We develop and test a risk map for Orthoebolaviruses which are emerging infectious pathogens primarily concentrated in sub-Saharan Africa. The accuracy of predictive models and risk maps has been limited thus far by uncertainty in mechanisms underlying spread, low number of known outbreaks, and in how well various drivers predict different types of outbreaks (e.g. human vs epizootic outbreaks, and outbreaks of different viral species). Here, we explore frugivory and other factors as mechanisms of Orthoebolavirus spread and demonstrate statistical methods with repeated cross-validation that can be used even with very small data sets to explore how different factors influence different classes of events using ensemble machine learning logistic regression. We show that covariates predicting outbreaks with the highest discrimination power are frugivore richness (area under curve [AUC] = 0.95) and fruit tree (Ficus) habitat suitability (AUC = 0.94). We found that Ficus distributions contributed to predictions of past Orthoebolavirus outbreaks relatively equally, regardless of type, based on feature contributions estimated using Shapley value calculations. In contrast, frugivore richness was a better contributor of predictions of epizootic than human outbreaks. Hunting activity was a poor predictor overall (AUC = 0.85) but contributed to some Sudan outbreaks predictions. Our results suggest that different drivers best influence different classes of Orthoebolavirus outbreaks and models taking into account a variety of factors are needed to predict future spillover events.

The frequency of infectious disease outbreaks and pandemics is rising, demanding an understanding of their drivers. Common wisdom suggests that increases in outbreak frequency are driven by socioeconomic factors such as globalization and urbanization, yet, the majority of disease outbreaks are caused by zoonotic pathogens that can be transmitted from animals to humans, suggesting the important role of ecological and environmental drivers. Previous studies of outbreak drivers have also failed to quantify the differences between major classes of pathogens, such as bacterial and viral pathogens. Here, we reconsider the observed drivers of a global sample of 300 zoonotic outbreaks, including the 100 largest outbreaks that occurred between 1977 and 2017. We show that socioeconomic factors more often trigger outbreaks of bacterial pathogens, whereas ecological and environmental factors trigger viral outbreaks. However, socioeconomic factors also act as amplifiers of viral outbreaks, with higher case numbers in viral outbreaks driven by a larger proportion of socioeconomic factors. Our results demonstrate that it is useful to consider the drivers of global disease patterns in aggregate due to commonalities that cross disease systems. However, our work also identifies important differences between the driver profiles of bacterial and viral diseases in aggregate.

ZIKV is an emerging mosquito-borne pathogen with the unique capability to cause severe congenital abnormalities. Mosquito-borne viruses are reliant on an optimal temperature within the mosquito host to spread to humans. Few studies explore how day/night temperature variations impact virus replication within the mosquito. Most laboratory studies are conducted at constant temperatures, or with very minimal temperature oscillations. In consequence, ZIKV transmission models and risk maps utilize biological data derived from constant temperature studies. We sought to determine if the use of viral replication data obtained in more biologically relevant, fluctuating temperatures alters the geographic range for ZIKV transmission risk. We used a growing degree day (GDD)-based model to determine ZIKV transmission potential across the US. Replication curves were conducted at eight different average temperatures between 18°C and 32°C with daily fluctuations of either +/−2.5°C or +/−5°C from the baseline. We found that at lower temperatures (18–22°C), fluctuation was beneficial for virus replication, where it had less of an impact at more optimal temperatures (24–30°C). Our transmission potential map exhibited the highest proportion of infected mosquitoes in southeastern United States, with discrete pockets of elevated potential across interior regions, including the southern Great Plains and lower Mississippi Valley.