Mosquito Alert Dataset

データ レコード

この オカレンス（観察データと標本) リソース内のデータは、1 つまたは複数のデータ テーブルとして生物多様性データを共有するための標準化された形式であるダーウィン コア アーカイブ (DwC-A) として公開されています。 コア データ テーブルには、37,828 レコードが含まれています。

拡張データ テーブルは1 件存在しています。拡張レコードは、コアのレコードについての追加情報を提供するものです。 各拡張データ テーブル内のレコード数を以下に示します。

この IPT はデータをアーカイブし、データ リポジトリとして機能します。データとリソースのメタデータは、 ダウンロード セクションからダウンロードできます。 バージョン テーブルから公開可能な他のバージョンを閲覧でき、リソースに加えられた変更を知ることができます。

バージョン

次の表は、公にアクセス可能な公開バージョンのリソースのみ表示しています。

引用方法

研究者はこの研究内容を以下のように引用する必要があります。:

Mosquito Alert, Escobar A, Južnič-Zonta Ž (2024). Mosquito Alert Dataset. Version 1.15. CREAF - Centre de Recerca Ecològica i Aplicacions Forestals. Occurrence dataset. https://doi.org/10.15470/t5a1os

権利

研究者は権利に関する下記ステートメントを尊重する必要があります。:

パブリッシャーとライセンス保持者権利者は CREAF - Centre de Recerca Ecològica i Aplicacions Forestals。 To the extent possible under law, the publisher has waived all rights to these data and has dedicated them to the Public Domain (CC0 1.0). Users may copy, modify, distribute and use the work, including for commercial purposes, without restriction.

GBIF登録

このリソースをはGBIF と登録されており GBIF UUID: 1fef1ead-3d02-495e-8ff1-6aeb01123408が割り当てられています。   GBIF Spain によって承認されたデータ パブリッシャーとして GBIF に登録されているCREAF - Centre de Recerca Ecològica i Aplicacions Forestals が、このリソースをパブリッシュしました。

キーワード

Occurrence; Observation; Occurrence

外部データ

リソース データは他の形式で入手可能です。

連絡先

地理的範囲

Worldwide dataset, but mostly centered in Europe.

生物分類学的範囲

The dataset contains 24788 records of Aedes and Culex genus. For Aedes, four species are reported.

