Global Mosquito Observations Dashboard (GMOD): a user-friendly web interface fueled by citizen science to monitor invasive and vector mosquitoes

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Uelmen, Andrew Clark, John Palmer, Jared Kohler, Landon C. Van Dyke, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3200695/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Oct, 2023 Read the published version in International Journal of Health Geographics → Version 1 posted 7 You are reading this latest preprint version Abstract Background: Mosquitoes and the diseases they transmit pose a significant public health threat worldwide, causing more fatalities than any other animal. To effectively combat this issue, there is a need for increased public awareness and mosquito control campaigns. However, traditional surveillance programs are time-consuming, expensive, and lack scalability. Fortunately, the widespread availability of mobile phones with high-resolution cameras presents a unique opportunity for mosquito surveillance. In response to this, the Global Mosquito Observations Dashboard (GMOD) was developed as a free, public platform to improve the detection and monitoring of invasive and vector mosquitoes through citizen science participation worldwide. Methods: GMOD is an interactive web interface that collects and displays mosquito observation and habitat data submitted by citizen scientists worldwide. By providing information on the locations and times of observations, the platform enables the visualization of mosquito population trends and ranges. It also serves as an educational resource, encouraging collaboration and data sharing. The data acquired and displayed on GMOD is freely available in multiple formats and can be accessed from any device with an internet connection. Results: Since its launch less than a year ago, GMOD has already proven its value. It has successfully collected and processed large volumes of real-time data (~300,000 observations), offering valuable and actionable insights into mosquito species prevalence, abundance, and potential distributions, as well as engaging citizens in community-based surveillance programs. Conclusions: GMOD is a cloud-based platform that provides open access to mosquito vector data obtained from citizen science programs. Its user-friendly interface and data filters make it valuable for researchers, mosquito control personnel, and other stakeholders. With its expanding data resources and the potential for machine learning advancements, GMOD is poised to support public health initiatives aimed at reducing the spread of mosquito-borne diseases in a cost-effective manner, particularly in regions where traditional surveillance methods are limited. GMOD is continually evolving, with ongoing development of powerful machine learning algorithms to identify mosquito species and other features from submitted data. The future of citizen science and artificial intelligence holds great promise, and GMOD stands as an exciting initiative in this field. Anopheles stephensi citizen science data dashboard GIS malaria mosquito open source vector-borne disease surveillance Figures Figure 1 Figure 2 Figure 3 Background Mosquitoes and the diseases they transmit are a global health concern, killing nearly one million humans annually. In particular, malaria accounts for at least 430,000 of these deaths each year ( 1 – 3 ). Efforts to combat mosquitoes are becoming increasingly challenging, largely due to changes in climate, human expansion, and vast interconnected global travel and transport. Widely practiced methods for controlling mosquito-borne diseases target species of medical importance, requiring expert knowledge and a significant number of resources. Additionally, the risk of human error with manual identification is high, particularly when quantities of adult trap collections are processed en masse. Fortunately, advances in mobile technology and greater affordability have provided everyday users with highly capable and portable handheld devices for a large variety of tasks. With over 6 billion users globally, smartphones are equipped with global position systems (GPS) and high-resolution cameras ( 4 ). Combined with engaged community members and public science initiatives, smartphones are a significant force leading to the rapid rise in citizen science ( 5 ). When applied to broader mosquito control questions, information ascertained from citizen science data is cost-efficient and scalable ( 6 ), providing a wealth of knowledge that can aid in reducing the global burden of mosquito-borne diseases. Currently, there are several mobile phone applications that facilitate user-friendly citizen science participation. Collectively, vast amounts of data are available and provide an impressive assemblage, but several challenges have hindered translating this information into actionable change. Differences in interoperability, data formatting and consistency, and temporal and spatial coverages, discourage the standardization, merging, and analysis of data for sharing and use in future studies ( 7 ). With respect to submissions specific to mosquitoes and their habitats, three global applications exist: GLOBE Observer, Mosquito Alert, and iNaturalist ( 7 – 10 ). Driven by the Global Mosquito Alert Consortium ( 7 , 11 , 12 ), we seek to bolster submissions from these applications and connect citizen scientist data globally through standard protocols pertaining to mosquitoes (especially those of medical importance), their habitats, and biting occurrences. Therefore, we created the Global Mosquito Observation Dashboard (GMOD, www.mosquitodashboard.org ) , a streamlined central repository where mosquito-specific data are harmonized according to open geospatial consortium (OGC) standards and presented in near real-time. These data are integrated through an intuitive, user-friendly interface with all four datasets available in summarized and raw formats, thus serving as a tool with immediate application for planning and initiating interventions aimed at reducing the burden of disease at local, regional, and global scales. Indeed, GMOD fills the critical need for cross-platform integration of citizen science data for the detection and monitoring of invasive and vector mosquitoes across the globe – a problem of urgent concern in light of species such as Anopheles stephensi , the deadly urban malaria vector that has recently invaded the African continent (WHO 2022). Ultimately, by leveraging citizen science efforts from multiple platforms, GMOD increases access, interoperability, data sharing, and knowledge in the global fight against mosquito-borne diseases. Methods In the spirit of the 50th anniversary of Earth Day, our overarching goal was to follow the Global Earth Challenge’s call for the use of citizen science data that is findable, accessible, interoperable, and reusable (FAIR, globalearthchallenge.earthday.org ) ( 13 ). By leveraging open data created by the three citizen science programs, we created GMOD with the overall aim to increase awareness and develop consistent, real-time surveillance of mosquitoes globally with the ultimate goal of reducing the global burden of diseases. Partnering with GLOBE Observer, MosquitoAlert, and iNaturalist, our process began with the preparation, processing, and eventual integration of OCG-standard data. Data Sources GLOBE Observer – MHM & Land Cover The Global Learning and Observations to Benefit the Environment (GLOBE) is an international science and education program sponsored by NASA and supported by NSF, NOAA and the U.S. Department of State. Established in 1995, the GLOBE Program was originally implemented K-12 classrooms. In 2016, the GLOBE Observer mobile application was released, enabling citizen scientists of all ages to participate in NASA science. GLOBE Observer is available for use in 14 languages across 127 countries. Within the application are two tools of primary interest for this research: the Mosquito Habitat Mapper (MHM) and Land Cover tool. MHM, launched in 2017, supports citizen science efforts to identify, and eventually eliminate, medically important mosquitoes from Aedes ( Ae .), Anopheles ( An. ), and Culex ( Cx. ) genera. In addition to mosquito specimens, MHM also enables users to document suitable habitats (e.g. standing water, tires, etc.) along with larvae and pupal observations (presence/absence and number). Users are encouraged to identify their mosquito submissions to genus, but are not required. The application guides users through a visual key to identifying mosquito larvae based on anatomical characteristics of the distal segment of the abdomen under magnification. Characteristics evaluated include the presence, absence, and appearance of the siphon, saddle, pecten, tufts, and comb scales. Unique to MHM, users are prompted to report if they have the ability to mitigate mosquito habitats, by removing water and/or covering containers – a process called source reduction. This feature serves two beneficial purposes by 1. engaging citizen scientists to be actively involved in community-based mosquito surveillance, and 2. actively reducing the burden of mosquito-borne diseases