Disentangling the contributions of different hosts to disease transmission is highly complex but critical for improving predictions, surveillance and response. This is particularly challenging in wildlife, with pathogens often infecting multiple species and data collection being difficult. Using the emergence of Usutu virus (USUV) in the Netherlands as a case study, we demonstrate the use of an approximate Bayesian computation framework on diverse data sources to uncover drivers of spatiotemporal wildlife disease emergence. We calibrated single- and multi-host mechanistic transmission models to five types of wildlife surveillance and research data, describing molecular and serological evidence of USUV in birds. Although Eurasian blackbirds, the primary target species for surveillance, were most severely affected, our models indicated that USUV could not persist in blackbirds alone. Our framework provided statistical support for additional, unobserved bird species to have contributed to transmission. This population of bird species is characterized by limited infection mortality, a longer lifespan and likely further dispersal than blackbirds. Immunity in this population appears to have protected blackbirds from further USUV-related population decline. Our results underscore the importance of considering multiple host populations to understand outbreak dynamics. Neglecting the multi-host context of transmission can impact the reliability of predictions and projected impact of interventions. By collating molecular, serological and population data from multiple wildlife surveillance schemes, this paper demonstrates that host species other than the most-affected and sentinel blackbird species must be involved in the transmission of Usutu virus in the Netherlands
Wit et al. (Tue,) studied this question.