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Demographic processes drive increases in wildlife disease following population reduction.

Prentice JC, Marion G, White PC, Davidson RS, Hutchings MR - PLoS ONE (2014)

Bottom Line: However, population reduction can disrupt existing social and demographic structures leading to changes in observed host behaviour that may result in enhanced disease transmission.Analysis of a stochastic spatial meta-population model of demography and disease dynamics leads to qualitatively similar conclusions.This spatial analysis also shows that, below some threshold, population reduction can rapidly increase the area affected by disease, potentially expanding risks to sympatric species.

View Article: PubMed Central - PubMed

Affiliation: Disease Systems Team, SRUC, Edinburgh, United Kingdom; Biomathematics and Statistics Scotland, Edinburgh, United Kingdom; Environment Department, University of York, York, United Kingdom.

ABSTRACT
Population reduction is often used as a control strategy when managing infectious diseases in wildlife populations in order to reduce host density below a critical threshold. However, population reduction can disrupt existing social and demographic structures leading to changes in observed host behaviour that may result in enhanced disease transmission. Such effects have been observed in several disease systems, notably badgers and bovine tuberculosis. Here we characterise the fundamental properties of disease systems for which such effects undermine the disease control benefits of population reduction. By quantifying the size of response to population reduction in terms of enhanced transmission within a generic non-spatial model, the properties of disease systems in which such effects reduce or even reverse the disease control benefits of population reduction are identified. If population reduction is not sufficiently severe, then enhanced transmission can lead to the counter intuitive perturbation effect, whereby disease levels increase or persist where they would otherwise die out. Perturbation effects are largest for systems with low levels of disease, e.g. low levels of endemicity or emerging disease. Analysis of a stochastic spatial meta-population model of demography and disease dynamics leads to qualitatively similar conclusions. Moreover, enhanced transmission itself is found to arise as an emergent property of density dependent dispersal in such systems. This spatial analysis also shows that, below some threshold, population reduction can rapidly increase the area affected by disease, potentially expanding risks to sympatric species. Our results suggest that the impact of population reduction on social and demographic structures is likely to undermine disease control in many systems, and in severe cases leads to the perturbation effect. Social and demographic mechanisms that enhance transmission following population reduction should therefore be routinely considered when designing control programmes.

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Time trajectories and heterogeneity for emergent disease in the stochastic model.(A) Population numbers, S(t), I(t), and N(t). (B) Proportion of sub-populations containing infectives, , effective transmission rate β, and dispersal rate. (C) Distribution of I across sites. (D) Effective transmission rate β for disease transmission vs population reduction coverage p1. Parameters are given in Table 2, and initial conditions are at the disease free equilibrium , while in 20% of sites randomly chosen, a single individual is infected, resulting in . Population reduction occurs annually from years 50–69, and in  of sites (chosen randomly each year) the removal rate is set to , without regard to disease status (equivalent to an overall culling rate of ). An initial reduction in I is rapidly replaced by an increase, which is due to the increased chance of invasion of naïve groups by infectives due to the density dependent dispersal. The CI for the effective transmission rate increases for large  due to the increasing number of simulations where the disease becomes extinct.
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pone-0086563-g003: Time trajectories and heterogeneity for emergent disease in the stochastic model.(A) Population numbers, S(t), I(t), and N(t). (B) Proportion of sub-populations containing infectives, , effective transmission rate β, and dispersal rate. (C) Distribution of I across sites. (D) Effective transmission rate β for disease transmission vs population reduction coverage p1. Parameters are given in Table 2, and initial conditions are at the disease free equilibrium , while in 20% of sites randomly chosen, a single individual is infected, resulting in . Population reduction occurs annually from years 50–69, and in of sites (chosen randomly each year) the removal rate is set to , without regard to disease status (equivalent to an overall culling rate of ). An initial reduction in I is rapidly replaced by an increase, which is due to the increased chance of invasion of naïve groups by infectives due to the density dependent dispersal. The CI for the effective transmission rate increases for large due to the increasing number of simulations where the disease becomes extinct.

Mentions: Given the above discussion, when studying the spatial model we focus on cases where disease is distributed heterogeneously between groups and overall prevalence is low. This is most easily achieved when the system is close to the disease free equilibrium with: (i) disease maintained in each site by high within-site transmission rate βw and low mortality; (ii) low levels of disease transmission between sites; and (iii) relatively large and stable populations at each site leading to low levels of dispersal between sites. Under this scenario, even in the absence of population reduction, the number of sites infected, and thus overall prevalence, tends to slowly increase (from close to the disease free equilibrium) as rare dispersal or transmission events spread disease. Fig. 3 (discussed in detail below) shows how transient perturbation effects occur in such a system. In contrast, we show in Appendix S3.2 (in File S1) that by making both disease and population less stable within sites it is possible to achieve a dynamic quasi-equilibrium (quasi- because the ultimate fate of all simulations of this model is total extinction) where the spread of disease to uninfected sites is balanced by spontaneous recovery of infected sites, e.g. through death of infectives and birth of susceptible individuals. When the system is in such an endemic state population reduction leads to a persistent perturbation effect, as we saw in the non-spatial model (see Fig. S2 in File S1). However, this endemic state is very sensitive to the balance between site-level establishment and recovery of disease which makes it difficult to explore variation in the perturbation effect with respect to the value of key parameters. We therefore focus attention on the transient perturbation effect when starting close to the disease free state in the spatial model.


