FLOODMAL

疟疾是一种病媒传播的疾病，发生在按蚊病媒、疟原虫寄生虫和脆弱人群重合的地方。打破疟疾传播周期的机会很多;最近通过使用驱虫蚊帐和室内喷洒来保护人口，以及在预防失败时使用药物治疗来治疗寄生虫，取得了相当大的进展。然而，这些成果受到蚊子对杀虫剂的抗药性和寄生虫抗药性的威胁。然而，除了保护人类宿主和治疗寄生虫外，还有一个额外的机会：控制蚊子病媒种群-不仅在房屋内，而且在更广泛的环境中的水生栖息地。为了针对这些幼虫栖息地的蚊子，我们必须了解它们发生的时间和地点。虽然可以从卫星图像中识别栖息地，但这种方法只能提供其当前分布的快照;为了实施诸如杀幼虫或栖息地管理等干预措施，我们需要提前了解其分布。只有通过了解洪水如何产生水体以及蚊子媒介种群如何利用这些水体，才有可能预测洪泛区环境。因此，有必要联合收割机水文学和生态学各自的科学学科，以获得基于过程的理解，需要预测幼虫的栖息地分布为目标。数十年来，许多洪水建模技术已被用于预测洪泛区的水分布，但这种方法尚未用于预测水生蚊子栖息地。我们的项目将结合联合收割机建立洪水模型和基于代理人的模型，模拟在景观尺度上的蚊子种群的摄食，繁殖和扩散，基于这些疟疾媒介之间的相互作用和它们的栖息地和宿主的分布。我们将把这些已建立的建模方法与最新一代地球观测图像和赞比亚西部省赞比西河Barotse洪泛区实地数据的创新使用相结合。我们认为，这种跨学科方法将使我们能够以适当的规模和程度，首次预测疟疾病媒幼虫丰度的高度季节性热点的分布，以实施干预措施。该平台不仅使我们能够预测疟疾病媒热点的短期动态，而且还能评估从现在到世纪末未来气候变化对疟疾传播危险的潜在影响。虽然温度对疟疾病媒的影响已得到广泛研究，但河流流量变化对疟疾危害的影响-以及解决这些问题所需的干预措施-在很大程度上仍然未知。为了提供赞比亚政府到2020年消除疟疾运动所需的复原力，这是一个必须填补的知识空白。

Malaria is a vector-borne disease which occurs where Anopheles mosquito vectors, Plasmodium parasites, and vulnerable human populations coincide. There are many opportunities to break the malaria transmission cycle; considerable recent progress has been achieved by protecting human populations using insecticide-treated bednets and house-spraying, and treating the parasite using drug therapies when prevention fails. Nonetheless, these gains are threatened by emerging resistance of mosquitoes to insecticides and drug resistance in the parasite. In addition to protecting human hosts and treating the parasite, however, there is an additional opportunity: control of mosquito vector populations - not only within houses, but in aquatic habitats in the wider environment. In order to target mosquitoes in these larval habitats, we must understand when and where they occur. Whilst habitats can be identified from satellite imagery, this approach can only provide a snapshot of their current distribution; in order to implement interventions such as larviciding or habitat management, we need to know their distribution in advance. Prediction in floodplain environments can only be possible through an understanding of how water bodies are created by flooding, and how these water bodies are used by mosquito vector populations. It is therefore necessary to combine the respective scientific disciplines of hydrology and ecology to gain the process-based understanding needed to predict larval habitat distributions for targeting. Numerous flood modelling techniques have been used for decades to predict the distribution of water across a floodplain, yet this approach has not been used to predict aquatic mosquito habitats. Our project will combine an established flood model with an agent-based model which simulates the feeding, breeding and dispersal of a mosquito population at landscape scale, based on the interactions between these malaria vectors and the distributions of their habitat and hosts. We will integrate these established modelling approaches with innovative use of latest generation earth observation imagery and field data from the Barotse floodplain of the Zambezi River in Zambia's Western Province. We believe that this interdisciplinary approach will allow us to predict the distribution of highly seasonal hotspots of malaria vector larval abundance for the first time at an appropriate scale and extent for implementing interventions. This platform will not only enable us to predict the short-term dynamics of malaria vector hotspots, but also to assess the potential impact of future climate change on malaria transmission hazard between now and the end of the 21st century. Whilst the effect of temperature on malaria vectors has been widely studied, the impacts of changed river flows on malaria hazard - and the intervention efforts which will be required to address them - remain largely unknown. To provide the resilience required by the Zambian government's campaign to eliminate malaria by 2020, this is a knowledge gap which must be filled.