Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

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.</jats:p

Early microbiota, antibiotics and health

The colonization of the neonatal digestive tract provides a microbial stimulus required for an adequate maturation towards the physiological homeostasis of the host. This colonization, which is affected by several factors, begins with facultative anaerobes and continues with anaerobic genera. Accumulating evidence underlines the key role of the early neonatal period for this microbiota-induced maturation, being a key determinant factor for later health. Therefore, understanding the factors that determine the establishment of the microbiota in the infant is of critical importance. Exposure to antibiotics, either prenatally or postnatally, is common in early life mainly due to the use of intrapartum prophylaxis or to the administration of antibiotics in C-section deliveries. However, we are still far from understanding the impact of early antibiotics and their long-term effects. Increased risk of non-communicable diseases, such as allergies or obesity, has been observed in individuals exposed to antibiotics during early infancy. Moreover, the impact of antibiotics on the establishment of the infant gut resistome, and on the role of the microbiota as a reservoir of resistance genes, should be evaluated in the context of the problems associated with the increasing number of antibiotic resistant pathogenic strains. In this article, we review and discuss the above-mentioned issues with the aim of encouraging debate on the actions needed for understanding the impact of early life antibiotics upon human microbiota and health and for developing strategies aimed at minimizing this impact.The work carried out in the authors’ laboratories on the early life microbiota is founded by the EU Joint Programming Initiative—A Healthy Diet for a Healthy Life (JPI HDHL, http://www.healthydietforhealthylife.eu/) and the Spanish Ministry of Economy and Competitiveness (MINECO) (Project EarlyMicroHealth). The Grant GRUPIN14-043 from “Plan Regional de Investigación del Principado de Asturias” is also acknowledged. A. M. N. is the recipient of a JPI predoctoral fellowship and N. S. benefits from a JdC contract, from the Spanish Ministry of Economy and Competitiveness (MINECO).Peer reviewe