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

Authors Robert Manning Smith , Sarah Wise , Sophie Ayling

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Author Details

Robert Manning Smith
  • The Bartlett Centre for Advanced Spatial Analysis, University College London, UK
Sarah Wise
  • The Bartlett Centre for Advanced Spatial Analysis, University College London, UK
Sophie Ayling
  • The Bartlett Centre for Advanced Spatial Analysis, University College London, UK

Funding

  • Manning Smith, Robert: UKRI Grant MR/T02075X/1.
  • Wise, Sarah: UKRI Grant MR/T02075X/1.
  • Ayling, