A framework to predict zoonotic hosts under data uncertainty: a case study on betacoronaviruses

Modelling approaches aimed at identifying unknown hosts of zoonotic pathogens have the potential to make high-impact contributions to global strategies for zoonotic risk surveillance. However, geographical and taxonomic biases in host–pathogen associations affect the reliability of models and their predictions. Here, we propose a methodological framework to mitigate the effect of biases in host–pathogen data and account for uncertainty in models' predictions. Our approach involves identifying ‘pseudo-negative’ species and integrating sampling biases into the modelling pipeline. We present an application on the genus Betacoronavirus and provide estimates of mammal-borne betacoronavirus hazard at a global scale. We show that the inclusion of pseudo-negatives in the analysis improved the overall validation performance of our model when compared to a model that does not use pseudo-negatives, especially reducing the rate of false positives. Results of our application unveil currently unrecognised hotspots of betacoronavirus hazard in subequatorial Africa and the Americas. Our approach addresses crucial limitations in host–pathogen association modelling, with important downstream implications for zoonotic risk assessments. The proposed framework is adaptable to different multi-host disease systems and may be used to identify surveillance priorities as well as knowledge gaps in zoonotic pathogens' host-range.