Demographic processes drive increases in wildlife disease following population reduction.

Prentice JC, Marion G, White PC, Davidson RS, Hutchings MR - PLoS ONE (2014)

Time trajectories and heterogeneity for emergent disease in the stochastic model.(A) Population numbers, S(t), I(t), and N(t). (B) Proportion of sub-populations containing infectives, , effective transmission rate β, and dispersal rate. (C) Distribution of I across sites. (D) Effective transmission rate β for disease transmission vs population reduction coverage p1. Parameters are given in Table 2, and initial conditions are at the disease free equilibrium , while in 20% of sites randomly chosen, a single individual is infected, resulting in . Population reduction occurs annually from years 50–69, and in  of sites (chosen randomly each year) the removal rate is set to , without regard to disease status (equivalent to an overall culling rate of ). An initial reduction in I is rapidly replaced by an increase, which is due to the increased chance of invasion of naïve groups by infectives due to the density dependent dispersal. The CI for the effective transmission rate increases for large  due to the increasing number of simulations where the disease becomes extinct.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4008369&req=5

pone-0086563-g003: Time trajectories and heterogeneity for emergent disease in the stochastic model.(A) Population numbers, S(t), I(t), and N(t). (B) Proportion of sub-populations containing infectives, , effective transmission rate β, and dispersal rate. (C) Distribution of I across sites. (D) Effective transmission rate β for disease transmission vs population reduction coverage p1. Parameters are given in Table 2, and initial conditions are at the disease free equilibrium , while in 20% of sites randomly chosen, a single individual is infected, resulting in . Population reduction occurs annually from years 50–69, and in of sites (chosen randomly each year) the removal rate is set to , without regard to disease status (equivalent to an overall culling rate of ). An initial reduction in I is rapidly replaced by an increase, which is due to the increased chance of invasion of naïve groups by infectives due to the density dependent dispersal. The CI for the effective transmission rate increases for large due to the increasing number of simulations where the disease becomes extinct.
Mentions: Given the above discussion, when studying the spatial model we focus on cases where disease is distributed heterogeneously between groups and overall prevalence is low. This is most easily achieved when the system is close to the disease free equilibrium with: (i) disease maintained in each site by high within-site transmission rate βw and low mortality; (ii) low levels of disease transmission between sites; and (iii) relatively large and stable populations at each site leading to low levels of dispersal between sites. Under this scenario, even in the absence of population reduction, the number of sites infected, and thus overall prevalence, tends to slowly increase (from close to the disease free equilibrium) as rare dispersal or transmission events spread disease. Fig. 3 (discussed in detail below) shows how transient perturbation effects occur in such a system. In contrast, we show in Appendix S3.2 (in File S1) that by making both disease and population less stable within sites it is possible to achieve a dynamic quasi-equilibrium (quasi- because the ultimate fate of all simulations of this model is total extinction) where the spread of disease to uninfected sites is balanced by spontaneous recovery of infected sites, e.g. through death of infectives and birth of susceptible individuals. When the system is in such an endemic state population reduction leads to a persistent perturbation effect, as we saw in the non-spatial model (see Fig. S2 in File S1). However, this endemic state is very sensitive to the balance between site-level establishment and recovery of disease which makes it difficult to explore variation in the perturbation effect with respect to the value of key parameters. We therefore focus attention on the transient perturbation effect when starting close to the disease free state in the spatial model.

Bottom Line: However, population reduction can disrupt existing social and demographic structures leading to changes in observed host behaviour that may result in enhanced disease transmission.Analysis of a stochastic spatial meta-population model of demography and disease dynamics leads to qualitatively similar conclusions.This spatial analysis also shows that, below some threshold, population reduction can rapidly increase the area affected by disease, potentially expanding risks to sympatric species.

View Article: PubMed Central - PubMed

Affiliation: Disease Systems Team, SRUC, Edinburgh, United Kingdom; Biomathematics and Statistics Scotland, Edinburgh, United Kingdom; Environment Department, University of York, York, United Kingdom.

ABSTRACT
Population reduction is often used as a control strategy when managing infectious diseases in wildlife populations in order to reduce host density below a critical threshold. However, population reduction can disrupt existing social and demographic structures leading to changes in observed host behaviour that may result in enhanced disease transmission. Such effects have been observed in several disease systems, notably badgers and bovine tuberculosis. Here we characterise the fundamental properties of disease systems for which such effects undermine the disease control benefits of population reduction. By quantifying the size of response to population reduction in terms of enhanced transmission within a generic non-spatial model, the properties of disease systems in which such effects reduce or even reverse the disease control benefits of population reduction are identified. If population reduction is not sufficiently severe, then enhanced transmission can lead to the counter intuitive perturbation effect, whereby disease levels increase or persist where they would otherwise die out. Perturbation effects are largest for systems with low levels of disease, e.g. low levels of endemicity or emerging disease. Analysis of a stochastic spatial meta-population model of demography and disease dynamics leads to qualitatively similar conclusions. Moreover, enhanced transmission itself is found to arise as an emergent property of density dependent dispersal in such systems. This spatial analysis also shows that, below some threshold, population reduction can rapidly increase the area affected by disease, potentially expanding risks to sympatric species. Our results suggest that the impact of population reduction on social and demographic structures is likely to undermine disease control in many systems, and in severe cases leads to the perturbation effect. Social and demographic mechanisms that enhance transmission following population reduction should therefore be routinely considered when designing control programmes.

Show MeSH
Related in: MedlinePlus