| Literature DB >> 35886800 |
Russanne D Low1, Theresa G Schwerin1, Rebecca A Boger2, Cassie Soeffing1, Peder V Nelson3, Dan Bartlett4, Prachi Ingle5, Matteo Kimura6, Andrew Clark1.
Abstract
The GLOBE Program's GLOBE Observer Mosquito Habitat Mapper is a no-cost citizen scientist data collection tool compatible with Android and iOS devices. Available in 14 languages and 126 countries, it supports mosquito vector surveillance, mitigation, and education by interested individuals and as part of participatory community surveillance programs. For low-resource communities where mosquito control services are inadequate, the Mosquito Habitat Mapper supports local health action, empowerment, and environmental justice. The tangible benefits to human health supported by the Mosquito Habitat Mapper have encouraged its wide adoption, with more than 32,000 observations submitted from 84 countries. The Mosquito Habitat Mapper surveillance and data collection tool is complemented by an open database, a map visualization interface, data processing and analysis tools, and a supporting education and outreach campaign. The mobile app tool and associated research and education assets can be rapidly deployed in the event of a pandemic or local disease outbreak, contributing to global readiness and resilience in the face of mosquito-borne disease. Here, we describe the app, the Mosquito Habitat Mapper information system, examples of Mosquito Habitat Mapper deployment in scientific research, and the outreach campaign that supports volunteer training and STEM education of students worldwide.Entities:
Keywords: citizen science; community engagement; mitigation; mobile app; mosquito; open data; smartphone; vector surveillance; vector-borne disease
Year: 2022 PMID: 35886800 PMCID: PMC9316649 DOI: 10.3390/insects13070624
Source DB: PubMed Journal: Insects ISSN: 2075-4450 Impact factor: 3.139
List of representative citizen science mosquito surveillance projects, 2012–present.
| Project | Report Method | Data Type | Life Cycle Stage | Surveillance Target | Participant Focus | Date | Geographic | Country of Origin |
|---|---|---|---|---|---|---|---|---|
| Abuzz [ | Web | Audio | Adult | Wingbeat species ID | Public | 2017–2018 | Country | USA |
| Atrapar el Tigre [ | App | Photo | Adult |
| Public | 2013–2014 | Country | Spain |
| Sem Dengue–BreakZika [ | App | Photo Text | Larva | Larva habitat symptoms | Public | 2016–2017 | Country | Brazil |
| Caza Mosquitoes [ | App | Photo | Adult | Mosquito | Public | 2017– | Country | Argentina |
| Citizen AcTS [ | In situ expert ID | Specimen | Adult |
| Field expert | 2016–2017 | Local | USA |
| Dengue Chat [ | Web | Photo | Larva | Larva habitat, disease case | Public | 2015– | International | Nicaragua, Mexico, Brazil, Paraguay Colombia |
| GO Mosquito Community Challenge [ | App | Photo | Larva | Students | 2017–2019 | Local | Brazil, Peru | |
| GLOBE Observer Mosquito Habitat Mapper [ | App | Photo | Larva | (See above) | Students | 2017– | International | USA |
| GLOBE Zika Education and Prevention Project [ | App | Photo | Larva | (See above) | Students | 2018–2021 | International | Africa, Asia, and Pacific, Latin America, Caribbean |
| Great Arizona Mosquito Hunt [ | Egg paper | Egg | Students | 2015–2017 | Regional (State: AZ) | USA | ||
| Humbug [ | App | Audio | Adult | Wingbeat species ID | Public | 2014– | International | UK |
| Kidenga [ | App | Text | Adult | Adult activity, disease cases | Public | 2016– | Regional | USA |
| iMoustique [ | App | Photo | Adult |
| Public | 2013 | Country | France |
| iNaturalist: Mosquito AI [ | App | Photo | Adult | recent invasive species | Public | 2021–2022 | Regional | USA |
| iNaturalist: Mosquitoes in HI [ | App | Photo | Adult | invasive species | Public | 2015– | Regional | USA |