locally. The GLOBE Land Cover tool enhances characterization of the mosquito habitat observations collected with the MHM tool by allowing users to provide ecological conditions and additional photos of the environment around the targeted habitat. The interface has a built-in compass that orient users to provide six orientated photos for each location – north, south, east, west, up, down. Mosquito Alert Launched in 2014 and available in 18 languages, Mosquito Alert is managed by the following public research institutions: the Centre for Ecological Research and Forestry Applications (CREAF, https://www.creaf.cat/ ), Pompeau Fabra University (UPF, www.upf.edu ), Catalan Institution for Research and Advanced Studies (ICREA, https://www.icrea.cat/ ), and the Blanes Center for Advanced Studies (CEAB-CSIC, http://www.ceab.csic.es/en/ ). Initially focused on controlling diseases spread by Ae. albopictus in Spain, the program has expanded to include citizen science collections targeting Ae. aegypti, Ae. japonicus, Ae. koreicus , and Culex spp. Mosquito Alert prompts users to send observations of adult mosquitoes, breeding habitats, and/or bites encountered. All adult mosquitoes submitted are validated by accompanying photographs by a team of entomologists. iNaturalist Launched in 2008, iNaturalist is a social network of citizen scientists connected via shared observations of biodiversity, extending to plants, animals, and fungi. However, insects comprise a significant portion of all submissions, amounting to ~ 25% of all observations. Of all insect submissions, mosquitoes total ~ 78,000 (as of mid-April, 2023), representing 407 species globally. iNaturalist relies on the method of identifying observations based on crowdsourcing. More specifically, observations classified as “Research Grade” maintain at least a 2/3 community member agreement for identifying species, or the next finest taxonomic level, if species level is not possible. Uniquely, iNaturalist also uses tens of millions of photos to train artificial intelligence (AI) models for identification suggestions on more than 38,000 taxa ( 14 ). Application Programming Interface, Integration, Wrangling and Transformation of Data Pipeline Microsoft Azure Data Factory and Storage Data from each of the three sources are accessed via public URLs and stored in the Microsoft Azure Cloud. Using a scheduled Azure Data Factory pipeline, raw data is accessed daily from their respective source URLs and copied into an Azure Blob storage container called “Raw Data”. GLOBE MHM and Land Cover are accessed in a standard .csv format. Mosquito Alert is accessed in a flat-object .json format. iNaturalist, which uses unicode transformation format (UTF-16LE), is accessed as a lengthy multi-tiered nested-object .json format. The complexity of the iNaturalist data was so extensive that a special request to Microsoft was made to have it modify the way its Azure Data Factory copied .json data. Further, the data wrangling tool used to clean and standardize the iNaturalist data also required modification by its creators to enable such a large .json string to be ingested into its memory. Storage standards include that all data copied into the Azure Cloud has scheduled daily backups for 12 months being in a “hot” or quick-access state and after 18 months are put in “cold” or archived storage. Trifacta - Data Standardization Within the Azure Cloud, raw data is imported into a service called Trifacta, an application designed for data wrangling of large, raw datasets. Following the Open Geospatial Consortium (OGC) standard for data formatting, each data set is cleaned and restructured to match the OGC’s SensorThings format, enabling cross source data analysis ( 15 ). Every change made to the data structure is tracked and recorded for documenting data provenance. Within Trifacta, raw data are transformed to the OGC standard with each transformation being documented in a “recipe.” Each recipe contains customized formatting of data, including text and number conversion, creation of new fields, calculations, etc. and can be found in Supplemental Information. The resulting data set from each recipe is then exported in .json format to an Azure Blob storage container called “Derived Data.” Each recipe is scheduled to run daily after the raw data has been copied into the Azure cloud via Azure Data Factory. Since each dataset follows the OGC standard, all data is stored as nested-objects in .json format. To enable ingestion to the Esri ArcGIS® Online service, datasets are run through one more recipe which presents data as a flat .json data set. End User Display Customization and Experience – Esri ArcGIS Online Applications Given Esri’s extensive GIS mapping software, spatial analytics capabilities, and online workspaces, this project’s end user visual displays are created within the ArcGIS Online application interface. After Trifacta completes its daily transformation of the raw data, Esri’s ArcGIS Online web application (via ArcGIS® Notebooks) pulls each of the four flattened OGC-.json formatted data sets (GLOBE MHM, GLOBE Land Cover, MosquitoAlert, and iNaturalist) into the ArcGIS Online application interface and stores them as feature layers. Feature layers are then incorporated into the Web Map application element. This integration step is the first instance where all four data streams are displayed simultaneously. This map serves as the spatial representation of all mosquito, habitat, and land cover submissions and is the centerpiece to the overall dashboard visualization. In addition to the feature layers, each data set can be accessed via a RESTful API endpoint provided by Esri ArcGIS Online. An example flow of the steps performed by Python scripts in ArcGIS Notebooks can be found in Supplemental Information. Pop-Ups Within the web map application element, ArcGIS Online provides a variety of options to increase the interactions, dynamics, and aesthetics of the data displayed in maps. Among these options is the pop-up configuration – a custom information box that “pops-up” on any given observation point when clicked by a user. With custom elements coded in the ArcGIS® Arcade expression language, pop-ups can display a variety of dynamic alphanumeric text and media to aid in data exploration (Arcade code examples are found in Supplemental Information). Dashboards ArcGIS Online includes ArcGIS® Dashboards, a dashboard creation tool that seamlessly allows users to build their display through a variety of build-in tools, including elements (e.g. headers, widgets, footers, graphs, data selectors, numeric indicators, etc.), layouts, and themes. Furthermore, within each tool are several additional customizable options for including media, dynamic text, external links, and other features. Experience Builder The ArcGIS Dashboards tool is an excellent method for visually representing data dynamically. However, ArcGIS® Experience Builder provides a full-suite of complementary tools that enables deeper data interaction and exploration, providing a true, custom website look and feel. The creation and success of GMOD relies on the premise of being free to everyone, intuitive, and most importantly, accessible. This extends to viewership on all platforms – desktop computer, mobile phone, and/or tablet screens. Given each of these screen types are variable in resolution and aspect ratios, separate dashboards were created for each (ArcGIS Dashboards creation tool provides options for different platforms). Experience Builder allows for all three screen types to be integrated, so that all platforms will share the same web address link. Another key feature of Experience Builder is the ability to link external websites as “buttons”. For use in GMOD, these clickable tabs provide an option to explore the raw and summarized data, view publications relevant to the dashboard, and an option for users to subscribe to our mailing list (connected through the included application ArcGIS® Survey123). Hub The final key ArcGIS Online tool that completes the GMOD creation is ArcGIS® Hub℠. Hub is a cloud platform that facilitates rapid sharing and configuration of content. When ArcGIS® Open Data is enabled for an ArcGIS Online organization, Hub can be used to share an authoritative data repository that grants users full access to a variety of data formats, derived from the stored feature layers (.csv, .kml, .shp, GeoJSON, or file geodatabase). This feature is critical in our mission to fulfill data accessibility, transparency, and equity – all essential in promoting data sharing with those interested in using it. The data links to Hub are added via customizable “buttons”, created using Experience Builder. A summary of this entire workflow is available in Fig. 1 . Results GMOD Interface and Features The end-user product of GMOD is the culmination of data collected from citizen scientists (source), translated and checked for OGC standardization (Azure), converted to recipes and wrangled for uniformity (Trifacta), then displayed through a suite of tools and applications within Esri ArcGIS Online (Figure 1). The majority of all features created in GMOD are designed for a customizable experience promoting