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Mosquitoes and Public Health: Improving Data Validation of Citizen Science Contributions Using Computer Vision

  • J. Pablo Muñoz
  • Rebecca Boger
  • Scott Dexter
  • Russanne Low
Chapter
Part of the Healthcare Delivery in the Information Age book series (Healthcare Delivery Inform. Age)

Abstract

Citizen scientists have the potential to play an important role in combating mosquito-borne diseases such as malaria, dengue fever, Zika, and West Nile virus. While public health officials do not have the labor or resources to collect adequate spatial and temporal coverage of where, when, and what types of mosquitoes are found, these officials may be hesitant to include citizen science data because they are not confident of the data. The GLOBE Observer Mosquito Habitat Mapper (GO MHM) app was launched in 2017, and data are being collected from around the world. As part of the app, photos of mosquito larvae are submitted. Computer vision software provides a means of assisting in the validation of the images for correctness. We have developed an image recognition prototype that includes image collection, training of image classifiers, specimen recognition, and expert validation and analytics. Photos of known mosquito larvae are used to train a Deep Learning classification model. Citizen scientists submit photos, and the system conducts an analysis that indicates the probability of correct mosquito identification. New and better classification models can be trained from new data received from citizen scientists. Experts can have access to the recognition results to assess data quality and devise ways to improve the quality of GO MHM data, while public health officials can use the data to assist in the mitigation of disease outbreaks. Recommendations are made for prototype improvements.

