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. 2013 Dec 17;110(51):20837-42.
doi: 10.1073/pnas.1308817110. Epub 2013 Dec 2.

Resolving the roles of immunity, pathogenesis, and immigration for rabies persistence in vampire bats

Affiliations

Resolving the roles of immunity, pathogenesis, and immigration for rabies persistence in vampire bats

Julie C Blackwood et al. Proc Natl Acad Sci U S A. .

Abstract

Bats are important reservoirs for emerging infectious diseases, yet the mechanisms that allow highly virulent pathogens to persist within bat populations remain obscure. In Latin America, vampire-bat-transmitted rabies virus represents a key example of how such uncertainty can impede efforts to prevent cross-species transmission. Despite decades of agricultural and human health losses, control efforts have had limited success. To establish persistence mechanisms of vampire-bat-transmitted rabies virus in Latin America, we use data from a spatially replicated, longitudinal field study of vampire bats in Peru to parameterize a series of mechanistic transmission models. We find that single-colony persistence cannot occur. Instead, dispersal of bats between colonies, combined with a high frequency of immunizing nonlethal infections, is necessary to maintain rabies virus at levels consistent with field observations. Simulations show that the strong spatial component to transmission dynamics could explain the failure of bat culls to eliminate rabies and suggests that geographic coordination of control efforts might reduce transmission to humans and domestic animals. These findings offer spatial dynamics as a mechanism for rabies persistence in bats that might be important for the understanding and control of other bat-borne pathogens.

Keywords: Desmodus rotundus; host–pathogen dynamics; spatial processes; wildlife culling; zoonotic disease.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
(Right) Map of South America with Peru indicated by gray shading. Antibody prevalence field data on VBRV were sampled across 17 sites in four departments of Peru [Apurimac (red), Cajamarca (orange), Lima (blue), and Madre de Dios (green)] and are provided for 2007, 2009, and 2010 (19). Sites were sampled approximately yearly. For each site in a given year, the proportion of seropositive bats is denoted by off-white with the proportion seronegative denoted by the color associated with the department where the colony is located. The radius of each observation is proportional to sample size. See SI Appendix for the complete dataset.
Fig. 2.
Fig. 2.
(A) Schematic diagram of each model where the force of infection formula image. Demographic processes are omitted for clarity. Filled states have seroconverted, and white states are seronegative. Model IV (lifelong immunity) is a base model that includes all black lines and states. Model I (temporary immunity and lethal infection) additionally includes the blue text, model II (nonlethal infection) includes red and blue text where 1 − ρ is the probability of recovery from infection, and model III (immune boosting) includes yellow and blue where c is the strength of immune boosting that depends on the force of infection. (B) Here 95% confidence intervals (CIs) for lethal infection probability versus the effect of immigration on the force of infection. As in A, model I is colored blue, model II is colored red, model III is colored yellow, and model IV is represented by black. Models I–III arrive at the same MLE for α and ϕ (blue star), and the MLE for model IV is indicated by a black star. (C) Change in likelihood from the MLE for each model across all R0 values. Points above the horizontal gray line fall within the 95% CI (found using the likelihood ratio test), and the vertical line indicates formula image. The MLE of R0 and associated 95% CIs are also shown. (D) Log likelihood (log ℒ) and ΔAIC score (with zero associated with the best-fitting model) corresponding to the MLE for each model. The ΔAIC score accounts for differences in the number of estimated parameters.
Fig. 3.
Fig. 3.
Likelihood of VBRV dynamics in response to changes in α and ϕ shows that the most likely parameter combination is a low lethal infection probability (α) combined with a high effect of immigration on the force of infection (ϕ). (Center) Contour plot of the log likelihood for α versus ϕ for model 1. The reported likelihood values are maximized over βN and βR. The star indicates the MLE and the thick gray line represents the 95% CI. Above the contour plot is the profile likelihood for ϕ and to the right of the contour plot is the profile likelihood for α. The MLE corresponds to formula image and formula image with 95% CIs (10−3.51, 10−2.83) and (0, 0.29), respectively, noting 0 is not included in the CI for α. Parameters for sample simulations: (A) formula image, formula image, formula image, formula image ; (B) formula image, formula image, formula image, formula image ; (C) formula image, formula image, formula image, formula image ; and (D) formula image, formula image, formula image, formula image (the MLE). In A–D, shading indicates the proportion of seropositive individuals with temporary immunity (T, light red) and the proportion of seropositive individuals that are infectious (formula image, light blue). In contrast to D, simulations in A and C fail to consistently produce seropositive bats, while the dynamics in B drive the colony to extinction. At the MLE, ∼ 2 bats for every 10 susceptible bats present in a colony are expected to be exposed to VBRV by immigrant bats each year. Based on simulations this accounts for approximately half (53%) of all exposures, with the remaining exposures arising from bats within the colony. Department-specific parameters were consistent with the dynamics at the national scale (SI Appendix).
Fig. 4.
Fig. 4.
The effect of culling for different levels of immigration was evaluated by averaging over 1,000 stochastic simulations for each of the 17 sampled bat colonies. All other parameters were fixed at the MLE. Here, 50% culling is assumed (sensitivity analysis provided in the SI Appendix). We tested a series of culling strategies that, at one extreme, target all uninfected bats indiscriminately (darkest curve, formula image) and at the other, do not affect susceptible bats (lightest curve, formula image). On each plot, the thick black line indicates the no-cull scenario, and the vertical gray dashed line is the MLE of ϕ; Insets magnify the results near the MLE. (A) Seroprevalence initially declines the most when susceptible bats are not culled because Eq. 1 is highest when formula image, but in subsequent years the greatest decline corresponds to indiscriminate culling (SI Appendix). (B) Exposure rate of livestock to bats in the IN state, assuming one blood meal per night. Here, a significant decline in exposure rate is observed when culling is indiscriminate.

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