Project description:Rapid Acceleration of Diagnostics - Digital Health (RADx-DH): Covidseeker and COVID-19 Citizen Science: Leveraging Citizen Science and Real-Time Geospatial Temporal Mobile Data for Digital Contact Tracing and SARS-CoV-2 Hotspotting
Project description:Rapid Acceleration of Diagnostics - Digital Health (RADx-DH): Covidseeker and COVID-19 Citizen Science: Leveraging Citizen Science and Real-Time Geospatial Temporal Mobile Data for Digital Contact Tracing and SARS-CoV-2 Hotspotting
Project description:The Isala Citizen Science Project, is named after the first female doctor in Belgium, Isala Van Diest (1842-1916). In a co-creative way, volunteers provided samples and data, proposed research objectives and survey questions, and helped to disseminate and interpret the objectives and results through different media platforms, jointly breaking taboos on women’s and vaginal health. In Flow 1, we collected a single sample from a cross-section of the population, characterized the microbiome, and associated it with data collected in questionnaires. Here, we provide extensive sample meta-data for over 3000 women who completed questionnaires on their lifestyle, diet, health, contraceptive use and environment.
Project description:Global monitoring of disease vectors is undoubtedly becoming an urgent need as the human population rises and becomes increasingly mobile, international commercial exchanges increase, and climate change expands the habitats of many vector species. Traditional surveillance of mosquitoes, vectors of many diseases, relies on catches, which requires regular manual inspection and reporting, and dedicated personnel, making large-scale monitoring difficult and expensive. New approaches are solving the problem of scalability by relying on smartphones and the Internet to enable novel community-based and digital observatories, where people can upload pictures of mosquitoes whenever they encounter them. An example is the Mosquito Alert citizen science system, which includes a dedicated mobile phone app through which geotagged images are collected. This system provides a viable option for monitoring the spread of various mosquito species across the globe, although it is partly limited by the quality of the citizen scientists' photos. To make the system useful for public health agencies, and to give feedback to the volunteering citizens, the submitted images are inspected and labeled by entomology experts. Although citizen-based data collection can greatly broaden disease-vector monitoring scales, manual inspection of each image is not an easily scalable option in the long run, and the system could be improved through automation. Based on Mosquito Alert's curated database of expert-validated mosquito photos, we trained a deep learning model to find tiger mosquitoes (Aedes albopictus), a species that is responsible for spreading chikungunya, dengue, and Zika among other diseases. The highly accurate 0.96 area under the receiver operating characteristic curve score promises not only a helpful pre-selector for the expert validation process but also an automated classifier giving quick feedback to the app participants, which may help to keep them motivated. In the paper, we also explored the possibilities of using the model to improve future data collection quality as a feedback loop.
Project description:Cities around the world have converged on structural and environmental characteristics that exert similar eco-evolutionary pressures on local communities. However, evaluating how urban biodiversity responds to urban intensification remains poorly understood because of the challenges in capturing the diversity of a range of taxa within and across multiple cities from different types of urbanization. Here we utilize a growing resource-citizen science data. We analyzed 66,209 observations representing 5,209 species generated by the City Nature Challenge project on the iNaturalist platform, in conjunction with remote sensing (NLCD2011) environmental data, to test for urban biotic homogenization at increasing levels of urban intensity across 14 metropolitan cities in the United States. Based on community composition analyses, we found that while similarities occur to an extent, urban biodiversity is often much more a reflection of the taxa living locally in a region. At the same time, the communities found in high-intensity development were less explained by regional context than communities from other land cover types were. We also found that the most commonly observed species are often shared between cities and are non-endemic and/or have a distribution facilitated by humans. This study highlights the value of citizen science data in answering questions in urban ecology.