user interaction and data exploration. Highlighting the geospatial qualities of citizen scientist observations globally, GMOD is centered around the map element, displaying thousands of points from each of the four uniquely color-coded data streams (Figure 2). Clicking on any data point will open a pop-up feature, where users will see a summary of key information. Each point will display the data source, observation date, spatial coordinates, and photo(s) (if provided). Mosquito observations will display species and common name, adult, bite, or size characteristics (iNaturalist submissions only), land cover (GLOBE Land Cover only), or habitat type (MHM submissions only, Figure 3). Outside of the map element, GMOD has a variety of different features users can explore. On the banner, users have the ability to filter submissions by country or countries (map layer will zoom to selection(s) and flash the border outline(s)), date range, mosquito genus and/or species (for iNaturalist and MosquitoAlert layers only), and by photo submission (contains or does not contain at least one photo). If users would like to explore the original source applications, the far right of the header contains a toggle drop down selector that will open the original application’s website of interest. To the right of the map element are the data display features. Each data stream contains stacked widgets that display total observations and summary information of key elements for each respective data types (e.g. number of artificial containers, number of Ae. aegypti , bites, etc.). Below the widgets are interactive bar graphs of total submissions by year for each data stream, that update based on the selection(s) of features located in the banner. The far left side of GMOD contains the legend with colors corresponding to each of the data streams, as well as a description of GMOD and guide for navigating. At the bottom of GMOD is the footer, which contains clickable features that all open new tabs to external websites or features. Users can find a variety of methods for sharing their content (URL links, social media, email, QR code) as well as a subscription button for updates and news emailed directly. On the far right of the footer are three additional buttons: studies and publications relevant to GMOD, data that users can download (raw or summary forms in CSV, KML, Shapefile, GeoJSON, or file geodatabase formats), and information about the original NSF funding source. Discussion The creation of GMOD is a tool designed as a centralized digital repository for global mosquito and mosquito habitat data. Given the enormous potential in aiding ongoing scientific endeavors, our main goal was to leverage the vast resources of citizen scientists to enhance global mosquito surveillance and ultimately control efforts. The integration of hundreds of thousands of data points from our three partners, intuitive and interactive interface, and visually-appealing aesthetics enables users to personalize their experience. This in turn, provides positive feedback that demonstrates value in their contributions to their communities, further encouraging and advocating for citizen science. Indeed, citizen scientists armed with smartphones can serve as extra sets of eyes to monitor mosquitoes, in locations and at a scale otherwise impossible via traditional mosquito trapping methods. By contributing potentially actionable data on precisely where different mosquito species are found in their community, everyday citizens can help guide local mosquito programs in multiple ways. In the short term, such efforts can help inform mosquito surveillance and control efforts, especially during an epidemic. Currently, the southern counties of Sarasota (FL) and Cameron (TX) are experiencing locally transmitted cases of malaria (6 and 1 cases, respectively) ( 16 ). In the light of these developments, GMOD has been highlighted as a public health tool for informing public awareness ( 17 ). In the longer term, such data enables researchers to construct and validate mosquito habitat and risk models ( 18 ). Third, such observations provide the ability to detect invasive species ( 19 , 20 ), with particular relevance to vector species such as An. stephensi in Africa (WHO 2022). Fourth, by the public being aware of the problem and being engaged as part of the solution, they can help limit the spread of mosquito-borne diseases by eliminating standing water that serves as mosquito breeding habitats, as well as by practicing personal protection measures (e.g., using repellant with DEET). GMOD values data accessibility and strongly encourages collaborations among the global community, reusing data, and sharing knowledge. Created by our partners and combined by GMOD, future analyses that are generated from these data can encompass any time duration (1 week or 1 year) or spatial domain (a single county or a continent). Since GMOD’s launch, several insights have been made; alterations and additions to research studies have been deduced to fill spatiotemporal “gaps” in observations, invasive species have been detected in new locations, and positive interactions with individual users continues to occur. The next phase for GMOD is perhaps the most consequential for mosquito-borne diseases – the integration of real-time mosquito identifications of each submission at the level of select species (adults) and genus (larvae). Currently in development, preliminary results using machine learning training algorithms and artificial intelligence has led to exciting results. Employing a technique called the Mask Region-based Convolutional Neural Network highlighting key morphologic features (e.g. scutum, head, proboscis, wing patterns, etc.), adult classifications of Ae. aegypti and Ae. scapularis exhibited > 90% accuracy for both ( 8 ). These models can be used on larval and adult stages of dead or alive specimens. Most recently, our models detected a suspected An. stephensi larval observation in Madagascar, which prompted targeted surveillance around the area (Carney et al, in revision). Conclusions The broader scientific community can benefit from the relatively untapped, massive resources that citizen science has to offer. Applications like GLOBE Observer, Mosquito Alert, and iNaturalist put the power in the hands of the public to help fight mosquito-borne diseases in their community. GMOD harnesses this data on a global scale, by collating and standardizing all mosquito data into an easy to use, intuitive, real-time global observation dashboard. GMOD enhances the user experience by providing numerous tools to explore the data, creating a truly unique and custom experience. Most importantly, the data is free to use, easily accessible, and updated daily. The future of citizen science is promising and most importantly, pragmatic. Overall, such programs that leverage citizen science will empower communities and encourage even more advocacy and use, yielding benefits to the public and public health. Abbreviations GMOD Global Mosquito Observations Dashboard Ae. Aedes An. Anopheles Cu Culex MBD mosquito-borne diseases GPS Global Positioning System OGC open geospatial consortium GLOBE Global Learning and Observations to Benefit the Environment MHM Mosquito Habitat Mapper API Application Programming Interface UTF unicode transformation format ESRI Environmental Systems Research Institute AI artificial intelligence Declarations Ethics approval and consent to participate The use of protected human or animal data was not used in this research. Consent for publication Not applicable. Availability of data and materials All data referenced in this manuscript are free and publicly available at www.mosquitodashboard.org. At the bottom right of the main page, click the ‘Data’ tab where a new window will open to download the raw data from each of the four platforms in numerous formats (.csv, .kml, ,shp, geojson, or file geodatabase). Individuals can also acquire this data from each of the respective partners at https://observer.globe.gov/get-data (GLOBE Observer), http://www.mosquitoalert.com/en/ (Mosquito Alert), or https://www.inaturalist.org/ (iNaturalist). Competing interests Jared Kohler is an employee of ESRI and supported this research as part of ESRI Professional Services. Funding This research was funded by the National Science Foundation under Grant No. IIS-2014547 (R.M.C., R.D.L.). The GLOBE Observer app and citizen science programming are supported through National Aeronautics and Space Administration (NASA) cooperative agreement NNX16AE28A to the Institute for Global Environmental Strategies (IGES) for the NASA Earth Science Education Collaborative (NESEC, PI: Theresa Schwerin). J.P. acknowledges funding from: (a) the European Commission, under Grants CA17108 (AIM-COST Action), 874735 (VEO), 853271 (H-MIP), and 2020/2094 (NextGenerationEU, through CSIC’s Global Health Platform, PTI Salud Global); (b) the Dutch National Research Agenda (NWA), under Grant NWA/00686468; and (c) “la Caixa” Foundation, under Grant HR19-00336. Authors' contributions JU, CM, and RC developed the tables and figures, and drafted the manuscript. ONYC and DKTT were involved in data collection and data entry. JU and RC revised the manuscript critically for important intellectual content. JU and RC conceptualized and designed the study, as well as revised the manuscript critically for important intellectual content. All authors read and approve the final manuscript. Acknowledgements We are especially grateful for all the users from around the world who have submitted data to any of the platforms used to feed GMOD. 