Literature DB >> 31974703

[Digital epidemiology].

Dirk Brockmann1,2,3.   

Abstract

Digital epidemiology is a new and rapidly growing field. The technological revolution we have been witnessing during the last decade, the global rise of the Internet, the emergence of social media and social networks that connect individuals worldwide for information exchange and social interactions, and the almost complete social penetration of mobile devices such as smartphones provide access to data on individual behavior with unprecedented resolution and precision. In digital epidemiology, this type of high-resolution behavioral data is analyzed to advance our understanding of, for example, infectious disease dynamics and improve our abilities to forecast epidemic outbreaks and related phenomena.This article provides an overview on the topic. Different aspects of digital epidemiology are alluded to. Based on examples, I will explain how epidemiological data is integrated on new comprehensive and interactive websites, how the analysis of interactions and activities on social media platforms can yield answers to epidemiological questions, and finally how individual-based data collected by smartphones or wearable sensors in natural experiments can be used to reconstruct contact and physical proximity networks the knowledge of which substantially improves the predictive power of computational models for transmissible infectious diseases.The challenges posed in terms of privacy protection and data security will be discussed. Concepts and solutions will be explained that may help to improve public health by leveraging the new data while at the same time protecting the individual's data sovereignty and personal dignity.

Entities:  

Keywords:  Artificial intelligence; Big data; Complex networks; Computational epidemiology; Machine learning

Mesh:

Year:  2020        PMID: 31974703     DOI: 10.1007/s00103-019-03080-z

Source DB:  PubMed          Journal:  Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz        ISSN: 1436-9990            Impact factor:   1.513


  2 in total

1.  Twitter improves influenza forecasting.

Authors:  Michael J Paul; Mark Dredze; David Broniatowski
Journal:  PLoS Curr       Date:  2014-10-28

2.  Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.

Authors:  H A Haenssle; C Fink; R Schneiderbauer; F Toberer; T Buhl; A Blum; A Kalloo; A Ben Hadj Hassen; L Thomas; A Enk; L Uhlmann
Journal:  Ann Oncol       Date:  2018-08-01       Impact factor: 32.976

  2 in total
  4 in total

Review 1.  [Digital public health-an overview].

Authors:  Hajo Zeeb; Iris Pigeot; Benjamin Schüz
Journal:  Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz       Date:  2020-02       Impact factor: 1.513

Review 2.  [Ethical implications of digital public health].

Authors:  Georg Marckmann
Journal:  Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz       Date:  2020-02       Impact factor: 1.513

Review 3.  [Health reporting as part of public health surveillance: the example of diabetes].

Authors:  Lukas Reitzle; Rebecca Paprott; Francesca Färber; Christin Heidemann; Christian Schmidt; Roma Thamm; Christa Scheidt-Nave; Thomas Ziese
Journal:  Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz       Date:  2020-09       Impact factor: 1.513

Review 4.  [Geographic methods for health monitoring].

Authors:  Daniela Koller; Doris Wohlrab; Georg Sedlmeir; Jobst Augustin
Journal:  Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz       Date:  2020-09       Impact factor: 1.513

  4 in total

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