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The impact of human conflict on the genetics of Mastomys natalensis and Lassa virus in West Africa.

ABSTRACT: Environmental changes have been shown to play an important role in the emergence of new human diseases of zoonotic origin. The contribution of social factors to their spread, especially conflicts followed by mass movement of populations, has not been extensively investigated. Here we reveal the effects of civil war on the phylogeography of a zoonotic emerging infectious disease by concomitantly studying the population structure, evolution and demography of Lassa virus and its natural reservoir, the rodent Mastomys natalensis, in Guinea, West Africa. Analysis of nucleoprotein gene sequences enabled us to reconstruct the evolutionary history of Lassa virus, which appeared 750 to 900 years ago in Nigeria and only recently spread across western Africa (170 years ago). Bayesian demographic inferences revealed that both the host and the virus populations have gone recently through severe genetic bottlenecks. The timing of these events matches civil war-related mass movements of refugees and accompanying environmental degradation. Forest and habitat destruction and human predation of the natural reservoir are likely explanations for the sharp decline observed in the rodent populations, the consequent virus population decline, and the coincident increased incidence of Lassa fever in these regions. Interestingly, we were also able to detect a similar pattern in Nigeria coinciding with the Biafra war. Our findings show that anthropogenic factors may profoundly impact the population genetics of a virus and its reservoir within the context of an emerging infectious disease.

SUBMITTER: Lalis A

PROVIDER: S-EPMC3352846 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Project description:Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.

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The impact of human conflict on the genetics of Mastomys natalensis and Lassa virus in West Africa.

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