| Invasive Mosquito | Egg paper | Adult |
| Students | 2016– | Country | USA, Canada | |
| Lansanka Model [ | Paper | Text | Larva | Larva habitats | Public | 2014–2015 | Local | Thailand |
| Mo-Buzz [ | App | Photo | Adult | Larva habitat, bites, symptoms | Public | 2013– | Local | Sri Lanka |
| Mosquito Alert [ | App | Photo | Adult Larva | Invasive | Public | 2014– | International | Spain |
| MOSapp/DI Sapp [ | App | Text | Adult | Vector mosquitoes disease cases | Public | 2015– | Country | India |
| Mosquito Census [ | Web | Specimen | Adult | All mosquitoes | Public | (2019) | Country | New Zealand |
| Mosquito Mapper [ | App | Photo | Adult | All mosquitoes | Public | 2017 | City (Berlin) | Germany |
| Mosquito Reporting Scheme/ | Specimen | Adult | Mosquitoes | Public | 2005–2012 | Country | UK | |
| Mosquito Stoppers [ | Web | Text | Adult | Nuisance mosquitoes | Public | 2014–2015 | City | USA |
| MosquitoWEB [ | Specimen | Adult | All species | Public | 2014– | Country | Portugal | |
| Mozzie Monitors [ | Web | Photo | Adult | Gravid trap specimens | Public | 2018–2019 | Regional | Australia |
| Mueckenatlas [ | Specimen | Adult | All species | Public | 2011– | Country | Germany | |
| Muggenradar [ | Web, mail | Specimen | Adult | Nuisance mosquitoes | Public | 2014–2015 | Country | Germany |
| North American Mosquito Project [ | Specimen | Adult | All mosquitos from trap | Public | 2011–2015 | International | USA, Canada | |
| TopaDengue [ | App | Photo | Larva | Larva habitat monitoring, | Students | 2018–2019 | Local | Paraguay |
| West Nile Virus | Specimen | Adult |
| Public | Country | Netherlands | ||
| ZanzaMapp [ | App | Text | Adult | Nuisance mosquitoes | Public | 2016–2018 | Country | Italy |
| Zika Mozzie Seeker [ | Egg paper | Eggs |
| Public | 2017– | Regional | Australia | |
| Unnamed project [ | Paper | Map | Larva | Anopheles | Public | 2012–2013 | Local | Tanzania |
| Unnamed project [ | Egg | Egg | Invasive | Public | 2017 | Regional (6 provinces) | Austria | |
| Unnamed project [ | Paper | Specimen | Adult | Nuisance mosquitoes | Public | 2017–2018 | Local | Rwanda |
Figure 1GLOBE Mosquito Habitat Mapper is a strategic active engagement component of the integrated education and outreach effort led by the NASA Earth Science Education Collaborative [56].
Figure 2Infographic used by GLOBE Mosquito Habitat Mapper science outreach team in public climate change education. Image credit: Jenn Paul Glaser and Russanne Low.
Figure 3GLOBE Observer Mobile Application, supporting scientific investigation of the Earth’s system: Clouds (atmosphere), Mosquito Habitat Mapper (hydrosphere), Land Cover (pedosphere and biosphere), and Trees (biosphere). Modified from original: The GLOBE Program.
Figure 4Screen captures from each of the four data collection steps. (a) Larval habitat documentation, (b) sample and count larvae, (c) larva identification, (d) source reduction (larval habitat mitigation). Source: The GLOBE Program.
Figure 5(a) Screenshot of the final frame of the Mosquito Habitat Mapper. (b) Example data records accessible via the GLOBE Observer mobile application interface. Source: The GLOBE Program.
Figure 6Locations of submitted Mosquito Habitat Mapper citizen scientist observations (29 May 2017–12 March 2022). (n = 32,894). Source: GLOBE Visualization System. Larger circles indicate a greater density of mosquito habitat observations in that location.
Description of GLOBE Mosquito Habitat Mapper Information System related to data quality. Categories based on data collection scenarios described by Lukyanenko et al. [77] and data quality considerations and processes discussed in Kosmala et al. [78], Lewandowski and Specht [79], and Wiggins et al. [80].
