説明
The Mosquito Alert dataset includes occurrence records of adult mosquitoes. The records were collected through Mosquito Alert, a citizen science system for investigating and managing disease-carrying mosquitoes. Each record presented in the database is linked to a photograph submitted by a citizen scientist and validated by entomological experts to determine if it provides evidence of the presence of any of five targeted mosquito vectors of top concern in Europe (i.e. Aedes albopictus, Aedes aegypti, Aedes japonicus, Aedes koreicus, Culex pipiens). The temporal coverage of the database is from 2014 through 2022 and the spatial coverage is worldwide. Most of the records from 2014 to 2020 are from Spain, reflecting the fact that the project was funded by Spanish national and regional funding agencies. Since autumn 2020 the data has expanded to include substantial records from other countries in Europe, particularly the Netherlands, Italy, and Hungary, thanks to a human volunteering network of entomologists coordinated by the AIM-COST Action and to technological developments through the VEO project to increase scalability. Among many possible applications, Mosquito Alert dataset facilitates the development of citizen-based early warning systems for mosquito-borne disease risk. This dataset can be further re-used for modelling vector exposure risk or training machine-learning detection and classification routines on the linked images, to help experts in data validation and build up automated alert systems.
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、24,788 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 (2023). Mosquito Alert Dataset. Version 1.13. 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。
| Mosquito Alert webmap | http://webserver.mosquitoalert.com/ ASCII CSV |
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| Mosquito Alert pictures (BioImage Archive) | http://www.mosquitoalert.com/mosquito-images-data-base/ NA PNG, JPG |
| Mosquito Alert reports | https://doi.org/10.5281/zenodo.6235191 ASCII JSON |
連絡先
- 最初のデータ採集者
- メタデータ提供者 ●
- プログラマー
https://orcid.org/0000-0002-6856-0480
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地理的範囲
Worldwide dataset, but mostly centered in Europe.
| 座標（緯度経度） | 南 西 [-90, -180], 北 東 [90, 180] |
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生物分類学的範囲
The dataset contains 24788 records of Aedes and Culex genus. For Aedes, four species are reported.
| Genus | Culex (Linnaeus, 1758) |
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| Species | Aedes albopictus (Skuse, 1895) (Asian tiger mosquito), Aedes aegypti (Linnaeus, 1762) (Yellow fever mosquito), Aedes japonicus (Theobald, 1901) (Asian bush mosquito), Aedes koreicus (Edwards, 1917) (Korean bush mosquito) |
時間的範囲
| 開始日 / 終了日 | 2014-06-18 / 2022-12-31 |
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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.
| タイトル | Mosquito Alert |
|---|---|
| 識別子 | mosquitoalert |
| ファンデイング | This work was supported by: 2021-2022 Fair Computational Epidemiology (FACE); Plataforma Temática Interdisciplinar PTI+ Salud Global, Consejo Superior de Investigaciones Científicas (CSIC); Grant No.: N/A | 2020-2025 Human-Mosquito Interaction Project: Host-vector networks, mobility and the socio-ecological context of mosquito-borne disease; European Research Council (ERC); Grant No.: 853271 | 2020-2021 Strengthening Barcelona’s Defenses Against Disease-Vector Mosquitoes: Automatically Calibrated Citizen-Based Surveil- lance, Barcelona Ciència; Ajuntament de Barcelona, Institut de Cultura; Grant No.: BCNPC/00041 | 2020-2024 VEO: Versatile Emerging infectious disease Observatory, H2020 SC1-BHC-13-2019; European Commission (EC); Grant No.: 874735 | 2020-2025 Preparing for vector-borne virus outbreaks in a changing world: a One Health Approach; Dutch National Research Agenda (NWA); Grant No.: NWA/00686468 | 2019-2021 Big Mosquito Bytes: Community-Driven Big Data Intelligence to Fight Mosquito-Borne Disease; Fundació ”La Caixa”, Health Research 2018 “la Caixa” Banking Foundation; Grant No.: HR19-00336 | 2018-2022 Aedes Invasive Mosquitoes (AIM), COST ACTION OC-2017-1-22105; European Cooperation in Science and Technology (COST); Grant No.: CA17108 | 2018 Mosquito Alert: programa para investigar y controlar mosquitos vectores de enfermedades como el Dengue, el Chikungunya y el Zika; Fundació ”La Caixa”; Grant No.: N/A | 2017-2019 Plataforma Integral per al Control de l’Arbovirosis a Catalunya (PICAT); Departament de Salut, Programa PERIS 2016-2020, Generalitat de Catalunya; Grant No.: 00466 | 2016-2018 Ciència ciutadana per a la millora de la gestió i els models predictius de dispersió i distribució real de mosquit tigre a la Província de Girona; Diputació de Salut de Girona (DIPSALUT); Grant No.: N/A | 2016 Nuevas herramientas de participación en ciencia ciudadana: laboratorios de validación y cocreación para AtrapaelTigre.com; Fundación Española para la Ciencia y la Tecnología (FECYT); Grant No.: FCT-15-9515 | 2016-2017 Mosquito Alert: programa para investigar y controlar mosquitos vectores de enfermedades como el Dengue, el Chikungunya y el Zika; Fundació ”La Caixa”; Grant No.: N/A | 2016-2017 Ciència ciutadana per a la millora de la gestió i els models predictius de dispersió i distribució real de mosquit tigre a la Província de Girona; Diputació de Salut de Girona (DIPSALUT); Grant No.: N/A | 2015-2016 Citizens-based early warning systems for invasive species and disease vectors: The case of the Asian Tiger mosquito; Fundació ”La Caixa” and Centre de Recerca Ecològica i Aplicacions Forestals (CREAF); Grant No.: N/A | 2014-2016 Invasión del mosquito tigre en España: Salud pública y cambio global; Ministerio de Economía y Competitividad, Plan Estatal I+D+I; Grant No.: CGL2013-43139-R | 2014 Diseño e implementación de un sistema ciudadano de alerta y seguimiento del mosquito tigre: ciencia en sociedad (Atrapa el Tigre 2.0); Fundación Española para la Ciencia y la Tecnología (FECYT); Grant No.: FCT-13-701955 |
| Study Area Description | The project was initially focused on Spain, although in recent years it’s coverage expanded to Europe and is slowly taking momentum worldwide. At the moment, the Mosquito Alert application is available in 20 languages. |
| 研究の意図、目的、背景など（デザイン） | See extense documentation at http://www.mosquitoalert.com/en/publicaciones/ |
プロジェクトに携わる要員:
- POINT_OF_CONTACT
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.