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Parasit Vectors. 2019;12. Additional Declarations Competing interest reported. Jared Kohler is an employee of ESRI and supported this research as part of ESRI Professional Services. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 28 Oct, 2023 Read the published version in International Journal of Health Geographics → Version 1 posted Editorial decision: Major revision 18 Sep, 2023 Reviews received at journal 08 Aug, 2023 Reviewers agreed at journal 26 Jul, 2023 Reviewers invited by journal 25 Jul, 2023 Submission checks completed at journal 24 Jul, 2023 Editor assigned by journal 24 Jul, 2023 First submitted to journal 24 Jul, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Uelmen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACxgYeKIsdRFQwM0iAaB6cGpC1MIOIM0RoQciCtDC2EaGFub332OPCNobEfmbuxMeF86wTZ7Y3MD5424bHYT3n0o1nArXMbObdbDxzW3ribJ4DzIZz8WmZkWMmzQvUsuEw7zZp3m2HE+dJJLCBRAhr2X+Yd/tv3jlALfIP2H8TpWUDM+82Zt6Gw4mzJRjYmPFq6TljJs1zTsJ4xmHezdI8x4Ae60lslpxzDrcWw/YeoJYyG9n+9t6Nn3lqrGVnHD988MObMjxaGkBWsUmg2NyAWz0QyIPJP3jVjIJRMApGwUgHAPVFSzPFrs0kAAAAAElFTkSuQmCC","orcid":"","institution":"Duke University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Johnny","middleName":"A.","lastName":"Uelmen","suffix":""},{"id":221284341,"identity":"790b4884-2be0-4e9d-bcc0-c5f5d62f510b","order_by":1,"name":"Andrew Clark","email":"","orcid":"","institution":"Institute for Global Environmental Strategies","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Clark","suffix":""},{"id":221284342,"identity":"27f7f2fe-8874-4506-8853-382cf2eafe88","order_by":2,"name":"John Palmer","email":"","orcid":"","institution":"Universitat Pompeau Fabra","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Palmer","suffix":""},{"id":221284343,"identity":"47de3dac-8151-46d0-b95c-9928b258c6b1","order_by":3,"name":"Jared Kohler","email":"","orcid":"","institution":"Esri","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jared","middleName":"","lastName":"Kohler","suffix":""},{"id":221284344,"identity":"f0760593-4421-4c73-b5f3-e91183a24c5c","order_by":4,"name":"Landon C. Van Dyke","email":"","orcid":"","institution":"United States Department of State","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Landon","middleName":"C. Van","lastName":"Dyke","suffix":""},{"id":221284345,"identity":"84ea8b8f-a4bf-4626-9b7f-e345f31c4722","order_by":5,"name":"Russanne Low","email":"","orcid":"","institution":"Institute for Global Environmental Strategies","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Russanne","middleName":"","lastName":"Low","suffix":""},{"id":221284346,"identity":"b13fddd4-0175-489b-a04f-9891b65b7937","order_by":6,"name":"Connor D. Mapes","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Connor","middleName":"D.","lastName":"Mapes","suffix":""},{"id":221284347,"identity":"4105b8c9-e91b-4ba1-8635-3a00b409d7f9","order_by":7,"name":"Ryan M. Carney","email":"","orcid":"","institution":"University of South Florida (USF)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"M.","lastName":"Carney","suffix":""}],"badges":[],"createdAt":"2023-07-24 20:44:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3200695/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3200695/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12942-023-00350-7","type":"published","date":"2023-10-28T15:02:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":40723002,"identity":"0e2662c6-1755-48b3-bd54-85f7f39643f3","added_by":"auto","created_at":"2023-07-28 14:08:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131819,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of data pipeline, from source to end-user dashboard product.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3200695/v1/49d519e5e2d8c56cd73bdf67.png"},{"id":40723003,"identity":"a429b27c-af81-4f86-9552-e249615e7dd0","added_by":"auto","created_at":"2023-07-28 14:08:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":858642,"visible":true,"origin":"","legend":"\u003cp\u003eGMOD final end-user product (desktop version).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3200695/v1/ab37437269efe9efd0b5295a.png"},{"id":40723006,"identity":"5c768c38-a91d-47f4-a345-c22f27b3ca0d","added_by":"auto","created_at":"2023-07-28 14:08:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":500834,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of key information for each observation type, displayed within the configured pop-up feature.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3200695/v1/9a00fc6ca77d91c8a27905cf.png"},{"id":45454151,"identity":"d6f49b79-899b-4bfa-9a53-cae6d3d899fc","added_by":"auto","created_at":"2023-10-30 15:08:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1567120,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3200695/v1/ece4ae55-6bf1-4e91-a05c-ca1f17c3b9f0.pdf"},{"id":40723004,"identity":"54b80aee-7974-4ac9-8922-69a3df3ed969","added_by":"auto","created_at":"2023-07-28 14:08:35","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":20487,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3200695/v1/6e6458734f0160b6061c059f.docx"}],"financialInterests":"Competing interest reported. Jared Kohler is an employee of ESRI and supported this research as part of ESRI Professional Services.","formattedTitle":"Global Mosquito Observations Dashboard (GMOD): a user-friendly web interface fueled by citizen science to monitor invasive and vector mosquitoes","fulltext":[{"header":"Background","content":"\u003cp\u003eMosquitoes and the diseases they transmit are a global health concern, killing nearly one million humans annually. In particular, malaria accounts for at least 430,000 of these deaths each year (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Efforts to combat mosquitoes are becoming increasingly challenging, largely due to changes in climate, human expansion, and vast interconnected global travel and transport. Widely practiced methods for controlling mosquito-borne diseases target species of medical importance, requiring expert knowledge and a significant number of resources. Additionally, the risk of human error with manual identification is high, particularly when quantities of adult trap collections are processed en masse.\u003c/p\u003e \u003cp\u003eFortunately, advances in mobile technology and greater affordability have provided everyday users with highly capable and portable handheld devices for a large variety of tasks. With over 6\u0026nbsp;billion users globally, smartphones are equipped with global position systems (GPS) and high-resolution cameras (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Combined with engaged community members and public science initiatives, smartphones are a significant force leading to the rapid rise in citizen science (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). When applied to broader mosquito control questions, information ascertained from citizen science data is cost-efficient and scalable (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), providing a wealth of knowledge that can aid in reducing the global burden of mosquito-borne diseases.\u003c/p\u003e \u003cp\u003eCurrently, there are several mobile phone applications that facilitate user-friendly citizen science participation. Collectively, vast amounts of data are available and provide an impressive assemblage, but several challenges have hindered translating this information into actionable change. Differences in interoperability, data formatting and consistency, and temporal and spatial coverages, discourage the standardization, merging, and analysis of data for sharing and use in future studies (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). With respect to submissions specific to mosquitoes and their habitats, three global applications exist: GLOBE Observer, Mosquito Alert, and iNaturalist (\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Driven by the Global Mosquito Alert Consortium (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), we seek to bolster submissions from these applications and connect citizen scientist data globally through standard protocols pertaining to mosquitoes (especially those of medical importance), their habitats, and biting occurrences.\u003c/p\u003e \u003cp\u003eTherefore, we created the Global Mosquito Observation Dashboard (GMOD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.mosquitodashboard.org\" target=\"_blank\"\u003ewww.mosquitodashboard.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.mosquitodashboard.