| Information System Components | Subdimension | Characteristics | Example from Mosquito Habitat Mapper |
|---|---|---|---|
| Scope and activity | Geographic scale | Large, unbounded | Anytime, anywhere in 126 GLOBE countries |
| Task type | Easy–hard | Volunteers can choose tasks they want to perform and have time to perform, making participation available to citizen scientists with a wide variety of skill levels and interests. Easy = photographing standing water source, dumping water. Hard = taxonomic identification of a specimen | |
| Data collection tasks | Specified, closed | Standardized protocol, categorical variables, collection conditions reported | |
| Public participation model | Collaborative | Volunteers encouraged to collect, analyze, interpret, and disseminate outcomes | |
| Citizen scientists | Recruitment | Inside and outside GLOBE network | GLOBE training events, social media, publicly advertised data challenges, teacher professional development workshops and webinars |
| Ability level | Experts/nonexperts in project domain | Anyone aged 13+ years can participate | |
| Training | Minimal training required | Initial: in-app tutorial, instructional video, and eTraining module | |
| Volunteer Assessment | Optional certification via eTraining test | GLOBE eTraining module and certification quiz | |
| Device | Geospatial data | Automated, contributor-centric | Geospatial data are obtained automatically in-app. Users must wait until the satellite fix returns position with acceptable accuracy, and click on the “reset” button at 30 second time intervals until accuracy of ±12 m is achieved |
| Taxonomic identification | Instance/ | (1) Mosquito taxonomic attributes (such as siphon) identified individually prior to assigning to class (taxon) | |
| Raw data | Data quality management | Contributor-centric | Robust protocols and training support citizen scientist skill development and minimize errors in data reporting |
| Data documentation | Metadata | GLOBE Data User Guide | |
| Access | Open data | GLOBE Visualization System, ADAT, API, Earth System Explorer Portal | |
| Data analysis | Significant and advanced data cleaning and post-processing required | Characteristic of non-expert data collection system | |
| Preprocessing support and quality assurance procedures | Database range and logic checks | Algorithmic identification of outliers | GLOBE Data Information System |
| Expert validation | Data review | Photo approval system, expert validation, AI (in development) currently expert-validated cases available in curated datasets | |
| Tracking volunteer performance | Origin of each record identified | Each citizen scientist is identified anonymously, enabling quality tracking | |
| Access to processing algorithms | Support for reusable and reproducible data processing steps | Jupyter Notebooks (GLOBE data) | |
| Voucher photographs | Mosquito habitats and larval specimens | Manual and AI photo approval system rejects inappropriate photos | |
| Data users | Research Topics | Known (satellite data interpretation) and unknown (evolving) | Earth system science, predictive models of vector disease, environmental justice action, operational vector control management, satellite data interpretation, computer vision research (AI), invasive species monitoring |
| Education outreach | Pedagogic assets and programs | Student research applications | GLOBE US Student Research Symposium |
Outcomes of expert validation of citizen scientist identifications of larval specimens found in containers, submitted from Benin, Kenya, Senegal, and Madagascar.
| Expert Validation of Citizen Scientist Identifications | Benin | Kenya | Senegal | Madagascar |
|---|---|---|---|---|
|
| ||||
| Not | 8 | 38 | 192 | 4 |
| 4 | 2 | 23 | 1 | |
|
| ||||
| Siphon identified as present, but is absent | 1 | 65 | ||
| Siphon described as absent, but is present | 3 | 236 | ||
| Pupa misidentified as Anopheles larva | 2 | 22 | ||
| Identified as | 2 | 93 | ||
| Specimen is misidentified as a mosquito larva | 2 | 1 | ||
| Total identified specimens from containers/total | 22/233 | 40/67 | 632/2211 | 5/6 |
| % Accuracy based on voucher photos | 55% | 100% | 34% | 100% |
| % Specimens with attempted identification | 9% | 60% | 29% | 91% |
Figure 7Estimated location accuracy of Mosquito Habitat Mapper habitat observations in 2021, automatically reported by the app as ± meters. (Filtered data, values < 100 m). The peak at ±64 m indicates user error.
Figure 8Screenshot of Citizen Science Cloud Dashboard demonstrating interoperability of mosquito data deriving from three independent citizen scientist projects: Mosquito Habitat Mapper, Mosquito Alert, and iNaturalist. Image Credit: Ryan Carney.
Figure 9Example of autogenerated team map for Northwest Mosquito Abatement District, IL, USA. Source: The GLOBE Program.
Figure 10Position of 42 gravid traps superimposed over LULC classifications derived from European Space Agency (ESA) Sentinel-2 satellite imagery (10 m resolution). Base Map: Esri 2020 Global Land Cover. Left: Mosquito habitat features (water, trees, grass) documented using the GLOBE Observer Land Cover tool, but not identified in the land cover map product. Map from Northwest Mosquito Abatement District (NWMAD), northwest suburbs of Chicago, IL, USA. Source: Dan Bartlett, NWMAD.
Figure 11Data dashboard enabling rapid evaluation of land cover features and associated larvae (beta). Source: Peder Nelson.
