This paper is in the following e-collection/theme issue:

mHealth for Data Collection and Research (981) Mobile Health (mhealth) (2982) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (847) mHealth for Data Collection and Research (981) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (578)

Published on in Vol 2, No 1 (2014): Jan-Mar

The Schisto Track: A System for Gathering and Monitoring Epidemiological Surveys by Connecting Geographical Information Systems in Real Time

The Schisto Track: A System for Gathering and Monitoring Epidemiological Surveys by Connecting Geographical Information Systems in Real Time

The Schisto Track: A System for Gathering and Monitoring Epidemiological Surveys by Connecting Geographical Information Systems in Real Time

Authors of this article:

Onicio B Leal Neto1 Author Orcid Image ;   Cesar M Albuquerque1 ;   Jones O Albuquerque2 Author Orcid Image ;   Constança S Barbosa1
Article Authors Cited by (9) Tweetations (3) Metrics

Journals

  1. Molina Recio G, García-Hernández L, Molina Luque R, Salas-Morera L. The role of interdisciplinary research team in the impact of health apps in health and computer science publications: a systematic review. BioMedical Engineering OnLine 2016;15(S1) View
  2. Chang Y, Chiang W, Wang W, Lin C, Hung L, Tsai Y, Suen J, Chen Y. Google Trends-based non-English language query data and epidemic diseases: a cross-sectional study of the popular search behaviour in Taiwan. BMJ Open 2020;10(7):e034156 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. Oliveira A, Prats C, Espasa M, Zarzuela Serrat F, Montañola Sales C, Silgado A, Codina D, Arruda M, i Prat J, Albuquerque J. The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis. JMIR Research Protocols 2017;6(4):e70 View
  5. Masud M, Alhumyani H, Alshamrani S, Cheikhrouhou O, Ibrahim S, Muhammad G, Hossain M, Shorfuzzaman M. Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application. Wireless Communications and Mobile Computing 2020;2020:1 View
  6. Sawik B, Płonka J. Project and Prototype of Mobile Application for Monitoring the Global COVID-19 Epidemiological Situation. International Journal of Environmental Research and Public Health 2022;19(3):1416 View

Books/Policy Documents

  1. Dantas Oliveira A, Carvalho B, Prats C, Espasa M, Gomez i Prat J, Lopez Codina D, Albuquerque J. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. View
  2. Molina-Recio G, García-Hernández L, Castilla-Melero A, Palomo-Romero J, Molina-Luque R, Sánchez-Muñoz A, Arauzo-Azofra A, Salas-Morera L. Bioinformatics and Biomedical Engineering. View
  3. Areefa , Koneru S, Pragathi K, Rishitha K. Intelligent Control, Robotics, and Industrial Automation. View