Keywords

Mosquitoes Vector-borne diseases Computer vision Public health Citizen science 

References

  1. Barré, P., Stöver, B. C., Müller, K. F., & Steinhage, V. (2017). LeafNet: A computer vision system for automatic plant species identification. Ecological Informatics, 40, 50–56.CrossRefGoogle Scholar
  2. Berger-Wolf, T. Y., Rubenstein, D. I., Stewart, C. V., Holmberg, J. A., Parham, J., Menon, S., Crall, J., Van Oast, J., Kiciman, E., & Joppa, L. (2017). Wildbook: Crowdsourcing, computer vision, and data science for conservation. arXiv preprint arXiv:1710.08880.Google Scholar
  3. Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J. (2009). Citizen science: A developing tool for expanding science knowledge and scientific literacy. Bioscience, 59(11), 977–984.CrossRefGoogle Scholar
  4. Bonney, R., Shirk, J. L., Phillips, T. B., Wiggins, A., Ballard, H. L., Miller-Rushing, A. J., & Parrish, J. K. (2014). Next steps for citizen science. Science, 343(6178), 1436–1437.CrossRefGoogle Scholar
  5. Bonney, R., Phillips, T. B., Ballard, H. L., & Enck, J. W. (2016). Can citizen science enhance public understanding of science? Public Understanding of Science, 25(1), 2–16.CrossRefGoogle Scholar
  6. Chandler, M., See, L., Copas, K., Bonde, A. M., López, B. C., Danielsen, F., Legind, J. K., Masinde, S., Miller-Rushing, A. J., Newman, G., & Rosemartin, A. (2017). Contribution of citizen science towards international biodiversity monitoring. Biological Conservation, 213, 280–294.CrossRefGoogle Scholar
  7. Dhami, D. S., Leake, D., & Natarajan, S. (2017, March). Knowledge-based morphological classification of galaxies from vision features. In Workshops at the thirty-first AAAI conference on artificial intelligence.Google Scholar
  8. Dieleman, S., Willett, K. W., & Dambre, J. (2015). Rotation-invariant convolutional neural networks for galaxy morphology prediction. Monthly Notices of the Royal Astronomical Society, 450(2), 1441–1459.CrossRefGoogle Scholar
  9. Dollár, P., & Zitnick, C. L. (2013). Structured forests for fast edge detection. In Proceedings of the IEEE international conference on computer vision (pp. 1841–1848).Google Scholar
  10. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2014, January). Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning (pp. 647–655).Google Scholar
  11. Follett, R., & Strezov, V. (2015). An analysis of citizen science based research: usage and publication patterns. PLoS One, 10(11), e0143687.CrossRefGoogle Scholar
  12. Haklay, M. (2013). Citizen Science and volunteered geographic information: Overview and typology of participation (pp. 105–122). Dordrecht: Springer Netherlands.Google Scholar
  13. Haywood, B. K., Parrish, J. K., & Dolliver, J. (2016). Place-based and data-rich citizen science as a precursor for conservation action. Conservation Biology, 30(3), 476–486.CrossRefGoogle Scholar
  14. Horns, J. J., Adler, F. R., & Şekercioğlu, Ç. H. (2018). Using opportunistic citizen science data to estimate avian population trends. Biological Conservation, 221, 151–159.CrossRefGoogle Scholar
  15. Hunter, J., Alabri, A., & van Ingen, C. (2013). Assessing the quality and trustworthiness of citizen science data. Concurrency and Computation: Practice and Experience, 25(4), 454–466.CrossRefGoogle Scholar
  16. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014, November). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 675–678). ACM.Google Scholar
  17. Jordan, R. C., Gray, S. A., Howe, D. V., Brooks, W. R., & Ehrenfeld, J. G. (2011). Knowledge gain and behavioral change in citizen-science programs. Conservation Biology, 25(6), 1148–1154.CrossRefGoogle Scholar
  18. Keshavan, A., Yeatman, J., & Rokem, A. (2018). Combining citizen science and deep learning to amplify expertise in neuroimaging. BioRxiv, 363382.Google Scholar
  19. Kress, W. J., Garcia-Robledo, C., Soares, J. V., Jacobs, D., Wilson, K., Lopez, I. C., & Belhumeur, P. N. (2018). Citizen science and climate change: Mapping the range expansions of native and exotic plants with the mobile app leafsnap. Bioscience, 68(5), 348–358.CrossRefGoogle Scholar
  20. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).Google Scholar
  21. Land-Zandstra, A. M., Devilee, J. L., Snik, F., Buurmeijer, F., & van den Broek, J. M. (2016). Citizen science on a smartphone: Participants’ motivations and learning. Public Understanding of Science, 25(1), 45–60.CrossRefGoogle Scholar
  22. Lawrence, A. (2006). ‘No personal motive?’ Volunteers, biodiversity, and false dichotomies of participation. Ethics, Place & Environment: A Journal of Philosophy & Geography, 9, 279–298.CrossRefGoogle Scholar
  23. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.CrossRefGoogle Scholar
  24. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.CrossRefGoogle Scholar
  25. Leta, S., Beyene, T. J., De Clercq, E. M., Amenu, K., Kraemer, M. U., & Revie, C. W. (2018). Global risk mapping for major diseases transmitted by Aedes aegypti and Aedes albopictus. International Journal of Infectious Diseases, 67, 25–35.CrossRefGoogle Scholar
  26. Mattos, A. B., Herrmann, R., Shigeno, K. K., & Feris, R. S. (2014). A mission-oriented citizen science platform for efficient flower classification based on combination of feature descriptors. In EMR@ ICMR (pp. 45–52).Google Scholar
  27. Mazumdar, S., Wrigley, S., & Ciravegna, F. (2017, January 19). Citizen science and crowdsourcing for earth observations: An analysis of stakeholder opinions on the present and future. Remote Sensing, 9(1), 87.CrossRefGoogle Scholar
  28. McKinley, D. C., Miller-Rushing, A. J., Ballard, H., Bonney, R., Brown, H., Evans, D. M., French, R. A., Parrish, J. K., Phillips, T. B., Ryan, S. F., & Shanley, L. A. (2015). Investing in citizen science can improve natural resource management and environmental protection. Issues in Ecology, 2015(19), 1–27.Google Scholar
  29. McKinley, D. C., Miller-Rushing, A. J., Ballard, H. L., Bonney, R., Brown, H., Cook-Patton, S. C., Evans, D. M., French, R. A., Parrish, J. K., Phillips, T. B., & Ryan, S. F. (2017). Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation, 208, 15–28.CrossRefGoogle Scholar
  30. Munoz, J. P., Boger, R., Dexter, S. Low, R., & Li, J. (2018). Image recognition of disease-carrying insects: A system for combating infectious diseases using image classification techniques and citizen science. In HICSS.Google Scholar
  31. Nguyen, A., Yosinski, J., & Clune, J. (2015). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 427–436).Google Scholar
  32. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., & Berg, A. C. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252.CrossRefGoogle Scholar
  33. Sauermann, H., & Franzoni, C. (2015). Crowd science user contribution patterns and their implications. Proceedings of the National Academy of Sciences, 112(3), 679–684.CrossRefGoogle Scholar
  34. Schaller, R. R. (1997). Moore’s law: Past, present and future. IEEE Spectrum, 34(6), 52–59.CrossRefGoogle Scholar
  35. Sein, M., Henfridsson, O., Purao, S., Rossi, M., & Lindgren, R. (2011). Action design research. https://aisel.aisnet.org/misq/vol35/iss1/5/
  36. Shirk, J. L., Ballard, H. L., Wilderman, C. C., Phillips, T., Wiggins, A., Jordan, R., McCallie, E., Minarchek, M., Lewenstein, B. V., Krasny, M. E., & Bonney, R. (2012). Public participation in scientific research: A framework for deliberate design. Ecology and Society, 17(2), 29.CrossRefGoogle Scholar
  37. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.Google Scholar
  38. Von Alan, R. H., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.CrossRefGoogle Scholar
  39. Wiggins, A., & Crowston, K. (2011, January). From conservation to crowdsourcing: A typology of citizen science. In 2011 44th Hawaii international conference on system sciences (pp. 1–10). IEEE.Google Scholar
  40. Willi, M., Pitman, R. T., Cardoso, A. W., Locke, C., Swanson, A., Boyer, A., Veldthuis, M., & Fortson, L. (2018). Identifying animal species in camera trap images using deep learning and citizen science. Methods in Ecology and Evolution. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13099
  41. World Health Organization. (2014). A global brief on vector-borne diseases (No. WHO/DCO/WHD/2014.1). World Health Organization.Google Scholar
  42. Zitnick, C. L., & Dollár, P.. (2014, September). Edge boxes: Locating object proposals from edges. In European conference on computer vision (pp. 391–405). Cham: Springer.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • J. Pablo Muñoz
    • 1
  • Rebecca Boger
    • 1
  • Scott Dexter
    • 1
  • Russanne Low
    • 2
  1. 1.Brooklyn College, CUNYBrooklynUSA
  2. 2.Institute for Global Environmental StrategiesArlingtonUSA

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