A dynamic neural network model for predicting risk of Zika in real time.

Journal: BMC medicine
PMID: 31474220

Abstract

BACKGROUND: In 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak's expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner.

Authors

  • Mahmood Akhtar
    School of Civil and Environmental Engineering's Research Centre for Integrated Transport Innovation (rCITI), University of New South Wales, Sydney, Australia.
  • Moritz U G Kraemer
    Moritz U. G. Kraemer, DPhil, is in the Department of Zoology, University of Oxford, UK; Computational Epidemiology group, Boston Children's Hospital, Boston, MA; and Harvard Medical School, Harvard University, Boston, MA.
  • Lauren M Gardner
    School of Civil and Environment Engineering, UNSW Sydney, Sydney, NSW, Australia. l.gardner@jhu.edu.

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