Walsh, M. G., & Haseeb, M. A. (2015). The landscape configuration of zoonotic transmission of Ebola virus disease in West and Central Africa: interaction between population density and vegetation cover. PeerJ, 3(1), e735–13. http://doi.org/10.7717/peerj.735
Following the epidemic outbreaks of Ebola Virus Disease (EVD) in West Africa in 2014, it is obvious that the ability to predict, and perhaps even prevent, such outbreaks could greatly inform public health efforts, and save lives. Walsh & Haseeb (2015) use a point process distribution model to understand what are the socio-ecological drivers of zoonotic transmission events of EVD. Unique transmission events were recorded from the PubMed Database and World Health Organization reports, and matched to a geographical location. The authors chose three types of covariate data: WorldClim data on temperature and precipitation, Maximum Green Vegetation Fraction from Modis as a measure of vegetation, or forest, cover, and population density data from the Global Urban-Rural Mapping Project. First, they created a homogenous Poisson process (ppm), which served as a null model because the expected number of location points scaled with the area of the subregion, and inhomogenous Poisson process, which incorporate spatial dependence into the location of transmission events. The inhomogenous ppm fit the data better, and then was then expanded to include the four covariates listed above, plus altitude and an interaction covariate between vegetation cover and population density. The ppm allowed for the use of conventional statistical tests of significance, such as p-values and confidence intervals. Three covariates came out as important. Both increasing population density and increasing vegetation, although slightly less so, cover corresponded to a decrease in spillover risk. Interestingly, the interaction between these two variables was also significant, implying that the ‘protective effect’ of vegetation cover decreases with increasing population density. This suggests the presence of ecotones, where denser human populations are coming into contact with recently fragmented forest, an avenue for zoonotic spillover that has been suggested in the past.
Thoughts: An ecological niche model of EVD has been described previously, but this study incorporates the additional complexity of social factors, which I believe is especially important when considering spillover events. Doing so, however, removes distribution modeling from this idea of a fundamental niche, in my opinion, because it is no longer simply where EVD can persist but where it spills over. Semantically, this could be a ‘niche’ for spillover events. I also think it is important that they considered interactions amongst environmental covariates, especially because certain variables are correlated or depend on others.
The study’s code is online with data, if anyone is interested in reproducing it or just playing around point process models.