Transmission network reconstruction for foot-and-mouth disease outbreaks incorporating farm-level covariates

Abstract

Transmission network modelling to infer 'who infected whom' in infectious disease outbreaks is a highly active area of research. Outbreaks of foot-and-mouth disease have been a key focus of transmission network models that integrate genomic and epidemiological data. The aim of this study was to extend Lau's systematic Bayesian inference framework to incorporate additional parameters representing predominant species and numbers of animals held on a farm. Lau's Bayesian Markov chain Monte Carlo algorithm was reformulated, verified and pseudo-validated on 100 simulated outbreaks populated with demographic data Japan and Australia. The modified model was then implemented on genomic and epidemiological data from the 2010 outbreak of foot-and-mouth disease in Japan, and outputs compared to those from the SCOTTI model implemented in BEAST2. The modified model achieved improvements in overall accuracy when tested on the simulated outbreaks. When implemented on the actual outbreak data from Japan, infected farms that held predominantly pigs were estimated to have five times the transmissibility of infected cattle farms and be 49% less susceptible. The farm-level incubation period was 1 day shorter than the latent period, the timing of the seeding of the outbreak in Japan was inferred, as were key linkages between clusters and features of farms involved in widespread dissemination of this outbreak. To improve accessibility the modified model has been implemented as the R package 'BORIS' for use in future outbreaks.

Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions

“…This is because the connectivity of single host farm networks allows for the spread of disease quickly, infecting more farms of the specific host species in the 30-day time frame than when the disease is started in a multi-host farm. Similar patterns have been observed in past epidemics, such as the FMD [ 14 , 15 , 26 , 42 , 43 ] where multi-host farms had a role in infection propagation.…”

Elucidating the Local Transmission Dynamics of Highly Pathogenic Avian Influenza H5N6 in the Republic of Korea by Integrating Phylogenetic Information

“…In practice, this simplified assumption is likely not valid because there are remarkable phylogenetic differences among viruses sampled from different IPs. This means that some farms are probably not directly linked with other infected farms in terms of virus transmission; thus, in the estimation of transmission kernels, nonlinked farms have to be excluded from the analyses [11]. Accordingly, for the robust estimation of transmission parameters, phylogenetic information for all IPs within the area of interest must be incorporated, even if there is uncertainty associated with the inference of transmission linkage [12].…”

Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review

“…Eleven methods considered removal times, seven the onset of infectiousness, while few (3/22) considered the start of exposure (Table 1). Moreover, intrinsic characteristics (predominant species, number of animals, production period) are only considered in two methods, belonging to the NPF and SimPF, respectively: Aldrin 2011 [28] and BORIS (Bayesian Outbreak Reconstruction Inference and Simulation) [29] (Table 1). Similarly, only two other methods included contact data in their transmission model: one in the NPF, outbreaker2 [30], and one in the SeqPF, TiTUS [31] (Table 1).…”