Modelling the effect of landscape heterogeneity on the efficacy of vaccination for wildlife infectious disease control

Abstract

Summary1. Zoonotic disease control presents significant costs and challenges in human and wildlife populations. Although spatial variability and temporal variability in host populations play a significant role influencing the spread and persistence of pathogens, their impact on the effectiveness of disease control are not well understood. 2. Field studies are impractical for many zoonotic diseases; thus, simulation modelling is an alternative. Some research has experimented with metapopulation models of host-pathogen systems, with discrete host populations distributed on a network of connections or on a onedimensional transect of contiguous cells. Little attention has been paid to treating geographic space as a fine-grained two-dimensional continuum, a more appropriate spatial model for many generalist host and vector species. 3. Using raccoon rabies as an example, we apply an individual-based spatially explicit stochastic simulation model to evaluate effectiveness of vaccination barrier strategies to control rabies. Barrier width and immunization levels are varied over landscapes with habitats of varying quality and spatial heterogeneity, resulting in varying degrees of host connectivity. 4. Our results demonstrate that spatial heterogeneity in the landscape does affect vaccination efficacy. The probability that rabies will breach a vaccination barrier is greater and rabies incidence is higher in landscapes with (i) overall good-quality homogeneous habitat and (ii) overall poor-quality habitat with high spatial heterogeneity, than in landscapes with overall good-quality habitat and high spatial heterogeneity. The influence of landscape conditions on disease dynamics decreases with increasing population immunity. 5. Synthesis and applications. Using a spatially explicit stochastic simulation model, we demonstrated that landscape spatial heterogeneity and vaccination control will interact to influence the success of controlling infectious disease outbreaks. Further, under some landscape conditions, insufficient vaccination is counter-productive because immunized individuals (i) reduce the number of disease transmitting contacts, preventing the disease from growing rapidly thus depleting the susceptible population; and (ii) survive to replenish the stock of susceptible animals through reproduction, facilitating disease persistence.

Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges

“…Sometimes spatial heterogeneity may interact with interventions in unexpected ways (e.g., Rees, Pond, Tinline, & Bélanger, ). Thus, spatial models may also successfully reveal unexpected or counterintuitive consequences of interventions, or may provide insight to why certain interventions have not been effective.…”

Cost-benefit Analysis of Epidemics Spreading on Clustered Random Networks

“…In this approach, individuals are represented as nodes on a network and their interactions by edges [13][14][15]. Analytical solutions arising from the graph theory [16,17] and percolation [18,19] or simulations can be used to answer questions concerning the potential for a particular disease to invade the population and persist there [20,21], the relationship between the network structure and rate of spread [22][23][24], the future course of an unfolding epidemic [25], and, finally, to assess control strategies that either prevent the disease from invading [26] or aim at its eradication [27][28][29]. Network models are particularly suitable for the latter task, as they allow to represent spatial aspects of the disease spread [30,31] and, therefore, help in designing responsive and local control strategies that target particular individuals or their connections [32].…”

Modelling seasonal habitat suitability and connectivity for feral pigs in northern Australia: towards risk-based management of infectious animal diseases with wildlife hosts

“…Our results could also be used to generate improved estimates of host densities (albeit with the considerable uncertainties discussed above), which in turn could 'seed' spatially-explicit cellular automata or individual-based models (Doran & Laffan 2005;Ward et al 2011;Rees et al 2013). …”

“…Deeper, dynamic insights into disease-host interactions and the spread of epidemics for better incursion preparedness could be gained through epidemiological simulation models (Ostfeld et al 2005;Riley 2007;Milne et al 2008). Our data on patch and matrix connectivity could be integrated to realistically constrain pathways of spread or the extent of epidemiologically connected zones (Cowled & Garner 2008;Rees et al 2013, Macpherson et al 2016. Epidemiological models could also elucidate the relationship between connectivity and disease transmission or persistence in wildlife hosts.…”

“…We focused on understanding habitat connectivity for the wildlife host, as such knowledge gaps have impeded epidemiological models and disease management in Australia and elsewhere (Ostfeld et al 2005;Cowled & Garner 2008;Rees et al 2013;Macpherson et al 2016). …”