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Epidemiology and Infection, 145(1), 156–169.
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<journal_title>Australian and New Zealand Journal of Public
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<issue>1</issue>
<volume>42</volume>
<doi>10.1111/1753-6405.12750</doi>
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L. J., Stephens, N., McVernon, J., Dawson, P., &amp; McCaw., J. M.
(2018). Epidemic forecasts as a tool for public health: Interpretation
and (re)calibration. Australian and New Zealand Journal of Public
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<unstructured_citation>Pyne, C. (2018). Disease forecasting
system takes out National Innovation Awards. In Media release.
Australian Department of Defence.
https://www.minister.defence.gov.au/media-releases/2018-05-11/disease-forecasting-system-takes-out-national-innovation-awards</unstructured_citation>
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<article_title>Accounting for healthcare-seeking behaviours
and testing practices in real-time influenza forecasts</article_title>
<author>Moss</author>
<journal_title>Tropical Medicine and Infectious
Disease</journal_title>
<issue>1</issue>
<volume>4</volume>
<doi>10.3390/tropicalmed4010012</doi>
<cYear>2019</cYear>
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S. J., &amp; McCaw., J. M. (2019). Accounting for healthcare-seeking
behaviours and testing practices in real-time influenza forecasts.
Tropical Medicine and Infectious Disease, 4(1), 12.
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<article_title>Anatomy of a seasonal influenza epidemic
forecast</article_title>
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<journal_title>Communicable Diseases
Intelligence</journal_title>
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P., Franklin, L. J., Birrell, F. A., &amp; McCaw., J. M. (2019). Anatomy
of a seasonal influenza epidemic forecast. Communicable Diseases
Intelligence, 43, 1–14.
https://doi.org/10.33321/cdi.2019.43.7</unstructured_citation>
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Cromer, D., Wood, J., Abbott, S., McVernon, J., &amp; McCaw, J. M.
(2020). Early analysis of the Australian COVID-19 epidemic. eLife, 9,
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<article_title>Coronavirus disease model to inform
transmission-reducing measures and health system preparedness,
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<journal_title>Emerging Infectious Diseases</journal_title>
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J. M. (2023). Forecasting COVID-19 activity in Australia to support
pandemic response: May to October 2020. Scientific Reports, 13(1).
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<journal_title>Journal of Computational and Graphical
Statistics</journal_title>
<issue>1</issue>
<volume>5</volume>
<doi>10.1080/10618600.1996.10474692</doi>
<cYear>1996</cYear>
<unstructured_citation>Kitagawa, G. (1996). Monte Carlo
filter and smoother for non-Gaussian nonlinear state space models.
Journal of Computational and Graphical Statistics, 5(1), 1–25.
https://doi.org/10.1080/10618600.1996.10474692</unstructured_citation>
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