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, a streamlined central repository where mosquito-specific data are harmonized according to open geospatial consortium (OGC) standards and presented in near real-time. These data are integrated through an intuitive, user-friendly interface with all four datasets available in summarized and raw formats, thus serving as a tool with immediate application for planning and initiating interventions aimed at reducing the burden of disease at local, regional, and global scales. Indeed, GMOD fills the critical need for cross-platform integration of citizen science data for the detection and monitoring of invasive and vector mosquitoes across the globe \u0026ndash; a problem of urgent concern in light of species such as \u003cem\u003eAnopheles stephensi\u003c/em\u003e, the deadly urban malaria vector that has recently invaded the African continent (WHO 2022). Ultimately, by leveraging citizen science efforts from multiple platforms, GMOD increases access, interoperability, data sharing, and knowledge in the global fight against mosquito-borne diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn the spirit of the 50th anniversary of Earth Day, our overarching goal was to follow the Global Earth Challenge\u0026rsquo;s call for the use of citizen science data that is findable, accessible, interoperable, and reusable (FAIR, \u003cem\u003eglobalearthchallenge.earthday.org\u003c/em\u003e) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). By leveraging open data created by the three citizen science programs, we created GMOD with the overall aim to increase awareness and develop consistent, real-time surveillance of mosquitoes globally with the ultimate goal of reducing the global burden of diseases. Partnering with GLOBE Observer, MosquitoAlert, and iNaturalist, our process began with the preparation, processing, and eventual integration of OCG-standard data.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eGLOBE Observer \u0026ndash; MHM \u0026amp; Land Cover\u003c/h2\u003e \u003cp\u003eThe Global Learning and Observations to Benefit the Environment (GLOBE) is an international science and education program sponsored by NASA and supported by NSF, NOAA and the U.S. Department of State. Established in 1995, the GLOBE Program was originally implemented K-12 classrooms. In 2016, the GLOBE Observer mobile application was released, enabling citizen scientists of all ages to participate in NASA science. GLOBE Observer is available for use in 14 languages across 127 countries. Within the application are two tools of primary interest for this research: the Mosquito Habitat Mapper (MHM) and Land Cover tool. MHM, launched in 2017, supports citizen science efforts to identify, and eventually eliminate, medically important mosquitoes from \u003cem\u003eAedes\u003c/em\u003e (\u003cem\u003eAe\u003c/em\u003e.), \u003cem\u003eAnopheles\u003c/em\u003e (\u003cem\u003eAn.\u003c/em\u003e), \u003cem\u003eand Culex\u003c/em\u003e (\u003cem\u003eCx.\u003c/em\u003e) genera. In addition to mosquito specimens, MHM also enables users to document suitable habitats (e.g. standing water, tires, etc.) along with larvae and pupal observations (presence/absence and number). Users are encouraged to identify their mosquito submissions to genus, but are not required. The application guides users through a visual key to identifying mosquito larvae based on anatomical characteristics of the distal segment of the abdomen under magnification. Characteristics evaluated include the presence, absence, and appearance of the siphon, saddle, pecten, tufts, and comb scales.\u003c/p\u003e \u003cp\u003eUnique to MHM, users are prompted to report if they have the ability to mitigate mosquito habitats, by removing water and/or covering containers \u0026ndash; a process called source reduction. This feature serves two beneficial purposes by 1. engaging citizen scientists to be actively involved in community-based mosquito surveillance, and 2. actively reducing the burden of mosquito-borne diseases locally. The GLOBE Land Cover tool enhances characterization of the mosquito habitat observations collected with the MHM tool by allowing users to provide ecological conditions and additional photos of the environment around the targeted habitat. The interface has a built-in compass that orient users to provide six orientated photos for each location \u0026ndash; north, south, east, west, up, down.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMosquito Alert\u003c/h2\u003e \u003cp\u003eLaunched in 2014 and available in 18 languages, Mosquito Alert is managed by the following public research institutions: the Centre for Ecological Research and Forestry Applications (CREAF, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.creaf.cat/\u003c/span\u003e\u003cspan address=\"https://www.creaf.cat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Pompeau Fabra University (UPF, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.mosquitodashboard.org\" target=\"_blank\"\u003ewww.upf.edu\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.upf.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Catalan Institution for Research and Advanced Studies (ICREA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.icrea.cat/\u003c/span\u003e\u003cspan address=\"https://www.icrea.cat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the Blanes Center for Advanced Studies (CEAB-CSIC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ceab.csic.es/en/\u003c/span\u003e\u003cspan address=\"http://www.ceab.csic.es/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Initially focused on controlling diseases spread by \u003cem\u003eAe. albopictus\u003c/em\u003e in Spain, the program has expanded to include citizen science collections targeting \u003cem\u003eAe. aegypti, Ae. japonicus, Ae. koreicus\u003c/em\u003e, and \u003cem\u003eCulex\u003c/em\u003e spp. Mosquito Alert prompts users to send observations of adult mosquitoes, breeding habitats, and/or bites encountered. All adult mosquitoes submitted are validated by accompanying photographs by a team of entomologists.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eiNaturalist\u003c/h2\u003e \u003cp\u003eLaunched in 2008, iNaturalist is a social network of citizen scientists connected via shared observations of biodiversity, extending to plants, animals, and fungi. However, insects comprise a significant portion of all submissions, amounting to ~\u0026thinsp;25% of all observations. Of all insect submissions, mosquitoes total\u0026thinsp;~\u0026thinsp;78,000 (as of mid-April, 2023), representing 407 species globally. iNaturalist relies on the method of identifying observations based on crowdsourcing. More specifically, observations classified as \u0026ldquo;Research Grade\u0026rdquo; maintain at least a 2/3 community member agreement for identifying species, or the next finest taxonomic level, if species level is not possible. Uniquely, iNaturalist also uses tens of millions of photos to train artificial intelligence (AI) models for identification suggestions on more than 38,000 taxa (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eApplication Programming Interface, Integration, Wrangling and Transformation of Data Pipeline\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eMicrosoft Azure Data Factory and Storage\u003c/h2\u003e \u003cp\u003eData from each of the three sources are accessed via public URLs and stored in the Microsoft Azure Cloud. Using a scheduled Azure Data Factory pipeline, raw data is accessed daily from their respective source URLs and copied into an Azure Blob storage container called \u0026ldquo;Raw Data\u0026rdquo;. GLOBE MHM and Land Cover are accessed in a standard .csv format. Mosquito Alert is accessed in a flat-object .json format. iNaturalist, which uses unicode transformation format (UTF-16LE), is accessed as a lengthy multi-tiered nested-object .json format. The complexity of the iNaturalist data was so extensive that a special request to Microsoft was made to have it modify the way its Azure Data Factory copied .json data. Further, the data wrangling tool used to clean and standardize the iNaturalist data also required modification by its creators to enable such a large .json string to be ingested into its memory. Storage standards include that all data copied into the Azure Cloud has scheduled daily backups for 12 months being in a \u0026ldquo;hot\u0026rdquo; or quick-access state and after 18 months are put in \u0026ldquo;cold\u0026rdquo; or archived storage.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTrifacta - Data Standardization\u003c/h2\u003e \u003cp\u003eWithin the Azure Cloud, raw data is imported into a service called Trifacta, an application designed for data wrangling of large, raw datasets. Following the Open Geospatial Consortium (OGC) standard for data formatting, each data set is cleaned and restructured to match the OGC\u0026rsquo;s SensorThings format, enabling cross source data analysis (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Every change made to the data structure is tracked and recorded for documenting data provenance. Within Trifacta, raw data are transformed to the OGC standard with each transformation being documented in a \u0026ldquo;recipe.