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

Abstract

Infectious diseases in livestock are well-known to infect multiple hosts and persist through a combination of within- and between-host transmission pathways. Uncertainty remains about the epidemic dynamics of diseases being introduced on farms with more than one susceptible host species. Here, we describe multi-host contact networks and elucidate the potential of disease spread through farms with multiple hosts. Four years of between-farm animal movement among all farms of a Brazilian state were described through a static and monthly snapshot of network representations. We developed a stochastic multilevel model to simulate scenarios in which infection was seeded into single host and multi-host farms to quantify disease spread potential, and simulate network-based control actions used to evaluate the reduction of secondarily infected farms. We showed that the swine network was more connected than cattle and small ruminants in both the static and monthly snapshots. The small ruminant network was highly fragmented, however, contributed to interconnecting farms, with other hosts acting as intermediaries throughout the networks. When a single host was initially infected, secondary infections were observed across farms with all other species. Our stochastic multi-host model demonstrated that targeting the top 3.25% of the farms ranked by degree reduced the number of secondarily infected farms. The results of the simulation highlight the importance of considering multi-host dynamics and contact networks while designing surveillance and preparedness control strategies against pathogens known to infect multiple species.

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

“…The analysis of livestock movement data based on graph theory, e.g. in the UK 12,18,19 , Denmark 20,21 , Germany 22 , Spain 23 , France 24,25 , Italy 26 , Brazil 27 , and Ireland 28 proved to be a valuable tool to explore the dynamics of trade patterns between livestock operations, quantitatively characterize the topology of animal trade networks, and understand the role each animal holding plays in the network 3,29 . In epidemiology, network approaches revealed also very powerful, e.g.…”

Modeling foot-and-mouth disease dissemination in Brazil and evaluating the effectiveness of control measures

“…The demonstrated predominance of spatial transmission later in the epidemic could be explained at least to some degree by spread across short to mid distances in some areas of the state of Rio Grande do Sul characterized by cool temperatures and high humidity, which are known to favor virus survival rates (Björnham et al, 2020; Chanchaidechachai et al, 2021; Green et al, 2006). More importantly, based on the high connectivity of bovine farms in some municipalities described earlier (Cardenas et al, 2022), the initial peak in bovine infection could be explained by the movement of animals, while the flip toward spatial transmission could have been facilitated when secondary cases spread into areas densely populated with up to 12.23 km 2 farms (Cardenas et al, 2022) These densely populated areas coincide with our model simulation in which control actions were not sufficient to stamp out large-scale epidemics (Supplementary Material Figure S15). Even though our modeling work considered the main transmission routes, data regarding other important indirect transmission routes were not considered, e.g., fomites and transportation vehicles (Paton et al, 2018; Rossi et al, 2017) future iterations could include these additional routes.…”

“…However, we highlight that a small number of simulations with fewer than 50 infected farms at 20 days of dissemination that were not successfully controlled by the baseline control scenario. Thus, it is important to consider the profile of infected farms, such as location and the size of the farm's contact network (Cardenas et al, 2022) along with simulated results to make field-level decisions. Finally, we remark that as described earlier, we assume full compliance with all associated surveillance actions and movement restrictions; thus, the results should be interpreted with this in mind.…”

“…given the high connectivity of bovine farms in some municipalities 47 , the initial peak in bovine infection could be explained by animal movement, while the shift toward spatial transmission could have been facilitated when secondary cases spread into densely populated areas with up to 12.23 farms per km 2 47 . These densely populated areas coincide with areas in our simulations in which control actions were not sufficient to eliminate large-scale epidemics (Supplementary Figure S15).…”

Assessing epidemiological parameters and dissemination characteristics of the 2000 and 2001 foot-and-mouth disease outbreaks in Rio Grande do Sul, Brazil

“…Other useful models to estimate transmission dynamics and epidemiological parameters are based on the retrospective reconstruction of previous outbreak networks (Firestone et al, 2020; Hayama et al, 2019; Jombart et al, 2014). Such network modeling exercises emulate the characteristics of spread and allow the identification of potential super spreading events as well as epidemics sizes by describing interactions between individuals using animal movement data (Cabezas et al, 2021; Cardenas et al, 2021, 2022; Dubé et al, 2009; Fèvre et al, 2006; Machado et al, 2021; Ruget et al, 2021; VanderWaal et al, 2016).…”