Abstract
Morbilliviruses cause major mortality in marine mammals, but the dynamics of transmission and persistence are ill understood compared to terrestrial counterparts such as measles; this is especially true for epidemics in cetaceans. However, the recent outbreak of dolphin morbillivirus in the northwestern Atlantic Ocean can provide new insights into the epidemiology and spatio-temporal spread of this pathogen. To deal with uncertainties surrounding the ecology of this system (only stranded animals were observed), we develop a statistical framework that can extract key information about the underlying transmission process given only sparse data. Our self-exciting Poisson process model suggests that individuals are infectious for at most 24 days and can transfer infection up to two latitude degrees (220 km) within this time. In addition, the effective reproduction number is generally below one, but reaches 2.6 during a period of heightened stranding numbers near Virginia Beach, Virginia, in summer 2013. Network analysis suggests local movements dominate spatial spread, with seasonal migration facilitating wider dissemination along the coast. Finally, a low virus transmission rate or high levels of pre-existing immunity can explain the lack of viral spread into the Gulf of Mexico. More generally, our approach illustrates novel methodologies for analysing very indirectly observed epidemics.
Highlights
In July 2013, an outbreak of dolphin morbillivirus (DMV) was officially declared along the northwestern (NW) Atlantic coast of the United States and has since been implicated in the stranding of over 1600 common bottlenose dolphins (Tursiops truncatus, hereafter referred to as bottlenose dolphin)
To assess whether frequency- (c 1⁄4 0) or density-dependent (c 1⁄4 1) transmission best characterized the underlying epidemic dynamics, both models were fitted to the data using Markov chain Monte Carlo (MCMC) methods as described above
There is some variation between the parameter estimates, but the main distinction is that the frequencydependent model returns lower Akaike information criterion (AIC) and Watanabe–Akaike information criterion (WAIC) values than the density-dependent model and represents a better fit to the entire dataset
Summary
In July 2013, an outbreak of dolphin morbillivirus (DMV) was officially declared along the northwestern (NW) Atlantic coast of the United States and has since been implicated in the stranding of over 1600 common bottlenose dolphins (Tursiops truncatus, hereafter referred to as bottlenose dolphin). The current outbreak has been declared an unusual mortality event (UME) due to the high number and unexpected nature of the strandings that have occurred [6]. This outbreak represents the largest number of bottlenose dolphin strandings reported in the NW Atlantic since the last recorded DMV epidemic in. The current NW Atlantic UME provides a unique opportunity to explore the spatio-temporal spread of DMV within a species, and gain new insights into the dynamics of this poorly understood pathogen
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