\u0026rdquo; Each recipe contains customized formatting of data, including text and number conversion, creation of new fields, calculations, etc. and can be found in Supplemental Information. The resulting data set from each recipe is then exported in .json format to an Azure Blob storage container called \u0026ldquo;Derived Data.\u0026rdquo; Each recipe is scheduled to run daily after the raw data has been copied into the Azure cloud via Azure Data Factory. Since each dataset follows the OGC standard, all data is stored as nested-objects in .json format. To enable ingestion to the Esri ArcGIS\u0026reg; Online service, datasets are run through one more recipe which presents data as a flat .json data set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEnd User Display Customization and Experience \u0026ndash; Esri ArcGIS Online Applications\u003c/h2\u003e \u003cp\u003eGiven Esri\u0026rsquo;s extensive GIS mapping software, spatial analytics capabilities, and online workspaces, this project\u0026rsquo;s end user visual displays are created within the ArcGIS Online application interface. After Trifacta completes its daily transformation of the raw data, Esri\u0026rsquo;s ArcGIS Online web application (via ArcGIS\u0026reg; Notebooks) pulls each of the four flattened OGC-.json formatted data sets (GLOBE MHM, GLOBE Land Cover, MosquitoAlert, and iNaturalist) into the ArcGIS Online application interface and stores them as feature layers. Feature layers are then incorporated into the Web Map application element. This integration step is the first instance where all four data streams are displayed simultaneously. This map serves as the spatial representation of all mosquito, habitat, and land cover submissions and is the centerpiece to the overall dashboard visualization. In addition to the feature layers, each data set can be accessed via a RESTful API endpoint provided by Esri ArcGIS Online. An example flow of the steps performed by Python scripts in ArcGIS Notebooks can be found in Supplemental Information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePop-Ups\u003c/h2\u003e \u003cp\u003eWithin the web map application element, ArcGIS Online provides a variety of options to increase the interactions, dynamics, and aesthetics of the data displayed in maps. Among these options is the pop-up configuration \u0026ndash; a custom information box that \u0026ldquo;pops-up\u0026rdquo; on any given observation point when clicked by a user. With custom elements coded in the ArcGIS\u0026reg; Arcade expression language, pop-ups can display a variety of dynamic alphanumeric text and media to aid in data exploration (Arcade code examples are found in Supplemental Information).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDashboards\u003c/h2\u003e \u003cp\u003eArcGIS Online includes ArcGIS\u0026reg; Dashboards, a dashboard creation tool that seamlessly allows users to build their display through a variety of build-in tools, including elements (e.g. headers, widgets, footers, graphs, data selectors, numeric indicators, etc.), layouts, and themes. Furthermore, within each tool are several additional customizable options for including media, dynamic text, external links, and other features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExperience Builder\u003c/h2\u003e \u003cp\u003eThe ArcGIS Dashboards tool is an excellent method for visually representing data dynamically. However, ArcGIS\u0026reg; Experience Builder provides a full-suite of complementary tools that enables deeper data interaction and exploration, providing a true, custom website look and feel. The creation and success of GMOD relies on the premise of being free to everyone, intuitive, and most importantly, accessible. This extends to viewership on all platforms \u0026ndash; desktop computer, mobile phone, and/or tablet screens. Given each of these screen types are variable in resolution and aspect ratios, separate dashboards were created for each (ArcGIS Dashboards creation tool provides options for different platforms). Experience Builder allows for all three screen types to be integrated, so that all platforms will share the same web address link. Another key feature of Experience Builder is the ability to link external websites as \u0026ldquo;buttons\u0026rdquo;. For use in GMOD, these clickable tabs provide an option to explore the raw and summarized data, view publications relevant to the dashboard, and an option for users to subscribe to our mailing list (connected through the included application ArcGIS\u0026reg; Survey123).\u003c/p\u003e \u003cp\u003e \u003cem\u003eHub\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe final key ArcGIS Online tool that completes the GMOD creation is ArcGIS\u0026reg; Hub℠. Hub is a cloud platform that facilitates rapid sharing and configuration of content. When ArcGIS\u0026reg; Open Data is enabled for an ArcGIS Online organization, Hub can be used to share an authoritative data repository that grants users full access to a variety of data formats, derived from the stored feature layers (.csv, .kml, .shp, GeoJSON, or file geodatabase). This feature is critical in our mission to fulfill data accessibility, transparency, and equity \u0026ndash; all essential in promoting data sharing with those interested in using it. The data links to Hub are added via customizable \u0026ldquo;buttons\u0026rdquo;, created using Experience Builder. A summary of this entire workflow is available in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch4\u003eGMOD Interface and Features\u003c/h4\u003e\n\u003cp\u003eThe end-user product of GMOD is the culmination of data collected from citizen scientists (source), translated and checked for OGC standardization (Azure), converted to recipes and wrangled for uniformity (Trifacta), then displayed through a suite of tools and applications \u0026nbsp; within Esri ArcGIS Online (Figure 1). The majority of all features created in GMOD are designed for a customizable experience promoting user interaction and data exploration.\u0026nbsp;\u003cbr\u003eHighlighting the geospatial qualities of citizen scientist observations globally, GMOD is centered around the map element, displaying thousands of points from each of the four uniquely color-coded data streams (Figure 2). Clicking on any data point will open a pop-up feature, where users will see a summary of key information. Each point will display the data source, observation date, spatial coordinates, and photo(s) (if provided). Mosquito observations will display species and common name, adult, bite, or size characteristics (iNaturalist submissions only), land cover (GLOBE Land Cover only), or habitat type (MHM submissions only, Figure 3). Outside of the map element, GMOD has a variety of different features users can explore. On the banner, users have the ability to filter submissions by country or countries (map layer will zoom to selection(s) and flash the border outline(s)), date range, mosquito genus and/or species (for iNaturalist and MosquitoAlert layers only), and by photo submission (contains or does not contain at least one photo). If users would like to explore the original source applications, the far right of the header contains a toggle drop down selector that will open the original application\u0026rsquo;s website of interest. To the right of the map element are the data display features. Each data stream contains stacked widgets that display total observations and summary information of key elements for each respective data types (e.g. number of artificial containers, number of \u003cem\u003eAe. aegypti\u003c/em\u003e, bites, etc.). Below the widgets are interactive bar graphs of total submissions by year for each data stream, that update based on the selection(s) of features located in the banner. The far left side of GMOD contains the legend with colors corresponding to each of the data streams, as well as a description of GMOD and guide for navigating. At the bottom of GMOD is the footer, which contains clickable features that all open new tabs to external websites or features. Users can find a variety of methods for sharing their content (URL links, social media, email, QR code) as well as a subscription button for updates and news emailed directly. On the far right of the footer are three additional buttons: studies and publications relevant to GMOD, data that users can download (raw or summary forms in CSV, KML, Shapefile, GeoJSON, or file geodatabase formats), and information about the original NSF funding source.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe creation of GMOD is a tool designed as a centralized digital repository for global mosquito and mosquito habitat data. Given the enormous potential in aiding ongoing scientific endeavors, our main goal was to leverage the vast resources of citizen scientists to enhance global mosquito surveillance and ultimately control efforts. The integration of hundreds of thousands of data points from our three partners, intuitive and interactive interface, and visually-appealing aesthetics enables users to personalize their experience. This in turn, provides positive feedback that demonstrates value in their contributions to their communities, further encouraging and advocating for citizen science. Indeed, citizen scientists armed with smartphones can serve as extra sets of eyes to monitor mosquitoes, in locations and at a scale otherwise impossible via traditional mosquito trapping methods.