| Study Extent | There are no limitations in terms of geographic areas from which citizen are allowed to participate, so data can be sent from all over the world. Nevertheless, Mosquito Alert’s main coverage has been in Spain, with increasing coverage in Europe since 2020, mainly in The Netherlands, Italy, and Hungary. The temporal coverage of the dataset is from June 18, 2014 to September 20, 2021. |
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| Quality Control | The Digital Entomology Network is formed by a number of experts, including the so-called National Supervisors. At each European country participating through the projects AIM-COST and VEO, the National Supervisors serve as national level coordinators and supervisors. In addition, a senior entomologist Super Expert is in charge of the coordination of the whole validation flow and mechanics in the Mosquito Alert system. A manual for the expert validation system is distributed beforehand to the members of the network and published in the Mosquito Alert website [36] with specifications on the criteria for species determination. The taxonomic determination of an observation results in two potential outputs indicating the degree of certainty: confirmed, when taxonomic features can be clearly seen in the picture/s and probable, when only some characteristic features can be observed. The final taxonomic determination and the relative degree of certainty is computed based on expert’s validations in two steps. (1) Selection of most voted category. The selection for the most voted category is a simple majority selection. For example, assume the following three expert validation assessment: "Probably Ae. albopictus | Definitely Ae. albopictus | Probably Ae. aegypti". The most voted category is Ae. albopictus with two votes. Note that in this step, the "Probably" and "Definitely" qualifications given by each expert are ignored. If there is no majority (i.e, every expert chooses a different taxonomic category) the classification result is considered a "conflict" and the report is flagged and revised by the super-expert. (2) Certainty value selection. The certainty labels of the most voted taxonomic category are mapped to integer values such that 1 corresponds to "Probably" and 2 to "Definitely". The final certainty assessment value is given by averaging the values and rounding them to the nearest integer value with a round half down strategy. For the above example, the most voted category is Ae. albopictus where two values are issued ("Probably" and "Definitely") that results in an average value of 1.5. Finally, after rounding the average to 1 the assessment gives a "Probable" Ae. albopictus occurrence. If the final result would be 2, the certainty degree of the occurrence would be labeled as "Confirmed". Note that rounding half down strategy implies a conservative approach in the certainty evaluation: if one of the expert expresses doubt, the overall value is decreased. The validation procedure allows an expert to label a report with not sure in case of pictures with insufficient information. Those records are not included in the current dataset, since only confirmed or probable mosquito records are valid occurrences. For each record, the corresponding entomologist experts who reviewed it are cited by name or by a group label (e.g. institution, team name, etc.). The Anonymous expert label is assigned to experts who wish to remain anonymous. |
Method step description:
- An anonymous citizen scientist observes an adult-mosquito (dead or alive).
- 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).
- The report is reviewed by members of the Mosquito Alert team to remove anything that appears to be personally identifying information or inappropriate content.
- 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).
- 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.
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追加のメタデータ
| 目的 | This data set can reach many entomological (vector) surveillance and management objectives. First, due to its scalability and massive networking capability it can be used as an Early Warning System (EWS) for detection of invasive species across scales, from city to continental scales. At local scales this type of data can help optimise vector control, as citizen inform about nuisance and presence of mosquitoes at almost real time. Mosquito reduction campaigns, may combine top-down strategies of mosquito (larvae) control (undertaken by public health agencies) with bottom-up strategies promoting social action and behavioural change to reduce domestic and peri-domestic breeding sites’ proliferation. Second, if combined with other data sources this data can be used to make risk assessments, like characterisation of critical areas and seasonal variability for disease risk transmission. It can also be used for data augmentation and calibration in mosquito distribution models of seasonal and inter-annual patterns as well as and spatial suitability maps. Third, the associated images contribute to train machine-learning models for image flow optimisation procedures in digital-based EWS and mosquito detection and classification. |
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| 代替識別子 | 10.15470/t5a1os |
| 1fef1ead-3d02-495e-8ff1-6aeb01123408 | |
| https://ipt.gbif.es/resource?r=mosquitoalert | |
| https://ipt.gbif.es/resource?r=mosquitoalert |