(a) Strategies to Implement Effective School CS Programs in Infectious Disease Surveillance, as outlined by Abourashed et al. [15]. (b) Alignment of Mosquito Habitat Mapper’s programmatic attributes to each strategy.
| Effective Strategies for School-Based Citizen Science Programs | Examples from Mosquito Habitat Mapper Programming | |
|---|---|---|
| (1) | Consider program participants: student participants are different from adults in a citizen science program. Developing programs that account for student motivation, scientific curiosity, and capabilities are crucial for a program’s success. | Student motivations: desire to conduct locally meaningful work; contribute to community health; earn recognition from other students, teachers, and community; and obtain data for science fair projects. Fulfills service learning and environmental justice project requirements in some district curricula. |
| (2) | Support current school curriculum and initiatives: citizen science projects that align well with teachers’ lesson plans and standards make implementing a citizen science project less demanding. | Mosquito Habitat Mapper educational resources are designed as activities that support educational objectives of US Next Generation Science Standards. |
| (3) | Create simple and clear protocols: students focus on following procedures. Protocols should be explained plainly and be easy to follow. In addition, data collection should be accessible. | The Mosquito Habitat Mapper app tool is easy to use and students can participate in those data collection tasks that are appropriate to their interest and skill levels. Beta tested with youth (13–17 years old). |
| (4) | Take advantage of appropriate technology: using technology that is portable, such as smartphones, can aid implementation of citizen science projects. This also supports rapid data collection. | The GLOBE Observer mobile app work on older devices and offline. Currently available in 14 languages. |
| (5) | Maintain open communities and feedback with students and teachers: students should understand their role as citizen scientists. Students and teachers need to know why they are collecting data and why they are doing so in a specific way. Discussing the impact of their work and how long the data will be available for analysis is also valuable. | The app’s |
| (6) | Promote community outreach: citizen science is a community-driven scientific initiative. Involving students and their community members enhances scientific confidence and strengthens civic cooperation. | GLOBE Observer connects with both adult and student audiences through libraries, which serve as community science hubs throughout the US, especially in rural and underserved communities. Community partners identify their own data collection and participation goals. |
| (7) | Spread knowledge gained through experience and results: publicizing citizen science projects and showing collaborations between experts and non-experts can build the public’s trust in science and combat scientific misinformation. | Coordinated social media campaign, webinars connecting citizen scientists with experts. NASA actively encourages scientific publication with citizen scientists as co-authors (including youth). |
| (a) | (b) |
Figure 122021 Mosquito Habitat Photo Challenge Dashboard. Red dots indicate locations of Mosquito Habitat Mapper observations. Blue dots indicate locations of Land Cover observations. Source: Andrew Clark, IGES.
Figure 13Scenes from the Instagram story about the 2021 Mosquito Habitat Photo Challenge. Source: GLOBE Communications Team.
The number of GLOBE International Virtual Science Symposium projects submitted by students using Mosquito Habitat Mapper data. The numbers for 2022 are preliminary and the number of total submissions was not available (n/a) at time of publication. Data source: The GLOBE Program.
| Grade Level | 2022 | 2021 | 2020 | 2019 | 2018 |
|---|---|---|---|---|---|
| Upper secondary (grades 9–12 US) | 44 | 58 | 26 | 9 | 6 |
| Lower secondary (grades 5–8 US) | 3 | 7 | 12 | 8 | 2 |
| Upper elementary (grades 3–4 US) | 0 | 1 | 5 | 1 | 0 |
| Total submissions, all topics | 220 | 242 | 265 | 238 | 113 |
| % IVSS Projects analyzing Mosquito Habitat Mapper data | 21% | 27% | 16% | 8% | 7% |
Figure 14Locations of libraries field testing Mosquito Habitat Mapper resources, including California, Florida, Illinois, Kentucky, New Jersey, North Carolina, and Texas, USA. Source: Theresa Schwerin.
Figure 15Example of a Mosquito Habitat Mapper DIY Citizen Science Kit. Source: LAPL.
Figure 16LaSalle Public Library Mosquito Mappers post “do not disturb” signs near larvae traps. The signs include the LaSalle Public Library Facebook Group for the project, providing additional outreach and awareness. Source: LaSalle Public Library.
Figure 17Mosquito Habitat Mapper Activities and Games. (a) Mosquito Habitats and Hideouts Bingo, (b) Build a Mosquito Larvae Trap, and (c) The GLOBE Mission Mosquito Larvae Hunter’s Field Guide. These activities reinforce concepts and allow practicing skills needed for taking observations, e.g., identifying different habitats where mosquitoes can breed, understanding habitat characteristics that meet mosquito needs, and practice identifying mosquito larvae from specimen photos. Source: IGES.
Figure 18Mosquito Habitat Mapper Observations by identified by month, year, and region.