The influence of empirical contact networks on modelling diseases in cattle

Abstract

We present two stochastic models of the passage of an SEIR (susceptible-latent-infectedresistant) disease through herds of cattle. One model is based on a contact network constructed via continuously recorded interaction data from two herds of cattle, the other, a matching network constructed using the principles of mass-action mixing. The recorded contact data were produced by attaching proximity data loggers to two separate herds of cattle during two separate recording periods. The network constructed using the principles of mass-action mixing uses the same number of contacts as the recorded network but distributes them randomly amongst the animals. The recorded networks had a greater number of repeated contacts, lower closeness and clustering scores and greater average path length than the mass-action networks. A lower proportion of simulations of the recorded network produce any disease spread when compared to those simulations of the mass-action network and, of those that did, fewer infected animals were predicted. For all parameter values tested, within the sensitivity analysis, similar differences were found between the recorded and mass-action network models. AbstractWe present two stochastic models of the passage of an SEIR (susceptible-latentinfected-resistant) disease through herds of cattle. One model is based on a contact network constructed via continuously recorded interaction data from two herds of cattle, the other, a matching network constructed using the principles of mass-action mixing. The recorded contact data were produced by attaching proximity data loggers to two separate herds of cattle during two separate recording periods. The network constructed using the principles of mass-action mixing uses the same number of contacts as the recorded network but distributes them randomly amongst the animals. The recorded networks had a greater number of repeated contacts, lower closeness and clustering scores and greater average path length than the mass-action networks. A lower proportion of simulations of the recorded network produce any disease spread when compared to those simulations of the mass-action network and, of those that did, fewer infected animals were predicted. For all parameter values tested, within the sensitivity analysis, similar differences were found between the recorded and mass-action network models.

The Risk of Disease to Great Apes: Simulating Disease Spread in Orang-Utan (Pongo pygmaeus wurmbii) and Chimpanzee (Pan troglodytes schweinfurthii) Association Networks

“…More recent models have incorporated the natural heterogeneity of contact patterns using social network analysis. A typical finding is that the topology of the network can have a considerable impact on the predicted disease spread [18], [27]–[29]. For example, simulations of disease transmission in African buffalo indicated a much faster spread of disease on a mean-field network than on actual association networks [18].…”

Highly dynamic animal contact network and implications on disease transmission

“…Links between farms are constructed using animal movement databases. However, the contact structure within premises and at lower scales such as pen level are less understood despite their influence on disease transmission, especially for highly transmissible diseases 18,19 . The characterization of animal-to-animal contact networks at higher temporal and spatial resolution within farm animal groups has been rarely investigated, and therefore it is unknown how these networks change over time, including their structure (e.g.…”

Temporal-spatial heterogeneity in animal-environment contact: Implications for the exposure and transmission of pathogens

“…number of contacts, duration of each contact, and total duration of all contacts in a given time), making the variation in contact structure one of the most important sources of transmission heterogeneity in infectious diseases1112. Individual animals spend different amounts of time doing different activities in various areas1314. These heterogeneous behavioral patterns among animals can affect the contact structure (in this study defined as the duration of total contact with specific areas in a pen in 1 day).…”

“…Proximity loggers have been used to describe close social encounters in both humans and animals151617. Animal movement data collected using global positioning and other triangulation and accelerometer systems can be used to both characterize direct contacts and to describe the interactions between animals and their associated environment1114. These systems provide novel empirical data to describe contact for environmentally transmitted diseases and can be integrated with infectious disease transmission models to provide a more accurate description of pathogen transmission throughout the environment.…”