Literature DB >> 34627478

Digital and technological innovation in vector-borne disease surveillance to predict, detect, and control climate-driven outbreaks.

Caitlin Pley1, Megan Evans2, Rachel Lowe3, Hugh Montgomery4, Sophie Yacoub5.   

Abstract

Vector-borne diseases are particularly sensitive to changes in weather and climate. Timely warnings from surveillance systems can help to detect and control outbreaks of infectious disease, facilitate effective management of finite resources, and contribute to knowledge generation, response planning, and resource prioritisation in the long term, which can mitigate future outbreaks. Technological and digital innovations have enabled the incorporation of climatic data into surveillance systems, enhancing their capacity to predict trends in outbreak prevalence and location. Advance notice of the risk of an outbreak empowers decision makers and communities to scale up prevention and preparedness interventions and redirect resources for outbreak responses. In this Viewpoint, we outline important considerations in the advent of new technologies in disease surveillance, including the sustainability of innovation in the long term and the fundamental obligation to ensure that the communities that are affected by the disease are involved in the design of the technology and directly benefit from its application.
Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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Year:  2021        PMID: 34627478     DOI: 10.1016/S2542-5196(21)00141-8

Source DB:  PubMed          Journal:  Lancet Planet Health        ISSN: 2542-5196


  2 in total

1.  A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions.

Authors:  Surbhi Bhatia; Dhruvisha Bansal; Seema Patil; Sharnil Pandya; Qazi Mudassar Ilyas; Sajida Imran
Journal:  Front Public Health       Date:  2022-05-27

2.  The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality.

Authors:  Damien K Ming; Nguyen M Tuan; Bernard Hernandez; Sorawat Sangkaew; Nguyen L Vuong; Ho Q Chanh; Nguyen V V Chau; Cameron P Simmons; Bridget Wills; Pantelis Georgiou; Alison H Holmes; Sophie Yacoub
Journal:  Front Digit Health       Date:  2022-03-14
  2 in total

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