A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports

Journals

1. Mellou K, Saranti-Papasaranti E, Mandilara G, Georgakopoulou T. Laboratory capacity of Greek hospitals for diagnosis of salmonellosis and surveillance systems’ performance in the years of economic crisis, 2010–2016. Epidemiology and Infection 2019;147 View
2. Ogden N, AbdelMalik P, Pulliam J. Emerging infectious diseases: prediction and detection. Canada Communicable Disease Report 2017;43(10):206 View
3. Leal Neto O, Cruz O, Albuquerque J, Nacarato de Sousa M, Smolinski M, Pessoa Cesse E, Libel M, Vieira de Souza W. Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study. JMIR Public Health and Surveillance 2020;6(2):e16119 View
4. Gupta A, Katarya R. Social media based surveillance systems for healthcare using machine learning: A systematic review. Journal of Biomedical Informatics 2020;108:103500 View
5. Wenham C, Gray E, Keane C, Donati M, Paolotti D, Pebody R, Fragaszy E, McKendry R, Edmunds W. Self-Swabbing for Virological Confirmation of Influenza-Like Illness Among an Internet-Based Cohort in the UK During the 2014-2015 Flu Season: Pilot Study. Journal of Medical Internet Research 2018;20(3):e71 View
6. Moumni Abdou H, Dahbi I, Akrim M, Meski F, Khader Y, Lakranbi M, Ezzine H, Khattabi A. Outbreak Investigation of a Multipathogen Foodborne Disease in a Training Institute in Rabat, Morocco: Case-Control Study. JMIR Public Health and Surveillance 2019;5(3):e14227 View
7. Maharana A, Cai K, Hellerstein J, Hswen Y, Munsell M, Staneva V, Verma M, Vint C, Wijaya D, Nsoesie E. Detecting reports of unsafe foods in consumer product reviews. JAMIA Open 2019;2(3):330 View
8. Deng X, Cao S, Horn A. Emerging Applications of Machine Learning in Food Safety. Annual Review of Food Science and Technology 2021;12(1):513 View
9. Marshall Q, Bellows A, McLaren R, Jones A, Fanzo J. You Say You Want a Data Revolution? Taking on Food Systems Accountability. Agriculture 2021;11(5):422 View
10. Tao D, Zhang D, Hu R, Rundensteiner E, Feng H. Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media. Scientific Reports 2021;11(1) View
11. Tian X, Hu J, Wei T, Ding W, Miao Q, Ning Z, Fan S, Wu H, Lu J, Lyu M, Wang S. Fast and sensitive graphene oxide‐DNAzyme‐based biosensor for Vibrio alginolyticus detection. Journal of Fish Diseases 2022;45(5):687 View
12. Zhou Q, Zhang H, Wang S. Artificial intelligence, big data, and blockchain in food safety. International Journal of Food Engineering 2022;18(1):1 View
13. Zanetta L, Mucinhato R, Hakim M, Stedefeldt E, da Cunha D. What Motivates Consumer Food Safety Perceptions and Beliefs? A Scoping Review in BRICS Countries. Foods 2022;11(3):432 View
14. Tao D, Hu R, Zhang D, Laber J, Lapsley A, Kwan T, Rathke L, Rundensteiner E, Feng H. A Novel Foodborne Illness Detection and Web Application Tool Based on Social Media. Foods 2023;12(14):2769 View
15. Tseng Y, Olson K, Bloch D, Mandl K. Engaging a national-scale cohort of smart thermometer users in participatory surveillance. npj Digital Medicine 2023;6(1) View
16. Pudaruth C, Biranjia-Hurdoyal S. Food Safety Knowledge and Practice in the Era of Dark Kitchens. BioMed Target Journal 2024:3 View
17. Makhunga S, Hlongwana K. Food handling practices and sanitary conditions of charitable food assistance programs in eThekwini District, KwaZulu-Natal, South Africa. Scientific Reports 2024;14(1) View
18. Pudaruth C, Biranjia-Hurdoyal S. Food Safety Knowledge and Practice in the Era of Dark Kitchens. BioMed Target Journal 2024;2(1):50 View

Books/Policy Documents

1. Pujahari R, Khan R. Artificial Intelligence Applications in Agriculture and Food Quality Improvement. View