\u003c/p\u003e \u003cp\u003eBy contributing potentially actionable data on precisely where different mosquito species are found in their community, everyday citizens can help guide local mosquito programs in multiple ways. In the short term, such efforts can help inform mosquito surveillance and control efforts, especially during an epidemic. Currently, the southern counties of Sarasota (FL) and Cameron (TX) are experiencing locally transmitted cases of malaria (6 and 1 cases, respectively) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In the light of these developments, GMOD has been highlighted as a public health tool for informing public awareness (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In the longer term, such data enables researchers to construct and validate mosquito habitat and risk models (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Third, such observations provide the ability to detect invasive species (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), with particular relevance to vector species such as \u003cem\u003eAn. stephensi\u003c/em\u003e in Africa (WHO 2022). Fourth, by the public being aware of the problem and being engaged as part of the solution, they can help limit the spread of mosquito-borne diseases by eliminating standing water that serves as mosquito breeding habitats, as well as by practicing personal protection measures (e.g., using repellant with DEET).\u003c/p\u003e \u003cp\u003eGMOD values data accessibility and strongly encourages collaborations among the global community, reusing data, and sharing knowledge. Created by our partners and combined by GMOD, future analyses that are generated from these data can encompass any time duration (1 week or 1 year) or spatial domain (a single county or a continent). Since GMOD\u0026rsquo;s launch, several insights have been made; alterations and additions to research studies have been deduced to fill spatiotemporal \u0026ldquo;gaps\u0026rdquo; in observations, invasive species have been detected in new locations, and positive interactions with individual users continues to occur.\u003c/p\u003e \u003cp\u003eThe next phase for GMOD is perhaps the most consequential for mosquito-borne diseases \u0026ndash; the integration of real-time mosquito identifications of each submission at the level of select species (adults) and genus (larvae). Currently in development, preliminary results using machine learning training algorithms and artificial intelligence has led to exciting results. Employing a technique called the Mask Region-based Convolutional Neural Network highlighting key morphologic features (e.g. scutum, head, proboscis, wing patterns, etc.), adult classifications of \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eAe. scapularis\u003c/em\u003e exhibited\u0026thinsp;\u0026gt;\u0026thinsp;90% accuracy for both (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These models can be used on larval and adult stages of dead or alive specimens. Most recently, our models detected a suspected \u003cem\u003eAn. stephensi\u003c/em\u003e larval observation in Madagascar, which prompted targeted surveillance around the area (Carney et al, in revision).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe broader scientific community can benefit from the relatively untapped, massive resources that citizen science has to offer. Applications like GLOBE Observer, Mosquito Alert, and iNaturalist put the power in the hands of the public to help fight mosquito-borne diseases in their community. GMOD harnesses this data on a global scale, by collating and standardizing all mosquito data into an easy to use, intuitive, real-time global observation dashboard. GMOD enhances the user experience by providing numerous tools to explore the data, creating a truly unique and custom experience. Most importantly, the data is free to use, easily accessible, and updated daily. The future of citizen science is promising and most importantly, pragmatic. Overall, such programs that leverage citizen science will empower communities and encourage even more advocacy and use, yielding benefits to the public and public health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGMOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Mosquito Observations Dashboard\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eAe.\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eAedes\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eAn.\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eAnopheles\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCu\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eCulex\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emosquito-borne diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Positioning System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOGC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eopen geospatial consortium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLOBE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Learning and Observations to Benefit the Environment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMHM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMosquito Habitat Mapper\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eApplication Programming Interface\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eunicode transformation format\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnvironmental Systems Research Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eartificial intelligence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe use of protected human or animal data was not used in this research.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll data referenced in this manuscript are free and publicly available at www.mosquitodashboard.org. At the bottom right of the main page, click the \u0026lsquo;Data\u0026rsquo; tab where a new window will open to download the raw data from each of the four platforms in numerous formats (.csv, .kml, ,shp, geojson, or file geodatabase). Individuals can also acquire this data from each of the respective partners at https://observer.globe.gov/get-data (GLOBE Observer), http://www.mosquitoalert.com/en/ (Mosquito Alert), or https://www.inaturalist.org/ (iNaturalist).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eJared Kohler is an employee of ESRI and supported this research as part of ESRI Professional Services.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Science Foundation under Grant No. IIS-2014547 (R.M.C., R.D.L.). The GLOBE Observer app and citizen science programming are supported through National Aeronautics and Space Administration (NASA) cooperative agreement NNX16AE28A to the Institute for Global Environmental Strategies (IGES) for the NASA Earth Science Education Collaborative (NESEC, PI: Theresa Schwerin). J.P. acknowledges funding from: (a) the European Commission, under Grants CA17108 (AIM-COST Action), 874735 (VEO), 853271 (H-MIP), and 2020/2094 (NextGenerationEU, through CSIC\u0026rsquo;s Global Health Platform, PTI Salud Global); (b) the Dutch National Research Agenda (NWA), under Grant NWA/00686468; and (c) \u0026ldquo;la Caixa\u0026rdquo; Foundation, under Grant HR19-00336.\u003c/p\u003e\n\u003ch3\u003e\u003cu\u003eAuthors\u0026apos; contributions\u003c/u\u003e\u003c/h3\u003e\n\u003cp\u003eJU, CM, and RC developed the tables and figures, and drafted the manuscript. ONYC and DKTT were involved in data collection and data entry. JU and RC revised the manuscript critically for important intellectual content. JU and RC conceptualized and designed the study, as well as revised the manuscript critically for important intellectual content. All authors read and approve the final manuscript.\u003c/p\u003e\n\u003ch3\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/h3\u003e\n\u003cp\u003eWe are especially grateful for all the users from around the world who have submitted data to any of the platforms used to feed GMOD. The future of global mosquito surveillance is reliant upon the frequent, high-quality submissions of curious and driven citizen scientists.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcCarthy N. The World\u0026rsquo;s Deadliest Animals [Internet]. statista. 2014 [cited 2023 Apr 13]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.statista.com/chart/2203/the-worlds-deadliest-animals/\u003c/span\u003e\u003cspan address=\"https://www.statista.com/chart/2203/the-worlds-deadliest-animals/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. World malaria report 2021. Geneva: World Health Organization. ; 2021. Licence: CC BY-NC-SA 3.0 IGO. World Health Organization. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organisation. Dengue and severe dengue 2022. WHO Fact Sheet. 2020;117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandit JA, Radin JM, Quer G, Topol EJ. Smartphone apps in the COVID-19 pandemic. Nat Biotechnol. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates M. Tracking Disease: Digital Epidemiology Offers New Promise in Predicting Outbreaks. IEEE Pulse. 