License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.50
URN: urn:nbn:de:0030-drops-189452
URL: https://drops.dagstuhl.de/opus/volltexte/2023/18945/
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Manning Smith, Robert ; Wise, Sarah ; Ayling, Sophie

Calibration in a Data Sparse Environment: How Many Cases Did We Miss? (Short Paper)

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LIPIcs-GIScience-2023-50.pdf (0.6 MB)


Abstract

Reported case numbers in the COVID-19 pandemic are assumed in many countries to have underestimated the true prevalence of the disease. Deficits in reporting may have been particularly great in countries with limited testing capability and restrictive testing policies. Simultaneously, some models have been accused of over-reporting the scale of the pandemic. At a time when modeling consortia around the world are turning to the lessons learnt from pandemic modelling, we present an example of simulating testing as well as the spread of disease. In particular, we factor in the amount and nature of testing that was carried out in the first wave of the COVID-19 pandemic (March - September 2020), calibrating our spatial Agent Based Model (ABM) model to the reported case numbers in Zimbabwe.

BibTeX - Entry

@InProceedings{manningsmith_et_al:LIPIcs.GIScience.2023.50,
  author =	{Manning Smith, Robert and Wise, Sarah and Ayling, Sophie},
  title =	{{Calibration in a Data Sparse Environment: How Many Cases Did We Miss?}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{50:1--50:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18945},
  URN =		{urn:nbn:de:0030-drops-189452},
  doi =		{10.4230/LIPIcs.GIScience.2023.50},
  annote =	{Keywords: Agent Based Modelling, Infectious Disease Modelling, COVID-19, Zimbabwe, SARS-CoV-2, calibration}
}

Keywords: Agent Based Modelling, Infectious Disease Modelling, COVID-19, Zimbabwe, SARS-CoV-2, calibration
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023


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