Optimizing spatial and seasonal deployment of vaccination campaigns to eliminate wildlife rabies

Abstract

Understanding how the spatial deployment of interventions affects elimination time horizons and potential for disease re-emergence has broad application to control programmes targeting human, animal and plant pathogens. We previously developed an epidemiological model that captures the main features of rabies spread and the impacts of vaccination based on detailed records of fox rabies in eastern Germany during the implementation of an oral rabies vaccination (ORV) programme. Here, we use simulations from this fitted model to determine the best vaccination strategy, in terms of spatial placement and timing of ORV efforts, for three epidemiological scenarios representative of current situations in Europe. We found that consecutive and comprehensive twice-yearly vaccinations across all regions rapidly controlled and eliminated rabies and that the autumn campaigns had the greater impact on increasing the probability of elimination. This appears to result from the need to maintain sufficient herd immunity in the face of large birth pulses, as autumn vaccinations reach susceptible juveniles and therefore a larger proportion of the population than spring vaccinations. Incomplete vaccination compromised time to elimination requiring the same or more vaccination effort to meet similar timelines. Our results have important practical implications that could inform policies for rabies containment and elimination in Europe and elsewhere. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Local rabies transmission and regional spatial coupling in European foxes

“…It is the first to estimate key epidemiological parameters, including 381 spatial coupling and local transmission, using a model fit to data. Our findings have 382 implications for strategies aiming to achieve and maintain rabies freedom and the 383 modeling approach can be used to further explore vaccination strategies to inform 384 ongoing vaccination in Eastern Europe [42]. This work also makes an important…”

“…Models that capture local transmission dynamics 40 of fox rabies and regional connectivity therefore have immediate application to the 41 situation in Europe and elsewhere. Here, we examine fox rabies dynamics in response to 42 oral vaccination using a hierarchical Bayesian state-space model fit to incidence data 43 from Eastern Germany from 1982-2013. We use a metapopulation approach to model 44 transmission by representing space as a network of subpopulations and estimating the 45 movement of infected individuals (or coupling) between them.…”

“…Although 332 long-distance translocations of infected wildlife are known to occur [41], fox rabies 333 transmission is thought to mainly result from movement of rabid and latently infected 334 foxes. Even a small number of incursions from neighbouring regions through such 335 movement can enable disease resurgence in a rabies-free region and are an important 336 consideration in the design of vaccination strategies [42]. Coordinated vaccination 337 efforts between regions can act to isolate foci of infection [43] and are crucial to the 338 success of fox rabies elimination strategies [29].…”

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

“…Accordingly, epidemiological models of pathogens of humans, animals and plants often include a spatial component. In this issue, there are examples of spatial livestock disease models based on cattle movements [50], a spatial model of wildlife rabies [78] and spatial models of huanglongbing disease of citrus plants [16,62] (e.g. figure 3c).…”

“…Using stochastic simulations to assess the impact of existing interventions on epidemiological dynamics often involves exploring epidemiological dynamics with and without a proposed control strategy-a problem considered by Lessler et al [93]. Potential interventions include prophylactic controls such as vaccination, considered in the context of wildlife rabies by Baker et al [78] and Ebola in humans by Getz et al [79] and movement bans, an intervention assessed in the context of livestock diseases by Chaters et al [50]. Probert et al [94] and Bussell et al [87] also focus on using models to guide interventions-using reinforcement learning and optimal control theory, respectively-and these approaches could also potentially apply to pathogens irrespective of the type of host.…”