2017;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmer JRB, Oltra A, Collantes F, Delgado JA, Lucientes J, Delacour S et al. Citizen science provides a reliable and scalable tool to track disease-carrying mosquitoes. Nat Commun. 2017;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTyson E, Bowser A, Palmer J, Kapan D, Bartumeus F, Pauwels E et al. Global Mosquito Alert Consortium: Report from Mosquito Task \u0026amp; Finish Group UN Sponsored Workshop, Geneva 2017 [Internet]. Geneva, Switzerland; 2018 Apr. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wilsoncenter.org/sites/default/files/media/documents/publication/global_mosquito_alert_2018.pdf\u003c/span\u003e\u003cspan address=\"https://www.wilsoncenter.org/sites/default/files/media/documents/publication/global_mosquito_alert_2018.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarney RM, Mapes C, Low RD, Long A, Bowser A, Durieux D et al. Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes. Insects. 2022;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmos HM, Starke MJ, Rogerson TM, Col\u0026oacute;n Robles M, Andersen T, Boger R et al. GLOBE Observer Data: 2016\u0026ndash;2019. Earth and Space Science. 2020;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSousa LB, Webb C, Craig A, Williams C, Chitkara U, Baldock K et al. Methodological Diversity in Citizen Science Mosquito Surveillance: A Scoping Review. Citiz Sci. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmer JRB, Brocklehurst M, Tyson E, Bowser A, Pauwels E, Bartumeus F. Global mosquito alert. Citizen Science. UCL Press; 2018. pp. 210\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong A, Low RD, Zohdy S, Palmer JRB, Elias P, Bartumeus F et al. Citizen science as an approach for responding to the threat of Anopheles stephensi in Africa. Under Peer Review. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkinson MD, Dumontier M, Aalbersberg IjJ, Appleton G, Axton M, Baak A et al. Comment: The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeary P. New Computer Vision Model [Internet]. iNaturalist Blog. 2021 [cited 2023 Jun 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.inaturalist.org/blog/54236-new-computer-vision-model\u003c/span\u003e\u003cspan address=\"https://www.inaturalist.org/blog/54236-new-computer-vision-model\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIngle P, Kimura M, Bowser A, Fisk J, Long A, Low R, et al. Aligning GLOBE Observer Mosquito Habitat Mapper and Land Cover Citizen Science Datasets to Open Geospatial Consortium Standards. AGU Fall Meeting Abstracts; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal Health Division of Parasitic Diseases and Malaria. Locally Acquired Cases of Malaria in Florida and Texas [Internet]. 2023 Jul. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/malaria/new_info/2023/malaria_florida.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/malaria/new_info/2023/malaria_florida.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelamarter C. Mosquito watch: USF researchers urge use of global dashboard in light of recent malaria cases [Internet]. 2023 Jun. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.usf.edu/news/2023/mosquito-watch-usf-researchers-urge-use-of-global-dashboard-to-track-potential-cases-of-malaria.aspx\u003c/span\u003e\u003cspan address=\"https://www.usf.edu/news/2023/mosquito-watch-usf-researchers-urge-use-of-global-dashboard-to-track-potential-cases-of-malaria.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUelmen JA Jr, Mapes CD, Prasauskas A, Boohene C, Burns L, Stuck J et al. A Habitat Model for Disease Vector Aedes aegypti in the Tampa Bay Area, Florida. J Am Mosq Control Assoc [Internet]. 2023;39:96\u0026ndash;107. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2987/22-7109\u003c/span\u003e\u003cspan address=\"10.2987/22-7109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEritja R, Delacour-Estrella S, Ruiz-Arrondo I, Gonz\u0026aacute;lez MA, Barcel\u0026oacute; C, Garc\u0026iacute;a-P\u0026eacute;rez AL et al. At the tip of an iceberg: citizen science and active surveillance collaborating to broaden the known distribution of Aedes japonicus in Spain. Parasit Vectors. 2021;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEritja R, Ruiz-Arrondo I, Delacour-Estrella S, Schaffner F, \u0026Aacute;lvarez-Chachero J, Bengoa M et al. First detection of Aedes japonicus in Spain: An unexpected finding triggered by citizen science. Parasit Vectors. 2019;12.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anopheles stephensi, citizen science, data, dashboard, GIS, malaria, mosquito, open source, vector-borne disease, surveillance","lastPublishedDoi":"10.21203/rs.3.rs-3200695/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3200695/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMosquitoes and the diseases they transmit pose a significant public health threat worldwide, causing more fatalities than any other animal. To effectively combat this issue, there is a need for increased public awareness and mosquito control campaigns. However, traditional surveillance programs are time-consuming, expensive, and lack scalability. Fortunately, the widespread availability of mobile phones with high-resolution cameras presents a unique opportunity for mosquito surveillance. In response to this, the Global Mosquito Observations Dashboard (GMOD) was developed as a free, public platform to improve the detection and monitoring of invasive and vector mosquitoes through citizen science participation worldwide.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e GMOD is an interactive web interface that collects and displays mosquito observation and habitat data submitted by citizen scientists worldwide. By providing information on the locations and times of observations, the platform enables the visualization of mosquito population trends and ranges. It also serves as an educational resource, encouraging collaboration and data sharing. The data acquired and displayed on GMOD is freely available in multiple formats and can be accessed from any device with an internet connection.\u003c/p\u003e\n\u003cp\u003eResults: Since its launch less than a year ago, GMOD has already proven its value. It has successfully collected and processed large volumes of real-time data (~300,000 observations), offering valuable and actionable insights into mosquito species prevalence, abundance, and potential distributions, as well as engaging citizens in community-based surveillance programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e GMOD is a cloud-based platform that provides open access to mosquito vector data obtained from citizen science programs. Its user-friendly interface and data filters make it valuable for researchers, mosquito control personnel, and other stakeholders. With its expanding data resources and the potential for machine learning advancements, GMOD is poised to support public health initiatives aimed at reducing the spread of mosquito-borne diseases in a cost-effective manner, particularly in regions where traditional surveillance methods are limited. GMOD is continually evolving, with ongoing development of powerful machine learning algorithms to identify mosquito species and other features from submitted data. The future of citizen science and artificial intelligence holds great promise, and GMOD stands as an exciting initiative in this field.\u003c/p\u003e","manuscriptTitle":"Global Mosquito Observations Dashboard (GMOD): a user-friendly web interface fueled by citizen science to monitor invasive and vector mosquitoes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-28 14:08:30","doi":"10.21203/rs.3.rs-3200695/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-09-18T06:44:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-08-08T16:30:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e10acd5d-ced8-4b5e-95f9-0157e49dd77e","date":"2023-07-26T05:34:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-25T07:24:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-07-25T03:36:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-25T03:36:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Health Geographics","date":"2023-07-24T20:28:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4109bc13-5431-44c6-a649-762a83bbb567","owner":[],"postedDate":"July 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[{"value":"featured","date":"2023-07-28 18:18:35"}],"updatedAt":"2023-10-30T15:07:22+00:00","versionOfRecord":{"articleIdentity":"rs-3200695","link":"https://doi.org/10.1186/s12942-023-00350-7","journal":{"identity":"international-journal-of-health-geographics","isVorOnly":false,"title":"International Journal of Health Geographics"},"publishedOn":"2023-10-28 15:02:15","publishedOnDateReadable":"October 28th, 2023"},"versionCreatedAt":"2023-07-28 14:08:30","video":"","vorDoi":"10.1186/s12942-023-00350-7","vorDoiUrl":"https://doi.org/10.1186/s12942-023-00350-7","workflowStages":[]},"version":"v1","identity":"rs-3200695","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3200695","identity":"rs-3200695","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}