Descriptive network analysis and the influence of timescale on centrality and cohesion metrics from a system of between-herd dairy cow movements in Ontario, Canada.

Previous research has demonstrated that static monthly networks of between-herd dairy cow movements in Ontario, Canada were highly fragmented, reducing potential for large-scale outbreaks. Extrapolating results from static networks can become problematic for diseases with an incubation period that exceeds the timescale of the network. The objectives of this research were to: 1) describe the networks of dairy cow movements in Ontario, and 2) describe the changes that occur among network analysis metrics when conducted at seven different timescales. Networks of dairy cow movements were created using Lactanet Canada milk recording data collected in Ontario between 2009 and 2018. Centrality and cohesion metrics were calculated after aggregating the data at seven timescales: weekly, monthly, semi-annual, annual, biennial, quinquennial, and decennial. There were 50,598 individual cows moved between Lactanet-enrolled farms, representing approximately 75% of provincially registered dairy herds. Most movements occurred over short distances (median = 39.18 km), with fewer long-range movements (maximum = 1150.80 km). The number of arcs increased marginally relative to the number of nodes with longer network timescales. Both mean out-degree, and mean clustering coefficients increased disproportionately with increasing timescale. Conversely, mean network density decreased with increasing timescale. The largest weak and strong components at the monthly timescale were small relative to the full network (267 and 4 nodes), whereas yearly networks had much higher values (2213 and 111 nodes). Higher relative connectivity in networks with longer timescales suggests pathogens with long incubation periods and animals with subclinical infection present increased potential for wide-spread disease transmission among dairy farms in Ontario. Careful consideration of disease-specific dynamics should be made when using static networks to model disease transmission among dairy cow populations.

Mathematical modelling of population and food storage dynamics in a honey bee colony infected with Nosema ceranae.

Unusually high wintering losses of Apis mellifera in recent years has raised concerns regarding the well-being and productivity of honey bees across the globe. While these losses are likely multi-factorial, a proposed contributor are diseases, including those caused by parasites. We formulate and present a mathematical model for a colony of Apis mellifera honey bees infected with the microsporidian parasite Nosema ceranae. The model is numerically analyzed to determine the effects of N. ceranae infection on population and food storage dynamics and their subsequent implications towards colony survival and annual honey yield. Depending on the strength of disease, it is possible for either parasite fadeout, co-existence between bees and N. ceranae, or colony failure to occur. In all cases, the yield of honey collected by the beekeeper is reduced. We further extend the model to include various treatment schemes with the, now discontinued, antimicrobial fumagillin. Treatment with fumagillin can reduce the risk of colony failure and will increase honey yield compared to when no treatment is applied.