Improving malaria risk assessment in Blantyre district, Malawi by optimizing Anopheles surveillance using open-source and real-time data

使用开源和实时数据优化按蚊监测，改善马拉维布兰太尔地区的疟疾风险评估

• 批准号: MR/T031743/1
• 负责人: Julie-Anne Tangena
• 金额: $ 37.25万
• 依托单位: Liverpool School of Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT031743%2F1
• 关键词: Improving; malaria; risk; assessment; Blantyre

Malaria is an important public health problem in Malawi, where in 2017 alone more than 4.3 million cases were reported. It is a disease transmitted between people by a mosquito group called Anopheles. Malaria can be prevented by treating cases, removing Anopheles mosquitoes from an area (e.g. using insecticides) and creating a barrier between the local population and the mosquitoes (e.g. using bed nets). Significant investment in mosquito control and medical care has helped reduce malaria cases in the last decade. Although this has been successful, resources have not been enough to decrease malaria incidence below the annual 4 million cases. As we cannot treat everyone nor use mosquito control methods everywhere, resources need to be allocated in the most efficient and effective way. This requires a better understanding of both the Anopheles mosquitoes and malaria disease dynamics. To understand the malaria dynamic in Malawi, mosquito surveillance is essential. Unfortunately, mosquito surveillance is a costly and time-consuming activity that requires highly experienced and well-trained people. An important way in which mosquito surveillance can become more efficient and effective is by targeting specific areas: instead of collecting mosquitoes from all area, surveillance is focussed on high risk areas only. These targeted areas can be identified using data from the past (years ago and days ago), which show trends that help predict the future. This has already been done on a larger scale for disease outbreaks, where prevention activities are focussed on high risk provinces or regions. Although valuable, it is unfeasible to implement control activities throughout these large areas. During this fellowship I want to use similar technology at a much finer and more pragmatic scale. A targeted mosquito surveillance tool will be developed that 1) identifies high-risk areas and 2) selects sites within these high-risk areas where mosquito surveillance should take place for accurate risk assessment. Firstly, I will identify variables (such as temperature, rainfall, land cover and human population density) associated with an increase in Anopheles mosquitoes and subsequent increase in malaria cases. Historical mosquito data, malaria disease data and satellite data that captures environmental variables, will be analysed to identify trends. Secondly, the variables and values associated with an increase in Anopheles mosquitoes will be used to develop a predictive model that identifies high risk areas. Daily satellite data will be included in the model (real-time data) for up-to-date predictions. This model will be linked to a robust mosquito sampling framework that identifies specific sites within these high-risk areas where surveillance should take place. This tool will be validated in the field by comparing it to current mosquito surveillance activities. Finally, the predictive model will be adapted into a user-friendly interface that it requires limited training and provides clear direction. I will work closely with the Malawian Ministry of Health to implement this decision supporting tool in the vector control program. The aim of this fellowship is to help identify high-risk areas with less resources and bridge the gap between vector control programs and advanced quantitative methods. While statistical models and other advanced statistical approaches can improve efficacy and efficiency of vector control programs, they are rarely used due to their complexity. I will develop an easy-to-use tool that identifies defined areas where mosquito surveillance should take place. It will provide an economically feasible sampling strategy that improves the allocation of the limited resources available. Future development of the surveillance approach to other geographic areas and other mosquito-borne diseases (e.g. dengue and zika) will be investigated.

