Malaria, a deadly infectious disease caused by the protozoan Plasmodium, remains a major public health menace affecting at least half the human race. Although the large-scale usage of insecticides-based control measures, notably long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS), have led to a dramatic reduction of the burden of this global scourge between the period 2000 to 2015, the fact that the malaria vector (adult female Anopheles mosquito) has become resistant to all currently-available insecticides potentially makes the current laudable global effort to eradicate malaria by 2040 more challenging. This study presents a novel mathematical model, which couples malaria epidemiology with mosquito population genetics, for assessing the impact of insecticides resistance on malaria epidemiology. Numerical simulations of the model, using data relevant to malaria transmission dynamics in the Jimma Zone of Southwestern Ethiopia, show that the implementation of a control strategy based on using LLINs alone can lead to the effective control of malaria, while also effectively managing insecticide resistance, if the LLINs coverage in the community is high enough (over 90%). It is further shown that combining LLINs with IRS (both at reduced and realistically-attainable coverage levels) can lead to the aforementioned effective control of malaria and effective management of insecticide resistance if their coverage levels lie within a certain effective control window in the LLINs-IRS coverage parameter space (this result generally holds regardless of whether or not larviciding is implemented in the community). The study identifies three key parameters of the model that negatively affect the size of the effective control window, namely parameters related with the coverage level of larviciding, the number of new adult mosquitoes that are females and the initial size of the frequency of resistant allele in the community. For the coverage of LLINs and IRS within the effective control window, an additional increase in the values of the aforementioned three parameters may lead to a shrinkage in the size of the effective control window (thereby causing the failure of the insecticides-based control).

中文翻译：

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杀虫剂抗药性和疟疾控制：遗传学-流行病学建模方法。

疟疾是由原生动物疟原虫引起的致命性传染病，仍然是影响至少一半人类的主要公共卫生威胁。尽管大规模使用基于杀虫剂的控制措施，尤其是持久的杀虫网（LLIN）和室内残留喷洒（IRS），已大大减轻了2000年至2015年这一全球性灾害的负担，疟疾媒介（成年雌性按蚊）已经对所有目前可用的杀虫剂产生抗药性这一事实潜在地使当前值得称赞的全球努力到2040年消灭疟疾更具挑战性。这项研究提出了一种新颖的数学模型，该模型将疟疾流行病学与蚊子种群遗传学相结合，用于评估抗药性对疟疾流行病学的影响。使用与埃塞俄比亚西南部吉马地区疟疾传播动态有关的数据对该模型进行的数值模拟表明，仅基于LLIN的控制策略的实施可以有效控制疟疾，同时还可以有效控制杀虫剂的抗药性，如果社区中的LLIN覆盖率足够高（超过90％）。进一步显示，如果LLIN与IRS结合使用（覆盖率降低且可以实际达到），则可以有效控制上述疟疾，并有效控制杀虫剂的耐药性，前提是它们的覆盖水平在LLIN的某个有效控制窗口之内- IRS覆盖参数空间（无论是否在社区中实施杀幼虫，此结果通常都适用）。研究确定了该模型的三个关键参数，这些参数对有效控制窗口的大小有负面影响，即与幼虫的覆盖水平，雌性新成年蚊子的数量以及抗性等位基因频率的初始大小有关的参数。社区。为了在有效控制窗口内覆盖LLIN和IRS，上述三个参数的值的额外增加可能导致有效控制窗口大小的缩小（从而导致基于杀虫剂的控制失败）。雌性新成年蚊子的数量以及社区中抗性等位基因频率的初始大小。为了在有效控制窗口内覆盖LLIN和IRS，上述三个参数值的额外增加可能会导致有效控制窗口大小缩小（从而导致基于杀虫剂的控制失败）。雌性新成年蚊子的数量以及社区中抗性等位基因频率的初始大小。为了在有效控制窗口内覆盖LLIN和IRS，上述三个参数值的额外增加可能会导致有效控制窗口大小缩小（从而导致基于杀虫剂的控制失败）。