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Development of a Novel Rabies Simulation Model for Application in a Non-endemic Environment.

Dürr S, Ward MP - PLoS Negl Trop Dis (2015)

Bottom Line: Mathematical and simulation disease models are useful tools to provide insights on the most effective control strategies and to inform policy decisions.Illustrative simulations produced plausible results with epidemic characteristics expected for rabies outbreaks in disease free regions (mean R0 1.7, epidemic peak 97 days post-incursion, vaccination as the most effective response strategy).Systematic sensitivity analysis identified that model outcomes were most sensitive to seven of the 30 model parameters tested.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Veterinary Science, The University of Sydney, Camden, New South Wales, Australia.

ABSTRACT
Domestic dog rabies is an endemic disease in large parts of the developing world and also epidemic in previously free regions. For example, it continues to spread in eastern Indonesia and currently threatens adjacent rabies-free regions with high densities of free-roaming dogs, including remote northern Australia. Mathematical and simulation disease models are useful tools to provide insights on the most effective control strategies and to inform policy decisions. Existing rabies models typically focus on long-term control programs in endemic countries. However, simulation models describing the dog rabies incursion scenario in regions where rabies is still exotic are lacking. We here describe such a stochastic, spatially explicit rabies simulation model that is based on individual dog information collected in two remote regions in northern Australia. Illustrative simulations produced plausible results with epidemic characteristics expected for rabies outbreaks in disease free regions (mean R0 1.7, epidemic peak 97 days post-incursion, vaccination as the most effective response strategy). Systematic sensitivity analysis identified that model outcomes were most sensitive to seven of the 30 model parameters tested. This model is suitable for exploring rabies spread and control before an incursion in populations of largely free-roaming dogs that live close together with their owners. It can be used for ad-hoc contingency or response planning prior to and shortly after incursion of dog rabies in previously free regions. One challenge that remains is model parameterisation, particularly how dogs' roaming and contacts and biting behaviours change following a rabies incursion in a previously rabies free population.

No MeSH data available.


Related in: MedlinePlus

Outbreak results for three example control strategies based in 1000 repetitions per scenario.Three control strategies (RV = reactive vaccination, RC = reactive culling, MB = movement bans) in two regions (Elcho = Elcho Island, NPA = Northern Peninsula Area) were simulated. Number of rabid dogs, dead dogs (i.e. due to rabies or culling) and outbreak duration per scenario are represented for the NPA (upper line) and for Elcho Island (lower line). The box represents the interquartile range (IQR), the horizontal line in the box the median and the whiskers extend to the most extreme data point which is no more than 1.5 times IQR from the box. Outliers are not shown.
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pntd.0003876.g004: Outbreak results for three example control strategies based in 1000 repetitions per scenario.Three control strategies (RV = reactive vaccination, RC = reactive culling, MB = movement bans) in two regions (Elcho = Elcho Island, NPA = Northern Peninsula Area) were simulated. Number of rabid dogs, dead dogs (i.e. due to rabies or culling) and outbreak duration per scenario are represented for the NPA (upper line) and for Elcho Island (lower line). The box represents the interquartile range (IQR), the horizontal line in the box the median and the whiskers extend to the most extreme data point which is no more than 1.5 times IQR from the box. Outliers are not shown.

Mentions: Two measures of outbreak size are the cumulative number of rabid dogs and cumulative number of dead dogs (due to rabies plus culled). The outbreak duration is defined as the number of days from the introduction of the latently infected index dog until the death of the last infectious dog. The simulation model resulted in plausible results comparing outputs for the three different default control strategies (Fig 4). Rabies spreads through the communities in a wave pattern and, depending on the control strategy, can kill the entire dog population (S6 Fig illustrates an example epidemic curve). The reactive culling (RC) strategy reduces the number of rabid dogs; however the number of dead dogs is only slightly less than the total dog population. For the reactive vaccination (RV) strategy the number of rabid dogs (equal to the number of dead dogs) showed higher variability among the 1000 model simulations compared to RC, but in both regions, the median of RV was lower than for RC. Obviously, vaccination saves the dogs from death in contrast to culling strategies. The movement ban (MB) strategy showed a slight decrease of the outbreak size in the region of Elcho Island whereas no effect was observed in the NPA. However, it was found to be the strategy with the longest durations of outbreaks, demonstrating that movement bans (if not 100% compliant) only slow the speed of spread rather than reducing its size. The reduction in the number of movements between communities for MB was obvious, decreasing from a median of 52 (RV) and 46 (RC) to 19 (S7 Fig). Overall, outbreak duration ranged from 1−20 months (median 6.7 months) and was more homogenous between interventions than the outbreak size (Fig 4). Outbreaks lasting for one month did not spread beyond the index dog. For the RV strategy, the vaccination coverage was set at 70% of the households, producing dog level vaccination coverage of 59−75% and 56−76% for the NPA and Elcho Island region, respectively.


