Mosquito-borne diseases continue to ravage humankind with >700 million infections and nearly one million deaths every year. Yet only a small percentage of the >3500 mosquito species transmit diseases, necessitating both extensive surveillance and precise identification. Unfortunately, such efforts are costly, time-consuming, and require entomological expertise. As envisioned by the Global Mosquito Alert Consortium, citizen science can provide a scalable solution. However, disparate data standards across existing platforms have thus far precluded truly global integration. Here, utilizing Open Geospatial Consortium standards, we harmonized four data streams from three established mobile apps—Mosquito Alert, iNaturalist, and GLOBE Observer’s Mosquito Habitat Mapper and Land Cover—to facilitate interoperability and utility for researchers, mosquito control personnel, and policymakers. We also launched coordinated media campaigns that generated unprecedented numbers and types of observations, including successfully capturing the first images of targeted invasive and vector species. Additionally, we leveraged pooled image data to develop a toolset of artificial intelligence algorithms for future deployment in taxonomic and anatomical identification. Ultimately, by harnessing the combined powers of citizen science and artificial intelligence, we establish a next-generation surveillance framework to serve as a united front to combat the ongoing threat of mosquito-borne diseases worldwide.

Large wildfires (>125 hectares) in the United States account for over 95% of the burned area each year. Predicting large wildfires is imperative; however, current wildfire predictive models are region-based and computationally intensive. Using a scalable model based on easily available environmental and atmospheric data, this research aims to accurately predict whether large wildfires will develop across the United States. The data used in this study include 2109 wildfires over 20 years, representing 14 million hectares burned. Remote sensing environmental data (Normalized Difference Vegetation Index—NDVI; Enhanced Vegetation Index—EVI; Leaf Area Index—LAI; Fraction of Photosynthetically Active Radiation—FPAR; Land Surface Temperature during the Day—LST Day; and Land Surface Temperature during the Night—LST Night) consisting of 1.3 billion satellite observations was used. Atmospheric reanalysis data (u component of wind, v component of wind, relative humidity, temperature, and geopotential) at four pressure levels (300, 500, 700, and 850 Ha) were also factored in. Six machine learning classification models (Logistic Regression, Decision Tree, Random Forest, eXtreme Gradient Boosting, K-Nearest Neighbors, and Support Vector Machine) were created and tested on the resulting dataset to determine their accuracy in predicting large wildfires. Model validation tests and variable importance analysis were performed. The eXtreme Gradient Boosting (XGBoost) classification model performed best in predicting large wildfires, with 90.44% accuracy, a true positive rate of 0.92, and a true negative rate of 0.88. Furthermore, towards environmental justice, an analysis was performed to identify disadvantaged communities that are also vulnerable to wildfires. This model can be used by wildfire safety organizations to predict large wildfires with high accuracy and prioritize resource allocation to employ protective safeguards for impacted socioeconomically disadvantaged communities.

The GLOBE Program's GLOBE Observer application is a free citizen science mobile data collection and visualization tool compatible with iOS and Android operating systems. Citizen scientists armed with the app can report the mosquito larval habitats they identify using the GLOBE Mosquito Habitat Mapper tool. This data can complement the climate, weather, and land cover data obtained from satellite measurements by scientists who develop risk models for mosquito‐borne diseases. Public participation in mosquito surveillance research provides the opportunity to obtain the volume, velocity and variety of data needed to fight the threat of vector‐borne diseases, especially in under‐resourced communities with minimal to no municipal surveillance and mitigation services. GLOBE Mosquito Habitat Mappers document and describe potential and active mosquito larval habitats in and around their homes and communities. An easy‐to‐use pictorial interface enables users to geolocate and describe oviposition sites encountered, count and identify mosquito larvae, and when appropriate, eliminate the larval habitats. During Mosquito Habitat Mapper's first 3 years of use, over 24,000 data observations have been reported throughout the world. This technical report summarizes GLOBE Mosquito Habitat Mapper data reported by GLOBE citizen scientists from three regions: Africa, Asia and the Pacific Islands, and Latin America and the Caribbean. Localized mosquito larvae distribution patterns were examined by comparing data collected in three cities in Senegal–Dakar, Touba, and Thilmakha. The Senegal data show habitat and genera differences among mosquitoes identified by citizen scientists in the cities and illustrates the potential of the app for community‐based surveillance and research.

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.

Even as novel technologies emerge and medicines advance, pathogen-transmitting mosquitoes pose a deadly and accelerating public health threat. Detecting and mitigating the spread of Anopheles stephensi in Africa is now critical to the fight against malaria, as this invasive mosquito poses urgent and unprecedented risks to the continent. Unlike typical African vectors of malaria, An. stephensi breeds in both natural and artificial water reservoirs, and flourishes in urban environments. With An. stephensi beginning to take hold in heavily populated settings, citizen science surveillance supported by novel artificial intelligence (AI) technologies may offer impactful opportunities to guide public health decisions and community-based interventions. Coalitions like the Global Mosquito Alert Consortium (GMAC) and our freely available digital products can be incorporated into enhanced surveillance of An. stephensi and other vector-borne public health threats. By connecting local citizen science networks with global databases that are findable, accessible, interoperable, and reusable (FAIR), we are leveraging a powerful suite of tools and infrastructure for the early detection of, and rapid response to, (re)emerging vectors and diseases.

Large wildfires (>125 hectares) in the United States account for over 95% of the burned area each year. Predicting large wildfires is imperative, however, current wildfire predictive models are region-based and computationally intensive. Using a scalable model based on easily available environmental and atmospheric data, this research aims to accurately predict across the United States whether large wildfires will develop. The data used in this study includes 2,109 wildfires over 20 years, representing 14 million hectares burned. Remote sensing environmental data (Normalized Difference Vegetation Index - NDVI, Enhanced Vegetation Index - EVI, Leaf Area Index - LAI, Fraction of Photosynthetically Active Radiation - FPAR, Land Surface Temperature during the Day - LST Day, and Land Surface Temperature during the Night - LST Night) consisting of 1.3 billion satellite observations was used. Atmospheric reanalysis data (u component of wind, v component of wind, relative humidity, temperature, and geopotential) at four pressure levels (300, 500, 700, and 850 Ha) were also factored. Six machine learning classification models (Logistic Regression, Decision Tree, Random Forest, eXtreme Gradient Boosting, K-Nearest-Neighbors, and Support Vector Machine) were created and tested on the resulting dataset to determine their accuracy in predicting large wildfires. Model validation tests and variable importance analysis were performed. The eXtreme Gradient Boosting (XGBoost) Classification model performed the best in predicting large wildfires, with 90.44% accuracy, a True Positive Rate of 0.92, and a True Negative Rate of 0.88. Furthermore, towards environmental justice, an analysis was performed to identify disadvantaged communities that are also vulnerable to wildfires. This model can be used by wildfire safety organizations to predict large wildfires with high accuracy and prioritize resource allocation to employ protective safeguards for impacted socioeconomically disadvantaged communities.