說明
Mosquito vector data are collected in a myriad of different ways by multiple data collectors for a wide variety of purposes. They are distributed in literature, among individual scientists, organisations and countries that are not always well connected. In isolation, these data are able to answer the questions they were collected to address, but when combined, their value multiplies. The Vector Atlas aims to update and create vector species maps and spatial products that improve disease prediction, mitigation and preparedness. We propose to build a Vector Atlas data-hub that links ‘core’ (e.g. existing vector occurrence, bionomics and insecticide resistance data, MAP covariates) and ‘complimentary’ (e.g. GBIF, MalariaGEN, VectorBase, Amplicon Panel project) data resources to provide a ‘one stop shop’ of relatable and cross referenced data access. Using our standardised and comprehensive collation protocols, core data resources will be fully updated. The Vector Atlas data hub will enable the integration of historical and ongoing collections of data through systematic methods founded on well-defined ontologies. These data will underpin a suite of intuitive and informative maps generated using cutting-edge modelling techniques. The Vector Atlas will deliver spatial data and model outputs specifically tailored to inform the control of mosquito vectors of disease (From Investment document).
資料紀錄
此資源出現紀錄的資料已發佈為達爾文核心集檔案（DwC-A），其以一或多組資料表構成分享生物多樣性資料的標準格式。 核心資料表包含 29 筆紀錄。
此 IPT 存放資料以提供資料儲存庫服務。資料與資源的詮釋資料可由「下載」單元下載。「版本」表格列出此資源的其它公開版本，以便利追蹤其隨時間的變更。
版本
以下的表格只顯示可公開存取資源的已發布版本。
如何引用
研究者應依照以下指示引用此資源。:
Vector Atlas test publication
權利
研究者應尊重以下權利聲明。:
此資料的發布者及權利單位為 Training Organization。 This work is licensed under a Creative Commons Attribution Non Commercial (CC-BY-NC 4.0) License.
GBIF 註冊
此資源已向GBIF註冊，並指定以下之GBIF UUID: 0247dcef-1e64-4a63-bb1e-844eafee73eb。 Training Organization 發佈此資源，並經由GBIF Secretariat同意向GBIF註冊成為資料發佈者。
關鍵字
Control; Occurrence; Malaria; Modelling; Maps; Mosquito; Vector; Observation; Control; Occurrence; Malaria; Modelling; Maps; Mosquito; Vector
聯絡資訊
- 元數據提供者 ●
- 出處 ●
- 連絡人
- PI
https://orcid.org/http://orcid.org/0000-0001-7145-3179
地理涵蓋範圍
Africa
| 界定座標範圍 | 緯度南界 經度西界 [-35.604, -27.598], 緯度北界 經度東界 [38.135, 53.613] |
|---|
分類群涵蓋範圍
Diptera, Culicidae
| Genus | Anopheles (Mosquito) |
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計畫資料
Mosquito vector data are collected in a myriad of different ways by multiple data collectors for a wide variety of purposes. They are distributed in literature, among individual scientists, organisations and countries that are not always well connected. In isolation, these data are able to answer the questions they were collected to address, but when combined, their value multiplies. The Vector Atlas aims to update and create vector species maps and spatial products that improve disease prediction, mitigation and preparedness. We propose to build a Vector Atlas data-hub that links ‘core’ (e.g. existing vector occurrence, bionomics and insecticide resistance data, MAP covariates) and ‘complimentary’ (e.g. GBIF, MalariaGEN, VectorBase, Amplicon Panel project) data resources to provide a ‘one stop shop’ of relatable and cross referenced data access. Using our standardised and comprehensive collation protocols, core data resources will be fully updated. The Vector Atlas data hub will enable the integration of historical and ongoing collections of data through systematic methods founded on well-defined ontologies. These data will underpin a suite of intuitive and informative maps generated using cutting-edge modelling techniques. The Vector Atlas will deliver spatial data and model outputs specifically tailored to inform the control of mosquito vectors of disease (From Investment document).
| 計畫名稱 | Vector Atlas |
|---|---|
| 經費來源 | Bill and Melinda Gates Foundation |
| 研究區域描述 | In the first instance, we will focus on Africa and on the mosquito vectors of human malaria. Globally, Africa still suffers the highest malaria mortality and morbidity, and in a post COVID world, this is likely to increase due to the disruption in ongoing malaria intervention programmes (Weiss et al. 2021). Indeed, the WHO estimates that a lack of resources, as a consequence of the COVID pandemic, may result in between 40,000-100,000 extra malaria deaths in sub-Saharan Africa (World Health Organization 2020). Thus streamlining vector data to one hub to inform focused and thus financially prudent control efforts is both timely and necessary (From Investment document) (From Investment document). |
| 研究設計描述 | Following our established, published and comprehensive data collation protocol (Hay et al. 2010) we will bring the African occurrence dataset fully up to date and expand it to include PSV. These data will provide additional information on the species responsible for residual transmission. In addition, during the data abstraction process, peripheral information concerning the surrounding environment, such as the presence of livestock, domestic animals, notable crops or vegetation will be recorded. Where reported, human behaviour relevant to malaria transmission will also be captured to begin building data on gaps in malaria protection. To ensure data remains of the highest quality, our data collation protocol allows uncertainty to be captured. For example, by recording the methodology by which the mosquito was identified, a data user can determine whether there is any ambiguity in the reported identity. Spatial data is also categorised depending on the level of accuracy of the reported location within the data source (e.g. specified GPS coordinates of the sample site versus coordinate of the village where the sampling took place). Data abstraction will follow our published and widely used data abstraction protocol that includes two checking stages alongside the initial abstraction process ensuring accurate and reliable datasets. The occurrence dataset will also be supplemented with data generated via the Amplicon Panel Project @ The Wellcome Sanger Institute; a five-year data collection process to test and showcase its multi-locus sequencing amplicon panel. This new approach allows mosquito specimens to be identified using multiple loci in a non-destructive process. It can reduce the need for preliminary morphological identification, which, when conducted by inexperienced entomologists, can lead to the mis-selection of species-specific primers, non-amplification and subsequently a failure to fully identify a species of concern. The Institute’s data collation requires recording standardized metadata, including details on where and when the mosquito was sampled, which aligns well with the Vector Atlas’ objectives (From Investment document). |
取樣方法
*THIS IS THE SAME DATA FROM OLD DATA TEST PUBLICATION* The MAP collaboration has adopted three linked approaches to identifying empirical PR survey data: a) a traditional electronic search using PubMed [38] with 'malaria' and MEC name as free text rather than Medical Subject Headings terms that tend to be less inclusive; b) direct contact with malaria field scientists, research institutions and control agencies in MECs identified through the PubMed search; and c) an e-mail circular, linked to the launch of the MAP website, to locate sources of information not readily accessible from the first two search strategies. Assembling a digital data archive Each source of information was reviewed by one of the authors of this paper and the data extracted into a customized Microsoft Access (Microsoft, 2003) database. A unique, auto-generated identifier links the record to a reference manager platform and to an electronic copy of the source when this could be obtained. The entry form includes all fields related directly to malaria prevalence, including some geographic descriptions (geographic extent of the study area, as well as the land cover type as reported by the author(s) as either urban or rural, and forest and/or rice cultivation), and a full description of the cross-sectional study and its results (number of surveys, parasite detection method, dates, age, range sampled, number of slides examined and numbers of positive individuals). Records of sibling species occurrence, where species were identified using molecular methods, were retrieved from the published literature (from both resistance and behavioural studies) and from unpublished sources to compile a set of presence records for each species. A larger dataset, including all Anopheles surveys in the region, was used as a background dataset that captured sampling bias. From Wiebe A, Longbottom J, Gleave K, Shearer FM, Sinka ME, Massey NC, Cameron E, Bhatt S, Gething PW, Hemingway J, Smith DL, Coleman M, Moyes CL. Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance. Malar J. 2017 Feb 20;16(1):85. doi: 10.1186/s12936-017-1734-y. PMID: 28219387; PMCID: PMC5319841. Quality control Once a relevant literature source was identified, information was extracted using a list of data fields specified by a detailed pro forma. Precise geo-positioning was conducted using established methods [39], so that any uncertainty associated with the positioning could be estimated [46–49]. From Hay SI, Sinka ME, Okara RM, Kabaria CW, Mbithi PM, Tago CC, Benz D, Gething PW, Howes RE, Patil AP, Temperley WH, Bangs MJ, Chareonviriyaphap T, Elyazar IR, Harbach RE, Hemingway J, Manguin S, Mbogo CM, Rubio-Palis Y, Godfray HC. Developing global maps of the dominant anopheles vectors of human malaria. PLoS Med. 2010 Feb 9;7(2):e1000209. doi: 10.1371/journal.pmed.1000209. References 39 Guerra CA, Hay SI, Lucioparedes LS, Gikandi PW, Tatem AJ, Noor AM, Snow RW. Assembling a global database of malaria parasite prevalence for the Malaria Atlas Project. Malar J. 2007 Feb 16;6:17. doi: 10.1186/1475-2875-6-17. 46. Chapman AD, Wieczorek J (2006) Guide to best practices for georeferencing. Copenhagen: Global Biodiversity Information Facility. 47. Wieczorek J, Guo Q, Hijmans RJ (2004) The point-radius method for georeferencing locality descriptions and calculating associated uncertainty. Int J Geogr Inf Sci 18: 745–767. 48. Guralnick RP, Wieczorek J, Beaman R, Hijmans RJ (2006) BioGeomancer: automated georeferencing to map the world’s biodiversity data. PLoS Biol 4: e381. doi:10.1371/journal.pbio.0040381. 49. Guo Q, Liu Y, Wieczorek J (2008) Georeferencing locality descriptions and computing associated uncertainty using a probabilistic approach. Int J Geogr Inf Sci 22: 1067–1090.
| 研究範圍 | Current dataset for Africa includes 38,351 records and runs from 1970 to 2015. It currently does not include all sibling species nor any data for the PSV. Updated data are being gatheres from the literature and researchers and other stakeholders are expected to upload data. |
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| 品質控管 | Once uploaded, data quality will be assured via a series of automated curation steps, including validation and interpretation depending on source, and, where necessary, by research personnel. One of the most important elements of the vector data we have curated up until now (as MAP but also in other related projects) is that our data shows the level of uncertainty around its quality both in terms of the species identification and the georeferencing. There are also clear links to the source of the data, ensuring transparency in ownership as well as a ‘paper trail’ for data users. |
方法步驟描述:
- *THIS IS THE SAME DATA FROM OLD DATA TEST PUBLICATION* ● First round data abstraction from the collated literature; data to be entered into a pre evaluated template that allows occurrence, bionomic and IR data to be reconciled. ● Data georeferenced and checked against peripheral information given in the source ● Second round data checks repeat the data abstraction process by a second independent research assistant. ● Third round data checks by a third independent research assistant, focus on numerical abstracted data and georeferenced coordinates ● Automated data checks - all data mapped and confirmed to lie in the correctly stated country, admin area etc.