Development of a Novel Rabies Simulation Model for Application in a Non-endemic Environment.

Dürr S, Ward MP - PLoS Negl Trop Dis (2015)

Outbreak results for three example control strategies based in 1000 repetitions per scenario.Three control strategies (RV = reactive vaccination, RC = reactive culling, MB = movement bans) in two regions (Elcho = Elcho Island, NPA = Northern Peninsula Area) were simulated. Number of rabid dogs, dead dogs (i.e. due to rabies or culling) and outbreak duration per scenario are represented for the NPA (upper line) and for Elcho Island (lower line). The box represents the interquartile range (IQR), the horizontal line in the box the median and the whiskers extend to the most extreme data point which is no more than 1.5 times IQR from the box. Outliers are not shown.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482682&req=5

pntd.0003876.g004: Outbreak results for three example control strategies based in 1000 repetitions per scenario.Three control strategies (RV = reactive vaccination, RC = reactive culling, MB = movement bans) in two regions (Elcho = Elcho Island, NPA = Northern Peninsula Area) were simulated. Number of rabid dogs, dead dogs (i.e. due to rabies or culling) and outbreak duration per scenario are represented for the NPA (upper line) and for Elcho Island (lower line). The box represents the interquartile range (IQR), the horizontal line in the box the median and the whiskers extend to the most extreme data point which is no more than 1.5 times IQR from the box. Outliers are not shown.
Mentions: Two measures of outbreak size are the cumulative number of rabid dogs and cumulative number of dead dogs (due to rabies plus culled). The outbreak duration is defined as the number of days from the introduction of the latently infected index dog until the death of the last infectious dog. The simulation model resulted in plausible results comparing outputs for the three different default control strategies (Fig 4). Rabies spreads through the communities in a wave pattern and, depending on the control strategy, can kill the entire dog population (S6 Fig illustrates an example epidemic curve). The reactive culling (RC) strategy reduces the number of rabid dogs; however the number of dead dogs is only slightly less than the total dog population. For the reactive vaccination (RV) strategy the number of rabid dogs (equal to the number of dead dogs) showed higher variability among the 1000 model simulations compared to RC, but in both regions, the median of RV was lower than for RC. Obviously, vaccination saves the dogs from death in contrast to culling strategies. The movement ban (MB) strategy showed a slight decrease of the outbreak size in the region of Elcho Island whereas no effect was observed in the NPA. However, it was found to be the strategy with the longest durations of outbreaks, demonstrating that movement bans (if not 100% compliant) only slow the speed of spread rather than reducing its size. The reduction in the number of movements between communities for MB was obvious, decreasing from a median of 52 (RV) and 46 (RC) to 19 (S7 Fig). Overall, outbreak duration ranged from 1−20 months (median 6.7 months) and was more homogenous between interventions than the outbreak size (Fig 4). Outbreaks lasting for one month did not spread beyond the index dog. For the RV strategy, the vaccination coverage was set at 70% of the households, producing dog level vaccination coverage of 59−75% and 56−76% for the NPA and Elcho Island region, respectively.

Bottom Line: Mathematical and simulation disease models are useful tools to provide insights on the most effective control strategies and to inform policy decisions.Illustrative simulations produced plausible results with epidemic characteristics expected for rabies outbreaks in disease free regions (mean R0 1.7, epidemic peak 97 days post-incursion, vaccination as the most effective response strategy).Systematic sensitivity analysis identified that model outcomes were most sensitive to seven of the 30 model parameters tested.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Veterinary Science, The University of Sydney, Camden, New South Wales, Australia.

ABSTRACT
Domestic dog rabies is an endemic disease in large parts of the developing world and also epidemic in previously free regions. For example, it continues to spread in eastern Indonesia and currently threatens adjacent rabies-free regions with high densities of free-roaming dogs, including remote northern Australia. Mathematical and simulation disease models are useful tools to provide insights on the most effective control strategies and to inform policy decisions. Existing rabies models typically focus on long-term control programs in endemic countries. However, simulation models describing the dog rabies incursion scenario in regions where rabies is still exotic are lacking. We here describe such a stochastic, spatially explicit rabies simulation model that is based on individual dog information collected in two remote regions in northern Australia. Illustrative simulations produced plausible results with epidemic characteristics expected for rabies outbreaks in disease free regions (mean R0 1.7, epidemic peak 97 days post-incursion, vaccination as the most effective response strategy). Systematic sensitivity analysis identified that model outcomes were most sensitive to seven of the 30 model parameters tested. This model is suitable for exploring rabies spread and control before an incursion in populations of largely free-roaming dogs that live close together with their owners. It can be used for ad-hoc contingency or response planning prior to and shortly after incursion of dog rabies in previously free regions. One challenge that remains is model parameterisation, particularly how dogs' roaming and contacts and biting behaviours change following a rabies incursion in a previously rabies free population.

No MeSH data available.


Related in: MedlinePlus