![]() ![]() Illustrative simulations produced plausible results with epidemic characteristics expected for rabies outbreaks in disease free regions (mean R 0 1.7, epidemic peak 97 days post-incursion, vaccination as the most effective response strategy). 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. However, simulation models describing the dog rabies incursion scenario in regions where rabies is still exotic are lacking. Existing rabies models typically focus on long-term control programs in endemic countries. Mathematical and simulation disease models are useful tools to provide insights on the most effective control strategies and to inform policy decisions. 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. (A) Dependency of the number of rabid dogs on the three parameters defining the distance kernel for NPA (B) dependency of the number of rabid dogs on the three parameters defining the distance kernel for Elcho Island (C) dependency of the outbreak duration on the three parameters defining the distance kernel for the NPA and (D) dependency of the outbreak duration on the three parameters defining the distance kernel for Elcho Island.ĭomestic dog rabies is an endemic disease in large parts of the developing world and also epidemic in previously free regions. The boxes in the boxplots represent 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. The three parameters are the intercept ( IC), coefficient ( coef) and standard error of the coefficient ( se) and their distinct values on the x-axis were: IC 0.439 (A), 0.878 (B) and 1.318 (C) coef: -0.0174 (A), -0.0116 (B), -0.0058 (C) and se as “default” (def), “narrow” (nar) or “wide” (wide). S13 Fig: Dependency of the model outcome (number of rabid dogs and outbreak duration) on the variation of the three parameters defining the distance kernel during the sensitivity analysis step 2. (A) Dependency of the number of rabid dogs on the incubation period (B) dependency of the outbreak duration on the incubation period (C) dependency of the number of rabid dogs on the probability of being bitten given a contact between dogs of different households (D) dependency of the outbreak duration on the probability of being bitten given a contact between dogs of different households (E) dependency of the number of rabid dogs on the probability of rabies transmission given a bite (F) dependency of the outbreak duration on the probability of rabies transmission given a bite and (G) dependency of the number of rabid dogs (upper half) and outbreak duration (lower half) on the vaccine efficacy. ![]() The x-axis represents the value of the mode (A/B, E/F) or mean (C/D, G) of the parameter and the terms “default”, “narrow” and “wide” describe the shape see main text for further details. S12 Fig: Dependency of the model outcome (number of rabid dogs and outbreak duration) on the variation of the mode/mean and shape of the parameters selected for sensitivity analysis step 2. To facilitate the comparison between the different kernels the probabilities for a daily contact between two dogs living 300 meters apart from each other are highlighted by the grey lines. The variation in the intercept α is presented in (A) large intercept (+50% of medium values), (B) medium intercept (medium value) and (C) small intercept (-50% of medium values). The standard error β se of β is decreasing from the left (+50% of default value) to the right (-50% of default value). The coefficient (β) is decreasing from the top (-50% of default value) to the bottom (+50% of default value). The three values of β se were defined as a relationship to β, with β se/β varying ±50% around the medium, which is calculated from the three kernels used in step 1 of the SA. The medium value of α was defined as the mean of the large (default kernel) and low (minimal kernel) intercept values of the kernels used in step 1 of the SA and the medium value of β was set at the mean coefficient value of all kernels used in step 1. All three variables (α, β, β se see main text for further details) defining the distance kernel were varied ±50% around a medium value, resulting in a total of 27 distinct distance kernels. S5 Fig: Variation in the input values of the distance kernel explored in the second step of the sensitivity analysis (SA). ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |