and climatic factors that may shape a suitable habitat for small ruminants and provide favourable conditions for the virus spread— average elevation, annual mean temperature, annual precipitation, and maximum green vegetation fraction. To provide environmental and socio‐economic similarity between the study regions in China and Kazakhstan, we ensured that each variable's range overlapped for both countries.…”

Transboundary Animal Diseases, an Overview of 17 Diseases with Potential for Global Spread and Serious Consequences

“…Mathematical modelling has been performed to estimate economic impact, identify risks for transmission, and evaluate possible control techniques [522][523][524][525][526]. An in-silico approach to protein analysis may help with development of vaccines and therapeutics [527].…”