疟疾是马拉维的一个重要公共卫生问题，仅 2017 年就报告了超过 430 万例病例。这是一种由称为按蚊的蚊群在人与人之间传播的疾病。疟疾可以通过治疗病例、从某个地区清除按蚊（例如使用杀虫剂）以及在当地人口和蚊子之间建立屏障（例如使用蚊帐）来预防。过去十年，对蚊子控制和医疗保健的大量投资帮助减少了疟疾病例。尽管这一举措取得了成功，但资源还不足以将疟疾发病率降低到每年 400 万例以下。由于我们无法对待所有人，也无法在所有地方使用蚊虫控制方法，因此需要以最高效和最有效的方式分配资源。这需要更好地了解按蚊和疟疾疾病的动态。要了解马拉维的疟疾动态，蚊子监测至关重要。不幸的是，蚊子监测是一项昂贵且耗时的活动，需要经验丰富且训练有素的人员。提高蚊子监测效率和效果的一个重要方法是针对特定区域：不是从所有区域收集蚊子，而是仅针对高风险区域进行监测。这些目标区域可以使用过去（几年前和几天前）的数据来识别，这些数据显示的趋势有助于预测未来。针对疾病暴发，已经进行了更大规模的做法，预防活动集中在高风险省份或地区。尽管很有价值，但在这些大区域实施控制活动是不可行的。在这次研究期间，我想以更精细、更务实的规模使用类似的技术。将开发有针对性的蚊子监测工具，1）识别高风险区域，2）在这些高风险区域内选择应进行蚊子监测的地点，以进行准确的风险评估。首先，我将确定与按蚊增加以及随后疟疾病例增加相关的变量（例如温度、降雨量、土地覆盖和人口密度）。将分析历史蚊子数据、疟疾疾病数据和捕捉环境变量的卫星数据，以确定趋势。其次，与按蚊增加相关的变量和值将用于开发识别高风险区域的预测模型。每日卫星数据将包含在模型中（实时数据）以进行最新预测。该模型将与强大的蚊子采样框架相关联，该框架可识别这些高风险区域内应进行监测的特定地点。该工具将通过与当前的蚊子监测活动进行比较来进行现场验证。最后，预测模型将适应用户友好的界面，它需要有限的培训并提供明确的方向。我将与马拉维卫生部密切合作，在病媒控制计划中实施这一决策支持工具。该奖学金的目的是帮助识别资源较少的高风险地区，并弥合病媒控制计划和先进定量方法之间的差距。虽然统计模型和其他先进的统计方法可以提高病媒控制计划的功效和效率，但由于其复杂性而很少使用。我将开发一种易于使用的工具，用于识别应进行蚊子监测的指定区域。它将提供一种经济上可行的抽样策略，改善有限可用资源的分配。将调查其他地理区域和其他蚊媒疾病（例如登革热和寨卡）监测方法的未来发展。

项目成果

A first alert of Biomphalaria pfeifferi in the Lower Shire, Southern Malawi, a keystone intermediate snail host for intestinal schistosomiasis

马拉维南部下郡首次发现双脐螺（Biomphalaria pfeifferi），这是肠道血吸虫病的关键中间宿主

• DOI: 10.21203/rs.3.rs-3729630/v1
• 发表时间: 2023
• 作者: Nkolokosa C

Mosquito (Diptera: Culicidae) Larval Ecology in Rubber Plantations and Rural Villages in Dabou (Côte d'Ivoire).

达布（科特迪瓦）橡胶园和乡村的蚊子（双翅目：蚊科）幼虫生态。

• DOI: 10.1007/s10393-022-01594-8
• 发表时间: 2022
• 期刊: EcoHealth
• 影响因子: 2.5
• 作者: Traore I

Monitoring and simulating landscape changes: how do long-term changes in land use and long-term average climate affect regional biophysical conditions in southern Malawi?

• DOI: 10.1007/s10661-023-11783-9
• 发表时间: 2023-09-26
• 期刊: Environmental monitoring and assessment
• 影响因子: 3

Hotspots and super-spreaders: Modelling fine-scale malaria parasite transmission using mosquito flight behaviour.

• DOI: 10.1371/journal.ppat.1010622
• 发表时间: 2022-07
• 期刊: PLoS pathogens
• 影响因子: 6.7

Strengthening adult mosquito surveillance in Africa for disease control: learning from the present.

• DOI: 10.1016/j.cois.2023.101110
• 发表时间: 2023-12
• 期刊: Current opinion in insect science
• 影响因子: 5.3
• 作者: Coulibaly ZI;Gowelo S;Traore I;Mbewe RB;Ngulube W;Olanga EA;DePina AJ;Sanou A;Coleman S;Tangena JA
• 通讯作者: Tangena JA