時間的範囲

プロジェクトデータ

BACKGROUND. Vector-borne diseases (VBDs) are infections caused by pathogens transmitted by carrier species (vectors), most of which are arthropods. VBDs are a major global health issue, with 80% of the world’s population at risk of one or more of these diseases [1]. VBDs account for 17% of the global burden of communicable diseases with over 1 billion infections and over 700,000 deaths caused by VBDs annually [1]. Many of these diseases, once limited to tropical and subtropical zones, are now increasingly seen in temperate areas [1, 2]. Among VBDs, mosquito-borne diseases (MBDs) account for a large share of cases. In 2017 the World Health Organisation estimated over 347 million MBD cases and over 447,000 deaths caused by MBDs annually [1]. Of the 3,591 known species of mosquitoes (order Diptera; family Culicidae) [3], only a fraction are involved in disease transmission or cause considerable nuisance to human and animal populations. These include invasive species that are spreading throughout Europe due to globalisation and climate change [2, 4]. There are five mosquito vectors of primary concern in Europe, four Aedes invasive mosquitoes (AIMs) and the native Culex pipiens (northern house mosquito). The four AIMs established in Europe are Ae. (Stegomyia) aegypti (yellow fever mosquito), Ae. (Stegomyia) albopictus (Asian tiger mosquito), Ae. (Hulecoetomyia) japonicus (Asian bush mosquito) and Ae. (Hulecoetomyia) koreicus (korean bush mosquito) [5]. Their ability to spread into new territories, and their capacity to act as vectors of tropical viral diseases such as dengue, chikungunya, Zika, yellow fever and Japanese encephalitis, make AIMs key vectors of public health relevance [6]. Notably, Ae. albopictus has already caused outbreaks of exotic arboviruses in Europe, i.e. outbreaks of dengue in Croatia, France, Spain and Italy [7, 8, 9, 10], and two of chikungunya in Italy [11]. In Europe, Culex pipiens is considered the principal vector of West Nile virus (WNV) [12, 13] and Usutu virus [14]. Since 2010, the WNV epidemiological pattern in Europe has evolved, with an increasing incidence of human and horse cases after what began with a very low level of endemicity. Several WNV outbreaks have occurred during the last decades and there was a significant peak in incidence in 2018, with 1,503 cases in the European Union [13, 15, 16]. Given the absence of effective vaccine solutions for most MBDs [17], vector surveillance is critical and needs to be strengthened and coordinated on a global scale. Currently, no global surveillance system is in place to track the emergence and spread of MBDs [18, 19]. Increased mosquito surveillance is needed for timely detection of changes in abundances and species diversity, providing valuable knowledge to health authorities and enabling swift mosquito control responses and other public health interventions. Obtaining field information with traditional mosquito surveillance tools is notoriously costly and time-consuming, and a major drawback of these tools is that they lack scalability. Costs can be significantly reduced by combining citizen science approaches with traditional ones for targeted surveillance [20, 21], and using big data spatial modelling techniques to compute risk maps of vector presence and abundance, human-vector interactions, and disease transmission zones at local or regional scales [22, 23]. Citizen science and the use of digital and networked technologies (Internet, mobile phones) have provided a new dimension to scientific research in the fields of vector ecology, eco-epidemiology, and public health [24, 25]. In the context of MBDs, a considerable amount of ongoing citizen science surveillance projects (29 projects operating in 16 countries all over the world, including some with wide geographic coverage) [26] have successfully involved public participation and provided data on mosquito populations. For future improvement, there is a need to continue engaging with stakeholders, researchers, public health agents, industry, and policymakers. CONTEXT. Mosquito Alert is a citizen science system aimed at investigating and managing disease-carrying mosquitoes. It has been operational since 2014, with most participants initially located in Spain and participation expanding worldwide, particularly in Europe since 2020 [20, 27]. It uses mobile phones and the Internet to bring together citizens, scientists, and public health authorities to fight against MBDs. Mosquito Alert combines authoritative data with citizen science methodologies for data quality assessment and modelling, enabling large-scale estimates of mosquito population dynamics and the human-mosquito interactions through which MBDs are transmitted across a range of scales. The data set presented here was collected through the Mosquito Alert mobile phone application. Citizen scientists provide geo-localized reports and images of targeted mosquito species, breeding sites and biting behaviour. Mosquito Alert also includes a module for sending samples to reference research labs in Europe that can be launched when and where considered necessary, allowing these labs to perform vector specialised identification and screening analyses. In addition, the app collects anonymous information on the geographic distribution of participants in order to correct for sampling effort biases [20]. The application also includes a participant scoring and a notification system that provides scientific and educational contents to participants. These features are expected to increase engagement and encourage frequent and extensive participation [28]. The five target species that citizen scientists can report are Ae. albopictus, Ae. aegypti, Ae. japonicus, Ae. koreicus, and Cx. pipiens. The targeted Aedes species are relatively easy to identify in photographs, whereas Culex pipiens can be difficult to distinguish from other Culex species. App tutorials and communication with citizen scientists are used to facilitate the identification and reporting of the targeted species. Adult mosquito reports containing photos are validated independently by three expert entomologists from the Digital Entomological Network in a web-based private platform, the digital Entolab. In addition to these species of interest, expert entomologists also identify other species of mosquitoes (not targeted) and even other insect groups. These identifications are also valuable from an educational perspective, as they help citizen scientists understand differences between targeted and non-targeted mosquitoes/insects. Since manual inspection of digital images is not a scalable option, the Mosquito Alert database of expert-validated images has been used to train a deep learning model to find Ae. albopictus [29] and the other target species (work in progress). This artificial intelligence system will not only be a helpful pre-selector for the expert validation process but also an automated classifier giving quick feedback to the app participants, which is expected to contribute to long-term motivation. In this dataset we must differentiate two periods: the period 2014-2020 (August) and the period 2020 (September)-2021. During the period 2014-2020 the project was mainly focused in Spain, funded from various national sources (see section Funding), and therefore, most of the reports are from there. During this period the system was looking for two invasive species: Ae. albopictus and Ae. aegypti. This mosquito surveillance tool has so far yielded valuable results. It has served to monitor the spread of Ae. albopictus in Spain [30, 31] and to investigate mosquito species dispersal mechanisms [32]. It was also the source of the first-ever confirmed observation of Ae. japonicus in Spain and it has served as the basis for estimating the Ae. japonicus distribution in northern Spain [33, 34]. Mosquito Alert also provided the first record of Ae. (Fredwardsius) vittatus in northwestern Spain and it has contributed to mosquito biodiversity knowledge more broadly [35]. In addition to all this, Mosquito Alert provides direct links between researchers, public health authorities and the general public, serving as a valuable means for promoting public awareness and education about MBDs. From September 2020 to 2021 the project increased the number of targeted mosquito species to the five listed above, and expanded across Europe with the support of European funding (AIM-COST OC-2017-1-22105, CA17108; VEO SC1-BHC-13-2019,874735). These projects have facilitated the required changes to increase the number of targeted species, scale the system at European level, and promote the development of a Digital Entomological Network of experts, boosting the dissemination of activities across Europe to promote data collection and direct interaction with citizen scientists in different countries. In 2020 and 2021, the digital citizen science surveillance through Mosquito Alert was carried out in combination with pan-European harmonised field entomological sampling (AIMSurv campaigns) under the framework of AIM-COST Action.

プロジェクトに携わる要員:

収集方法

There is no pre-set sampling frequency: participants can send as much data as they like wherever and whenever they like or can. Data sampling may be more intense in some periods due to dissemination events (e.g. project appearances in TV, Science Fairs, etc.) but is also naturally modulated by mosquito seasonal prevalence and activity patterns.

Method step description:

1. An anonymous citizen scientist observes an adult-mosquito (dead or alive).
2. Within the Mosquito Alert smart-phone application, the citizen scientist answers a small questionnaire with taxonomic and environment-related questions, indicates the location of the observation, attaches one or more photographs (optional), and adds comments (optional).
3. The report is reviewed by members of the Mosquito Alert team to remove anything that appears to be personally identifying information or inappropriate content.
4. Each photograph attached to the report is evaluated independently by three entomologists, and each assigns a label to the report indicating their degree of certainty as to whether the photographs show the target species. A "not sure" label is used if an expert is not able to classify a report. A report is flagged if for any reason the report needs further discussion or should be temporarily left out from the public view. The final taxonomic classification comes from averaging the three expert validations (see section Data Validation and Quality Control).
5. The report is released into the public domain after the three entomologists’ validation and reviewed by a senior entomologist who also checks flagged reports. As citizen scientists can try several pictures of the same specimen in one single report, one of the three experts has the responsibility to choose the final image released to the public domain (public map), which is the one published in the GBIF dataset. The selection criteria is to choose the mosquito image that best represents the observation, or the most valid for species determination.

書誌情報の引用

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