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ZIKA: A New System to Empower Health Workers and Local Communities to Improve Surveillance Protocols by E-learning and to Forecast Zika Virus in Real Time in Brazil

Published: 23 April 2018 Publication History

Abstract

The devastating consequences of neonates infected with the Zika virus makes it necessary to fight and stop the spread of this virus and its vectors (Aedes mosquitoes). An essential part of the fight against mosquitoes is the use of mobile technology to support routine surveillance and risk assessment by community health workers (health agents). In addition, to improve early warning systems, the public health authorities need to forecast more accurately where an outbreak of the virus and its vector is likely to occur. The ZIKΛ system aims to develop a novel comprehensive framework that combines e-learning to empower health agents, community-based participatory surveillance, and forecasting of occurrences and distribution of the Zika virus and its vectors in real time. This system is currently being implemented in Brazil, in the cities of Campina Grande, Recife, Jaboatão dos Guararapes, and Olinda, the State of Pernambuco and Paraiba with the highest prevalence of the Zika virus disease. In this paper, we present the ZIKA system which helps health agents to learn new techniques and good practices to improve the surveillance of the virus and offer a real time distribution forecast of the virus and the vector. The forecast model is recalibrated in real time with information coming from health agents, governmental institutions, and weather stations to predict the areas with higher risk of a Zika virus outbreak in an interactive map. This mapping and alert system will help governmental institutions to make fast decisions and use their resources more efficiently to stop the spread of the Zika virus. The ZIKA app was developed and built in Ionic which allows for easy cross-platform rendering for both iOS and Android. The system presented in the current paper is one of the first systems combining public health surveillance, citizen-driven participatory reporting and weather data-based prediction. The implementation of the ZIKA system will reduce the devastating consequences of Zika virus in neonates and improve the life quality of vulnerable people in Brazil.

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Cited By

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  • (2023)Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case studyFrontiers in Tropical Diseases10.3389/fitd.2023.10397354Online publication date: 26-May-2023
  • (2022)An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina GrandeData10.3390/data70801067:8(106)Online publication date: 30-Jul-2022
  • (2022)Spatiotemporal forecasting for dengue, chikungunya fever and Zika using machine learning and artificial expert committees based on meta-heuristicsResearch on Biomedical Engineering10.1007/s42600-022-00202-638:2(499-537)Online publication date: 17-Feb-2022
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  1. ZIKA: A New System to Empower Health Workers and Local Communities to Improve Surveillance Protocols by E-learning and to Forecast Zika Virus in Real Time in Brazil

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      cover image ACM Conferences
      DH '18: Proceedings of the 2018 International Conference on Digital Health
      April 2018
      172 pages
      ISBN:9781450364935
      DOI:10.1145/3194658
      This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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      • IW3C2: International World Wide Web Conference Committee
      • University College London: University College London

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 23 April 2018

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      Author Tags

      1. big data
      2. e-learning
      3. forecasting
      4. surveillance
      5. zika virus

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      • Short-paper

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      • British Council Newton Fund

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      DH'18
      Sponsor:
      • IW3C2
      • University College London
      DH'18: International Digital Health Conference
      April 23 - 26, 2018
      Lyon, France

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      Cited By

      View all
      • (2023)Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case studyFrontiers in Tropical Diseases10.3389/fitd.2023.10397354Online publication date: 26-May-2023
      • (2022)An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina GrandeData10.3390/data70801067:8(106)Online publication date: 30-Jul-2022
      • (2022)Spatiotemporal forecasting for dengue, chikungunya fever and Zika using machine learning and artificial expert committees based on meta-heuristicsResearch on Biomedical Engineering10.1007/s42600-022-00202-638:2(499-537)Online publication date: 17-Feb-2022
      • (2021)MEWAR: Development of a Cross-Platform Mobile Application and Web Dashboard System for Real-Time Mosquito Surveillance in Northeast BrazilFrontiers in Public Health10.3389/fpubh.2021.7540729Online publication date: 27-Oct-2021
      • (2021)Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic ReviewsFrontiers in Public Health10.3389/fpubh.2021.6452609Online publication date: 5-May-2021
      • (2021)Modeling unmanned aerial vehicle system for identifying foci of arboviral disease with monitoring systemInternational Journal of Modeling, Simulation, and Scientific Computing10.1142/S179396232250015513:03Online publication date: 30-Sep-2021
      • (2021)Scoping future outbreaks: a scoping review on the outbreak prediction of the WHO Blueprint list of priority diseasesBMJ Global Health10.1136/bmjgh-2021-0066236:9(e006623)Online publication date: 16-Sep-2021
      • (2021)A review exploring the overarching burden of Zika virus with emphasis on epidemiological case studies from BrazilEnvironmental Science and Pollution Research10.1007/s11356-021-15984-y28:40(55952-55966)Online publication date: 8-Sep-2021
      • (2021)A customisable pipeline for the semi-automated discovery of online activists and social campaigns on TwitterWorld Wide Web10.1007/s11280-021-00887-224:4(1235-1271)Online publication date: 11-Jun-2021
      • (2021)Intelligent Systems for Dengue, Chikungunya, and Zika Temporal and Spatio-Temporal Forecasting: A Contribution and a Brief ReviewAssessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis10.1007/978-3-030-79753-9_17(299-331)Online publication date: 14-Jun-2021
      • Show More Cited By

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