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多管齐下对抗蚊子:疟疾消除矢量控制优化模型(VCOM)的见解

Attacking the mosquito on multiple fronts: Insights from the Vector Control Optimization Model (VCOM) for malaria elimination.

作者信息

Kiware Samson S, Chitnis Nakul, Tatarsky Allison, Wu Sean, Castellanos Héctor Manuel Sánchez, Gosling Roly, Smith David, Marshall John M

机构信息

Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.

Mathematics, Statistics, and Computer Science Department, Marquette University, Milwaukee, Wisconsin, United States of America.

出版信息

PLoS One. 2017 Dec 1;12(12):e0187680. doi: 10.1371/journal.pone.0187680. eCollection 2017.

Abstract

BACKGROUND

Despite great achievements by insecticide-treated nets (ITNs) and indoor residual spraying (IRS) in reducing malaria transmission, it is unlikely these tools will be sufficient to eliminate malaria transmission on their own in many settings today. Fortunately, field experiments indicate that there are many promising vector control interventions that can be used to complement ITNs and/or IRS by targeting a wide range of biological and environmental mosquito resources. The majority of these experiments were performed to test a single vector control intervention in isolation; however, there is growing evidence and consensus that effective vector control with the goal of malaria elimination will require a combination of interventions.

METHOD AND FINDINGS

We have developed a model of mosquito population dynamic to describe the mosquito life and feeding cycles and to optimize the impact of vector control intervention combinations at suppressing mosquito populations. The model simulations were performed for the main three malaria vectors in sub-Saharan Africa, Anopheles gambiae s.s, An. arabiensis and An. funestus. We considered areas having low, moderate and high malaria transmission, corresponding to entomological inoculation rates of 10, 50 and 100 infective bites per person per year, respectively. In all settings, we considered baseline ITN coverage of 50% or 80% in addition to a range of other vector control tools to interrupt malaria transmission. The model was used to sweep through parameters space to select the best optimal intervention packages. Sample model simulations indicate that, starting with ITNs at a coverage of 50% (An. gambiae s.s. and An. funestus) or 80% (An. arabiensis) and adding interventions that do not require human participation (e.g. larviciding at 80% coverage, endectocide treated cattle at 50% coverage and attractive toxic sugar baits at 50% coverage) may be sufficient to suppress all the three species to an extent required to achieve local malaria elimination.

CONCLUSION

The Vector Control Optimization Model (VCOM) is a computational tool to predict the impact of combined vector control interventions at the mosquito population level in a range of eco-epidemiological settings. The model predicts specific combinations of vector control tools to achieve local malaria elimination in a range of eco-epidemiological settings and can assist researchers and program decision-makers on the design of experimental or operational research to test vector control interventions. A corresponding graphical user interface is available for national malaria control programs and other end users.

摘要

背景

尽管经杀虫剂处理的蚊帐(ITNs)和室内滞留喷洒(IRS)在减少疟疾传播方面取得了巨大成就,但在当今许多环境中,仅靠这些工具不太可能足以消除疟疾传播。幸运的是,现场实验表明,有许多有前景的病媒控制干预措施可通过针对广泛的生物和环境蚊虫资源来补充ITNs和/或IRS。这些实验大多是单独测试单一的病媒控制干预措施;然而,越来越多的证据和共识表明,以消除疟疾为目标的有效病媒控制将需要多种干预措施的组合。

方法与结果

我们开发了一个蚊虫种群动态模型,以描述蚊虫的生活和觅食周期,并优化病媒控制干预组合在抑制蚊虫种群方面的影响。对撒哈拉以南非洲的三种主要疟疾媒介冈比亚按蚊、阿拉伯按蚊和嗜人按蚊进行了模型模拟。我们考虑了疟疾传播低、中、高的地区,分别对应每人每年10、50和100次感染性叮咬的昆虫学接种率。在所有环境中,除了一系列其他病媒控制工具以中断疟疾传播外,我们还考虑了50%或80%的基线ITN覆盖率。该模型用于遍历参数空间以选择最佳的最优干预方案。样本模型模拟表明,从覆盖率为50%(冈比亚按蚊和嗜人按蚊)或80%(阿拉伯按蚊)的ITNs开始,并添加不需要人类参与的干预措施(例如覆盖率为80%的杀幼虫剂、覆盖率为50%的经内吸杀虫剂处理的牛以及覆盖率为50%的有吸引力的有毒糖饵)可能足以将所有这三种物种抑制到实现当地疟疾消除所需的程度。

结论

病媒控制优化模型(VCOM)是一种计算工具,用于预测在一系列生态流行病学环境中病媒控制联合干预措施对蚊虫种群水平的影响。该模型预测了在一系列生态流行病学环境中实现当地疟疾消除的病媒控制工具的特定组合,并可协助研究人员和项目决策者设计实验性或操作性研究以测试病媒控制干预措施。国家疟疾控制项目和其他终端用户可使用相应的图形用户界面。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9e9/5711017/f046edc42b2a/pone.0187680.g001.jpg

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