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.

Crimean–Congo hemorrhagic fever virus (CCHFV) infection is identified in the 2018 World Health Organization Research and Development Blueprint and the National Institute of Allergy and Infectious Diseases (NIH/NIAID) priority A list due to its high risk to public health and national security. Tick-borne CCHFV is widespread, found in Europe, Asia, Africa, the Middle East, and the Indian subcontinent. It circulates between ticks and several vertebrate hosts without causing overt disease, and thus can be present in areas without being noticed by the public. As a result, the potential for zoonotic spillover from ticks and animals to humans is high. In contrast to other emerging viruses, human-to-human transmission of CCHFV is typically limited; therefore, prevention of spillover events should be prioritized when considering countermeasures. Several factors in the transmission dynamics of CCHFV, including a complex transmission cycle that involves both ticks and vertebrate hosts, lend themselves to a One Health approach for the prevention and control of the disease that are often overlooked by current strategies. Here, we examine critical focus areas to help mitigate CCHFV spillover, including surveillance, risk assessment, and risk reduction strategies concentrated on humans, animals, and ticks; highlight gaps in knowledge; and discuss considerations for a more sustainable One Health approach to disease control.

Egyptian rousette bats (Rousettus aegyptiacus) are natural reservoir hosts of Marburg virus (MARV), and Ravn virus (RAVV; collectively called marburgviruses) and have been linked to human cases of Marburg virus disease (MVD). We investigated the clinical and pathologic effects of experimental MARV infection in Egyptian rousettes through a serial euthanasia study and found clear evidence of mild but transient disease. Three groups of nine, captive-born, juvenile male bats were inoculated subcutaneously with 10,000 TCID₅₀ of Marburg virus strain Uganda 371Bat2007, a minimally passaged virus originally isolated from a wild Egyptian rousette. Control bats (n = 3) were mock-inoculated. Three animals per day were euthanized at 3, 5–10, 12 and 28 days post-inoculation (DPI); controls were euthanized at 28 DPI. Blood chemistry analyses showed a mild, statistically significant elevation in alanine aminotransferase (ALT) at 3, 6 and 7 DPI. Lymphocyte and monocyte counts were mildly elevated in inoculated bats after 9 DPI. Liver histology revealed small foci of inflammatory infiltrate in infected bats, similar to lesions previously described in wild, naturally-infected bats. Liver lesion severity scores peaked at 7 DPI, and were correlated with both ALT and hepatic viral RNA levels. Immunohistochemical staining detected infrequent viral antigen in liver (3–8 DPI, n = 8), spleen (3–7 DPI, n = 8), skin (inoculation site; 3–12 DPI, n = 20), lymph nodes (3–10 DPI, n = 6), and oral submucosa (8–9 DPI, n = 2). Viral antigen was present in histiocytes, hepatocytes and mesenchymal cells, and in the liver, antigen staining co-localized with inflammatory foci. These results show the first clear evidence of very mild disease caused by a filovirus in a reservoir bat host and provide support for our experimental model of this virus-reservoir host system.