@inbook{a7b435ad6b504c2287160991b074b251,
title = "AI for Covid-19: Conduits for Public Health Surveillance",
abstract = "The spread of SARS-Covid-19 virus has impacted the world as it continues to raise questions on the long-term impacts. With the absence of historic/big data sets, digital public health surveillance measures are informed via modelling using real-time data, which is collected and validated by public health agencies; and also aggregated/merged with self/open reported data by the public, via mobile apps and social media channels. This chapter informs on such conduits (using two case studies: Australia and Canada) that enabled AI-based solutions for informing public health strategies. Blue tooth technology used in contact tracing apps seems to allay privacy concerns to an extent, in both countries. Real-time streamed data collection to train predictive models and combining AI methods with active learning seems to be the way forward.",
keywords = "Artificial intelligence, Australia, Canada, Covid-19, Nowcasting, Public health",
author = "C. Unnithan and J. Hardy and N. Lilley",
note = "Funding Information: The authors wish to thank Fern Hardy and Madeleine Hardin from Lifeguard Digital Health team, Canada; and Nora Weber from Terracom Communications, Canada, for their valuable insights. Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2020",
month = dec,
day = "12",
doi = "10.1007/978-981-15-9682-7_2",
language = "English",
isbn = "978-981-15-9681-0",
series = "Lecture Notes on Data Engineering and Communications Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "9--17",
editor = "{Santosh }, {K.C. } and { Joshi}, Amit",
booktitle = "Lecture Notes on Data Engineering and Communications Technologies",
edition = "1",
}