Batrachochytrium salamandrivorans (Bsal) is an emerging invasive pathogen that is highly pathogenic to salamander species. Modeling infection dynamics in this system can facilitate proactive efforts to mitigate this pathogen's impact on north American species. Given its widespread distribution and high abundance, the eastern newt (Notophthalmus viridescens) has the potential to significantly influence Bsal epidemiology. We designed experiments to 1) estimate contact rates given different host densities and habitat structure and 2) estimate the probability of transmission from infected to susceptible individuals. Using parameter estimates from data generated during these experiments, we modeled infection and disease outcomes for a population of newts using a system of differential equations. We found that host contact rates were density-dependent, and that adding habitat structure reduced contacts. the probability of Bsal transmission given contact between newts was very high (>90%) even at early stages of infection. our simulations show rapid transmission of Bsal among individuals following pathogen introduction, with infection prevalence exceeding 90% within one month and >80% mortality of newts in three months. Estimates of basic reproductive rate (R 0) of Bsal for eastern newts were 1.9 and 3.2 for complex and simple habitats, respectively. Although reducing host density and increasing habitat complexity might decrease transmission, these management strategies may be ineffective at stopping Bsal invasion in eastern newt populations due to this species' hypersusceptibility. Across a variety of taxa, disease has been implicated as a major contributor to population-and species-level declines 1-6. Epidemiological modeling can facilitate disease response and management by elucidating host-pathogen interactions and identifying strategies that could reduce the severity of outbreaks in wild populations 7-9. Ideally, evaluating disease management strategies and modeling possible outcomes should occur prior to pathogen invasion, because the likelihood for disease control is greater and the cost of response is less 8,10-12. Conversely, reactive or delayed responses to disease outbreaks can result in significant biodiversity loss and economic impact, as demonstrated by the unexpected emergence of Batrachochytrium dendrobatids (Bd) 6,13,14 and Pseudogymnoascus destructans (the causative agent of White Nose syndrome) 13,15. The newly emergent fungal pathogen Batrachochytrium salamandrivorans (Bsal) provides a unique opportunity to evaluate possible management strategies, especially in areas where it has yet to emerge. Bsal is rapidly spreading in Europe, where it is believed to have been introduced from Asia via the pet trade 16,17. In areas where Bsal has emerged, populations of fire salamanders (Salamandra salamandra) have declined substantially 18. Preventing and mitigating Bsal outbreaks is described as one of the greatest current priorities for wildlife conservation 19. Bsal appears to have a high invasion probability ...
Numerous therapies have been implemented in an effort to minimize the debilitating effects of the degenerative eye disease Retinitis Pigmentosa (RP), yet none have provided satisfactory long-term solution. To date there is no treatment that can halt the degeneration of photoreceptors. The recent discovery of the RdCVF protein has provided researchers with a potential therapy that could slow the secondary wave of cone death. In this work, we build on an existing mathematical model of photoreceptor interactions in the presence of RP and incorporate various treatment regiments via RdCVF. Our results show that an optimal control exists for the administration of RdCVF. In addition, our numerical solutions show the experimentally observed rescue effect that the RdCVF has on the cones.
A two-patch epidemic model is considered in order to assess the impact of virtual dispersal on disease transmission dynamics. The two-patch system models the movement of individuals between the two-patches using a residence-time matrix P, where P depends on both residence times and state variables (infected classes). In this work, we employ this approach to a general two-patch SIR model in order to investigate the effect of state dependent dispersal behaviors on the disease dynamics. Furthermore, optimal control theory is employed to identify and evaluate patch-specific control measures aimed at reducing disease prevalence at a minimal cost. Optimal policies are computed under various dispersal scenarios (depending on the different residence-time matrix configurations). Our results suggest there is a reduction of the outbreak and the proportion of time spent by individuals in a patch exhibits less fluctuations in the presence of patch-specific optimal controls. Furthermore, the optimal strategies for each patch differ depending on the type of dispersal behavior and the different infection rate in a patch. In all of our results, we obtain that the optimal strategies reduce the number of infections per patch.
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