结合不同的数据来源对蚊子丰度进行地理空间预测,以巴西为激励案例研究

A. Musah, Ella Browning, Aisha Aldosery, Iuri Valerio Graciano Borges, T. Ambrizzi, M. Tunali, Selma Basibüyük, O. Yenigün, G. Moreno, Clarisse Lins de Lima, Ana Clara Gomes da Silva, W. P. dos Santos, T. Massoni, L. Campos, P. Kostkova
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引用次数: 0

摘要

在世界上任何地方进行蚊子占用或侵扰的地理空间监测的障碍之一是缺乏位于住宅物业水平的初级昆虫学调查数据,并与重要的风险因素信息(例如,人为、环境和气候)相匹配,从而能够预测蚊子占用或侵扰的空间风险。这些数据对于在蚊虫典型流行的非洲、拉丁美洲和东南亚资源匮乏地区开展工作的学者、决策者和公共卫生项目管理者来说是非常宝贵的信息。现实情况是,在这些资源匮乏的环境中,这类数据仍然难以获得,即使有,也很少有高质量的数据,包括个体和空间特征,为虫害的地理空间描述和风险模式提供信息。有许多可靠的开放源代码空间数据在线来源,可用于解决这方面的数据缺乏问题。因此,本文的目的有三个:(1)强调这些可靠的开源数据可以从哪里获得,以及如何将它们作为风险因素来预测蚊子占用的空间;(2)以巴西为例,展示如何通过使用最大熵算法的生态位建模,将这些数据集结合起来预测虫媒病毒的存在;(3)讨论在这些开源在线数据源之外使用定制应用程序的好处,展示它们如何成为收集初级昆虫学调查数据的新“金标准”方法。本文的范围主要限于巴西的情况,因为它建立在与伯南布哥州和帕拉伊巴州环境监测机构的学术界和利益相关者的现有伙伴关系的基础上。由于埃及伊蚊在巴西的地方性地位,本文的分析也仅限于一种特定的蚊子,即埃及伊蚊。
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Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case study
One of the barriers to performing geospatial surveillance of mosquito occupancy or infestation anywhere in the world is the paucity of primary entomologic survey data geolocated at a residential property level and matched to important risk factor information (e.g., anthropogenic, environmental, and climate) that enables the spatial risk prediction of mosquito occupancy or infestation. Such data are invaluable pieces of information for academics, policy makers, and public health program managers operating in low-resource settings in Africa, Latin America, and Southeast Asia, where mosquitoes are typically endemic. The reality is that such data remain elusive in these low-resource settings and, where available, high-quality data that include both individual and spatial characteristics to inform the geospatial description and risk patterning of infestation remain rare. There are many online sources of open-source spatial data that are reliable and can be used to address such data paucity in this context. Therefore, the aims of this article are threefold: (1) to highlight where these reliable open-source data can be acquired and how they can be used as risk factors for making spatial predictions for mosquito occupancy in general; (2) to use Brazil as a case study to demonstrate how these datasets can be combined to predict the presence of arboviruses through the use of ecological niche modeling using the maximum entropy algorithm; and (3) to discuss the benefits of using bespoke applications beyond these open-source online data sources, demonstrating for how they can be the new “gold-standard” approach for gathering primary entomologic survey data. The scope of this article was mainly limited to a Brazilian context because it builds on an existing partnership with academics and stakeholders from environmental surveillance agencies in the states of Pernambuco and Paraiba. The analysis presented in this article was also limited to a specific mosquito species, i.e., Aedes aegypti, due to its endemic status in Brazil.
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