Epidemiological Modelling of Simultaneous Control of Multiple Cassava Virus Diseases

在这个项目中，我们将与来自六个西非国家（尼日利亚、贝宁、多哥、加纳、科特迪瓦和布基纳法索）的研究人员合作，为这些木薯疾病设计有效的控制和管理战略，这些国家是比尔和梅林达·盖茨基金会和国际开发部WAVE（西非根和块茎作物病毒流行病学）项目的一部分。我们的研究旨在评估能够以具有成本效益的方式最大限度地提高产量的疾病控制方法。目前可能控制CMD和CBSD的方法包括使用抗性或耐受性品种，清除受感染的植物和限制贸易。从我们以前关于控制木薯疾病的工作中，我们知道，执行这些措施可能并不总是直截了当的。例如，贸易限制限制了疾病的传播，但也减缓了新品种通过非正规贸易部门的传播。最近，比尔和梅林达·盖茨基金会和国际开发部批准了“西非根茎作物病毒流行病学”项目。WAVE项目旨在收集数据，以支持疾病控制战略的制定。我们目前通过抽样指导支持WAVE项目，但是，WAVE没有能力使用数据来开发模型并同时为多种疾病提供一套有效的控制方案。我们的建议旨在通过建模并结合WAVE项目的数据，确定如何最好地协调一种综合方法，在使用抗性和耐受性种植材料的基础上控制这些疾病，这将有助于该地区的决策者规划如何最好地实施疾病控制战略，以减轻造成的产量损失。我们将开始我们的工作，利用最新的卫星人口、农田分布和木薯产量数据，建立该区域木薯分布的模型。我们将使用此主机分布图来建议抽样策略，并提供统计和数据管理支持。然后，我们将开发一个CMD传播模型，并探索提供强大的病原体控制的因素。我们将调整以前的CBSD传播模型，以适应西非的情况，并将确定引进的风险和传播的可能速度，如果它到达西非。然后，我们将确定快速遏制和根除这种疾病的有效因素。最后，我们将结合联合收割机的模型，同时考虑两种疾病的传播和控制。我们将利用这一点为WAVE合作者提供最佳控制策略的建议，以增加成功管理CMD的可能性，同时保留根除CBSD入侵的能力。这将大大减少整个区域木薯种植者因这两种疾病造成的产量损失，并随之增加人口福利。

In this project, we will collaborate with researchers from six West African countries (Nigeria, Benin, Togo, Ghana, Côte d'Ivoire and Burkina Faso), which are part of the Bill and Melinda Gates Foundation and the Department for International Development project WAVE (West Africa Virus Epidemiology for Root and Tuber Crops), to design effective control and management strategies for these cassava diseases. Our research aims to assess disease control methods that could maximise yield in a cost-effective manner. Current potential control methods for CMD and CBSD include using resistant or tolerant cultivars, removing infected plants and restricting trade. From our previous work on the control of cassava diseases, we know that implementing these measures may not always be straightforward. For example, trade restrictions limit the dispersal of the disease, but also slow the dispersal of new varieties through the informal trade sector. This suggests that control through a combination of strategies requires careful planning.Recently the Bill and Melinda Gates Foundation and the Department for International Development have awarded the project "West African Virus Epidemiology for Root and Tuber Crops" (WAVE). The WAVE project aims to collect data to underpin the development of disease control strategies. We currently support the WAVE project with sampling guidance, however, within WAVE there is no capacity to use the data to develop models and produce a set of effective control options for multiple diseases simultaneously. Our proposal aims to identify, using modelling in combination with the data from the WAVE project, how best to coordinate a combined approach to controlling these diseases based on the use of resistant and tolerant planting material, which will help decision-makers across the region to plan how best to implement disease control strategies to alleviate the yield loses caused.In order to assess the most effective cassava disease control strategies, we will begin our work by modelling the distribution of cassava in the region, using the most recent satellite population, cropland distribution and cassava production data. We will use this host distribution map to advise on sampling strategies, as well as offering statistical and data management support throughout. We will then develop a model for the spread of CMD and explore factors that deliver robust control of the pathogen. We will adapt a previous model on the spread of CBSD to a West-African context, and will determine both the risk of introduction and the likely rate of spread should it reach West Africa. We will then identify factors that are effective in rapid containment and eradication of the disease. Finally, we will combine the models to consider the dispersal and control of both diseases simultaneously. We will use this to advise WAVE collaborators on the best use of control strategies in order to increase the likelihood of successfully managing CMD while retaining the ability to eradicate CBSD incursions. This will lead to significant reductions in yield losses attributed to both diseases for cassava growers across the region and a subsequent increase in population welfare.