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        <identifier>oai:figshare.com:article/31063375</identifier>
        <datestamp>2026-09-29T00:23:34Z</datestamp>
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          <dc:title>AI for Covid-19 : Conduits for Public Health Surveillance</dc:title>
          <dc:creator>Chandana Unnithan (5936567)</dc:creator>
          <dc:subject>Planning and decision making</dc:subject>
          <dc:subject>Artificial intelligence</dc:subject>
          <dc:subject>Australia</dc:subject>
          <dc:subject>Canada</dc:subject>
          <dc:subject>Covid-19</dc:subject>
          <dc:subject>Nowcasting</dc:subject>
          <dc:subject>Public health</dc:subject>
          <dc:description>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.</dc:description>
          <dc:date>2020-12-12T00:00:00Z</dc:date>
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          <dc:type>Chapter</dc:type>
          <dc:identifier>10.1007/978-981-15-9682-7_2</dc:identifier>
          <dc:relation>https://figshare.com/articles/chapter/AI_for_Covid-19_Conduits_for_Public_Health_Surveillance/31063375</dc:relation>
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