The impact of vector migration on the effectiveness of strategies to control gambiense human African trypanosomiasis

BACKGROUND: Several modeling studies have been undertaken to assess the feasibility of the WHO goal of eliminating gambiense human African trypanosomiasis (g-HAT) by 2030. However, these studies have generally overlooked the effect of vector migration on disease transmission and control. Here, we evaluated the impact of vector migration on the feasibility of interrupting transmission in different g-HAT foci. METHODS: We developed a g-HAT transmission model of a single tsetse population cluster that accounts for migration of tsetse fly into this population. We used a model calibration approach to constrain g-HAT incidence to ranges expected for high, moderate and low transmission settings, respectively. We used the model to evaluate the effectiveness of current intervention measures, including medical intervention through enhanced screening and treatment, and vector control, for interrupting g-HAT transmission in disease foci under each transmission setting. RESULTS: We showed that, in low transmission settings, under enhanced medical intervention alone, at least 70% treatment coverage is needed to interrupt g-HAT transmission within 10 years. In moderate transmission settings, a combination of medical intervention and a vector control measure with a daily tsetse mortality greater than 0.03 is required to achieve interruption of disease transmission within 10 years. In high transmission settings, interruption of disease transmission within 10 years requires a combination of at least 70% medical intervention coverage and at least 0.05 tsetse daily mortality rate from vector control. However, the probability of achieving elimination in high transmission settings decreases with an increased tsetse migration rate. CONCLUSION: Our results suggest that the WHO 2030 goal of G-HAT elimination is, at least in theory, achievable. But the presence of tsetse migration may reduce the probability of interrupting g-HAT transmission in moderate and high transmission foci. Therefore, optimal vector control programs should incorporate monitoring and controlling of vector density in buffer areas around foci of g-HAT control efforts

An ecological framework for informing permitting decisions on scientific activities in protected areas

<div><p>There are numerous reasons to conduct scientific research within protected areas, but research activities may also negatively impact organisms and habitats, and thus conflict with a protected area’s conservation goals. We developed a quantitative ecological decision-support framework that estimates these potential impacts so managers can weigh costs and benefits of proposed research projects and make informed permitting decisions. The framework generates quantitative estimates of the ecological impacts of the project and the cumulative impacts of the proposed project and all other projects in the protected area, and then compares the estimated cumulative impacts of all projects with policy-based acceptable impact thresholds. We use a series of simplified equations (models) to assess the impacts of proposed research to: a) the population of any targeted species, b) the major ecological assemblages that make up the community, and c) the physical habitat that supports protected area biota. These models consider both targeted and incidental impacts to the ecosystem and include consideration of the vulnerability of targeted species, assemblages, and habitats, based on their recovery time and ecological role. We parameterized the models for a wide variety of potential research activities that regularly occur in the study area using a combination of literature review and expert judgment with a precautionary approach to uncertainty. We also conducted sensitivity analyses to examine the relationships between model input parameters and estimated impacts to understand the dominant drivers of the ecological impact estimates. Although the decision-support framework was designed for and adopted by the California Department of Fish and Wildlife for permitting scientific studies in the state-wide network of marine protected areas (MPAs), the framework can readily be adapted for terrestrial and freshwater protected areas.</p></div

Important species interactions for macrobiota that should be accounted for when estimating ultimate impacts.

<p>Important species interactions for macrobiota that should be accounted for when estimating ultimate impacts.</p

Coastal marine habitat categories.

<p>Coastal marine habitat categories.</p

Proximate and ultimate impacts calculated for each of four hypothetical projects.

<p>Proximate and ultimate impacts calculated for each of four hypothetical projects.</p

Examples of reasons why proposed scientific research and educational activities might be appropriate within an MPA.

<p>Examples of reasons why proposed scientific research and educational activities might be appropriate within an MPA.</p

The decision-support framework.

<p>The framework for consideration of proposed research activities in marine protected areas, includes the four key assessment elements: MPA appropriateness, ecological impacts, cumulative impacts, and comparison to thresholds of acceptable impact for each MPA. The final result of this decision framework is a recommendation that the proposed research be approved or modified to reduce impacts to levels below the impact thresholds for affected populations, assemblages, and habitat.</p

Relative sensitivity of estimated impacts to populations, assemblages, and habitats to variation in key input parameters.

<p>Sensitivity is expressed as the rate of change in estimated impact (vertical axis) caused by change in the parameter value (horizontal axis). Input values are standardized by the range of possible values, and plotted as a proportion of that range (horizontal axes), while all other inputs are held constant. To ensure that the impacts plotted are realistic, constants were set at the median of real world values and the proportion of the population, assemblage, or habitat targeted was set to 5% for the proximate impacts (top panels A, B, and C), and the proximate impact to the population, assemblage or habitat was set to 1% for calculation of the ultimate impacts (bottom panels (D, E, and F). <b>(A)</b> Relative sensitivity of estimated proximate population impact caused by variation in mortality associated with sampling method (<i>M</i>_{<i>meth i</i>}), handling effects (<i>M</i>_{<i>hand targ i</i>}), and effectiveness of the sampling method (<i>Eff</i>_{<i>meth i</i>}). <b>(B)</b> Sensitivity of estimated proximate assemblage impact caused by variation in mortality associated with sampling method (<i>M</i>_{<i>meth i</i>}), handling effects on non-targeted species (<i>M</i>_{<i>hand non- targ</i>}), and susceptibility of non-target species to the sampling method (<i>Suscep</i>_{<i>meth i</i>}). <b>(C)</b> Sensitivity of estimated proximate habitat impact associated with variation in sampling methods (<i>P</i>_{<i>alt hab i meth i</i>}). <b>(D)</b> Sensitivity of ultimate population impact to variation in population recovery time (<i>RT</i>_{<i>targ i</i>}) and species interaction index (<i>Interaction</i>_{<i>targ i</i>}). <b>(E)</b> Sensitivity of the ultimate assemblage impact to variation in assemblage recovery time (<i>RT</i>_{<i>assemb i</i>}) and species interaction indices within the assemblage (<i>Interaction</i>_{<i>assemb i</i>}), and <b>(F)</b> sensitivity of ultimate habitat impacts to variation in habitat recovery time (<i>RT</i>_{<i>hab i</i>}).</p