Development and assessment of a geographic knowledge-based model for mapping suitable areas for Rift Valley fever transmission in Eastern Africa

Rift Valley fever (RVF), a mosquito-borne disease affecting ruminants and humans, is one of the most important viral zoonoses in Africa. The objective of the present study was to develop a geographic knowledge-based method to map the areas suitable for RVF amplification and RVF spread in four East African countries, namely, Kenya, Tanzania, Uganda and Ethiopia, and to assess the predictive accuracy of the model using livestock outbreak data from Kenya and Tanzania. Risk factors and their relative importance regarding RVF amplification and spread were identified from a literature review. A numerical weight was calculated for each risk factor using an analytical hierarchy process. The corresponding geographic data were collected, standardized and combined based on a weighted linear combination to produce maps of the suitability for RVF transmission. The accuracy of the resulting maps was assessed using RVF outbreak locations in livestock reported in Kenya and Tanzania between 1998 and 2012 and the ROC curve analysis. Our results confirmed the capacity of the geographic information system-based multi-criteria evaluation method to synthesize available scientific knowledge and to accurately map (AUC = 0.786; 95% CI [0.730–0.842]) the spatial heterogeneity of RVF suitability in East Africa. This approach provides users with a straightforward and easy update of the maps according to data availability or the further development of scientific knowledge. (Résumé d'auteur

Potential distributions of Bacillus anthracis and Bacillus cereus biovar anthracis causing anthrax in Africa

Background Bacillus cereus biovar anthracis (Bcbva) is an emergent bacterium closely related to Bacillus anthracis, the etiological agent of anthrax. The latter has a worldwide distribution and usually causes infectious disease in mammals associated with savanna ecosystems. Bcbva was identified in humid tropical forests of Côte d’Ivoire in 2001. Here, we characterize the potential geographic distributions of Bcbva in West Africa and B. anthracis in sub-Saharan Africa using an ecological niche modeling approach. Methodology/Principal findings Georeferenced occurrence data for B. anthracis and Bcbva were obtained from public data repositories and the scientific literature. Combinations of temperature, humidity, vegetation greenness, and soils values served as environmental variables in model calibrations. To predict the potential distribution of suitable environments for each pathogen across the study region, parameter values derived from the median of 10 replicates of the best-performing model for each pathogen were used. We found suitable environments predicted for B. anthracis across areas of confirmed and suspected anthrax activity in sub-Saharan Africa, including an east-west corridor from Ethiopia to Sierra Leone in the Sahel region and multiple areas in eastern, central, and southern Africa. The study area for Bcbva was restricted to West and Central Africa to reflect areas that have likely been accessible to Bcbva by dispersal. Model predicted values indicated potential suitable environments within humid forested environments. Background similarity tests in geographic space indicated statistical support to reject the null hypothesis of similarity when comparing environments associated with B. anthracis to those of Bcbva and when comparing humidity values and soils values individually. We failed to reject the null hypothesis of similarity when comparing environments associated with Bcbva to those of B. anthracis, suggesting that additional investigation is needed to provide a more robust characterization of the Bcbva niche. Conclusions/Significance This study represents the first time that the environmental and geographic distribution of Bcbva has been mapped. We document likely differences in ecological niche—and consequently in geographic distribution—between Bcbva and typical B. anthracis, and areas of possible co-occurrence between the two. We provide information crucial to guiding and improving monitoring efforts focused on these pathogens

Did Ebola emerge in West Africa by a policy-driven phase change in agroecology? Ebola's social context

SCOPUS: no.jinfo:eu-repo/semantics/publishe

Chapter 7: To what extent do differences in legal systems affect cross-border insolvency? Evidence from foreign-owned Italian firms

Purpose — This chapter aims to investigate to what extent differences in legal systems affect cross-border insolvency. Specifically, it aims to answer the following research questions: What is the relationship between multinational status and firm death rates? To what extent can the legal system affect the pattern of firms’ death across countries? How can the cross-border insolvency legal rules produce firms’ death or survival through corporate restructuring and bailout? Methodology/approach — We apply survival methods and estimate a discrete-time hazard model in which we look for the effect of foreign ownership on firm death, controlling for firm- and industry-specific covariates. In doing this we analyse the determinants of firms’ death and crisis distinguishing Italian foreign-owned firms according to the legal system of the country where they have their ‘centre of main interests’ (COMI). Findings — Our main findings reveal that Italian firms owned by foreign multinationals are more likely to exit and to be in crisis than national firms. In addition, Italian foreign-owned firms which have their COMI in a Common law country, compared with those having their COMI in a Civil law country, exhibit a lower risk of death and a higher likelihood of surviving the crisis. Research limitations/implications — This analysis was limited to all Italian firms. Therefore, it might be interesting to verify if there is a sort of country/sectoral heterogeneity in the firms’ behaviour. In addition, the analysis could be extended to the Italian firms investing abroad (i.e. Domestic MNEs)

Wild boar mapping using population-density statistics: From polygons to high resolution raster maps

<div><p>The wild boar is an important crop raider as well as a reservoir and agent of spread of swine diseases. Due to increasing densities and expanding ranges worldwide, the related economic losses in livestock and agricultural sectors are significant and on the rise. Its management and control would strongly benefit from accurate and detailed spatial information on species distribution and abundance, which are often available only for small areas. Data are commonly available at aggregated administrative units with little or no information about the distribution of the species within the unit. In this paper, a four-step geostatistical downscaling approach is presented and used to disaggregate wild boar population density statistics from administrative units of different shape and size (polygons) to 5 km resolution raster maps by incorporating auxiliary fine scale environmental variables. 1) First a stratification method was used to define homogeneous bioclimatic regions for the analysis; 2) Under a geostatistical framework, the wild boar densities at administrative units, i.e. subnational areas, were decomposed into trend and residual components for each bioclimatic region. Quantitative relationships between wild boar data and environmental variables were estimated through multiple regression and used to derive trend components at 5 km spatial resolution. Next, the residual components (i.e., the differences between the trend components and the original wild boar data at administrative units) were downscaled at 5 km resolution using area-to-point kriging. The trend and residual components obtained at 5 km resolution were finally added to generate fine scale wild boar estimates for each bioclimatic region. 3) These maps were then mosaicked to produce a final output map of predicted wild boar densities across most of Eurasia. 4) Model accuracy was assessed at each different step using input as well as independent data. We discuss advantages and limits of the method and its potential application in animal health.</p></div

Validation results.

<p>Validation results.</p

Wild boar mapping using population-density statistics: From polygons to high resolution raster maps - Fig 5

<p><b>Model outputs: average trend (A), average geostatistical model (B) and mosaicked model (C)</b>.</p

Wild boar density by administrative units (original input data) and wild boar range.

<p>Wild boar density by administrative units (original input data) and wild boar range.</p

Wild boar mapping using population-density statistics: From polygons to high resolution raster maps - Fig 4

<p>Trend components derived from the regression relationship obtained for (A) the eastern, (B) western and (C) southern regions and extrapolated to the whole study area respectively. (D) Redefined bioclimatic regions and blending zones. The Asian region is not shown as it was excluded from the geostatistical analysis.</p

Results of the multiple regression models by bioclimatic region: Standardized coefficients and standard errors (in brackets), adjusted R², sample size (N), F value and degrees of freedom (dfs), residual standard error (RSE) and <i>p</i>-values.

<p>Results of the multiple regression models by bioclimatic region: Standardized coefficients and standard errors (in brackets), adjusted R², sample size (N), F value and degrees of freedom (dfs), residual standard error (RSE) and <i>p</i>-values.</p