Hotspots and super-spreaders: Modelling fine-scale malaria parasite transmission using mosquito flight behaviour.
Hotspots and super-spreaders: Modelling fine-scale malaria parasite transmission using mosquito flight behaviour.
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热点和超级传播者：利用蚊子飞行行为模拟精细尺度疟疾寄生虫传播。
DOI：
10.1371/journal.ppat.1010622
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发表时间：
2022-07
期刊：
影响因子：
6.7
通讯作者：
中科院分区：
文献类型：
--
作者：
几年来，疟疾热点一直是公共卫生管理人员的重点，因为针对这些热点可以获得潜在的消除收益。在确定热点的同时，必须说明稳定和不稳定疟疾热点的总体网络，特别是在以消灭疟疾为目标的中低传播环境中。针对热点地区采取疟疾控制干预措施迄今尚未产生预期效益。在这项工作中，我们采用了一种机械随机算法来确定集群的超级传播者的房子和他们相关的稳定的热点占蚊子的飞行能力和疟疾感染的空间配置在房子的水平。研究结果表明，超级扩散房屋和热点的数量取决于那些村庄的空间结构。此外，超级传播者还与家畜和家庭组成等房屋特征有关。我们发现，大部分的传播与下午6点到10点之间的风有关，尽管晚些时候也很重要。在三个研究地区中的两个，蚊子混合飞行（顺风和逆风，都有随机成分）是最有可能造成疟疾传播的运动。最后，我们的算法（名为MALSWOTS）提供了一个估计的速度疟疾感染进展从房子到房子，这是每天约200-400米，一个数字与标记释放-再捕获研究的按蚊分散。使用样本外程序的交叉验证显示了热点的准确识别。我们的研究结果为确定和开发最佳工具，在潜在的热点地区进行高效和有效的时空针对性疟疾干预措施做出了重大贡献。传染性按蚊的传播对于确定疟疾寄生虫在人类宿主和蚊媒之间传播的地理范围至关重要。疟疾在人群中的发病率随时间和空间的变化而变化，并且通常以热点为特征，在这些热点中，导致疟疾传播的宿主或个人不成比例地少。在这里，我们提出了一种方法来确定疟疾热点和超级传播者房屋的位置，该方法基于使用作为马拉维南部更广泛的疟疾研究的一部分收集的感染和风数据来模拟传染性蚊子从房屋到房屋的移动。从我们的模型中，我们可以确定疟疾传播的关键组成部分，包括确定稳定和不稳定的疟疾热点（包括超级传播者房屋），疟疾在家庭之间传播的速度，量化村庄配置对疟疾传播的重要性，并确定当地生态环境中最重要的风类型。我们的结论是，有可能确定蚊子传播的感染网络结合感染和风数据。确定疟疾热点为在疟疾发病率高得不成比例的地区开展疟疾防治工作提供了机会。
Malaria hotspots have been the focus of public health managers for several years due to the potential elimination gains that can be obtained from targeting them. The identification of hotspots must be accompanied by the description of the overall network of stable and unstable hotspots of malaria, especially in medium and low transmission settings where malaria elimination is targeted. Targeting hotspots with malaria control interventions has, so far, not produced expected benefits. In this work we have employed a mechanistic-stochastic algorithm to identify clusters of super-spreader houses and their related stable hotspots by accounting for mosquito flight capabilities and the spatial configuration of malaria infections at the house level. Our results show that the number of super-spreading houses and hotspots is dependent on the spatial configuration of the villages. In addition, super-spreaders are also associated to house characteristics such as livestock and family composition. We found that most of the transmission is associated with winds between 6pm and 10pm although later hours are also important. Mixed mosquito flight (downwind and upwind both with random components) were the most likely movements causing the spread of malaria in two out of the three study areas. Finally, our algorithm (named MALSWOTS) provided an estimate of the speed of malaria infection progression from house to house which was around 200–400 meters per day, a figure coherent with mark-release-recapture studies of Anopheles dispersion. Cross validation using an out-of-sample procedure showed accurate identification of hotspots. Our findings provide a significant contribution towards the identification and development of optimal tools for efficient and effective spatio-temporal targeted malaria interventions over potential hotspot areas. The dispersal of infectious Anopheles mosquitoes is critical to determining the geographical range over which malaria parasites are transmitted between human hosts and mosquito vectors. Malaria rates in the human population vary over space and time and are often characterised by hotspots, where disproportionately few hosts or individuals contribute to malaria transmission. Here, we present an approach to determine the location of malaria hotspots and super spreader houses based on modelling infectious mosquito movements from house to house using infection and wind data collected as part of a wider malaria study in southern Malawi. From our model, we show that it is possible to determine key components of malaria transmission including the identification of stable and unstable malaria hotspots (including super spreader houses), how quickly malaria spreads between households, quantify the importance of village configuration on malaria spread and identify the most important wind types in the local ecological setting. We conclude that it is possible to determine networks of mosquito-borne infection from combining infection and wind data. The identification of malaria hotspots presents an opportunity to target malaria control efforts in areas where malaria is disproportionately high.
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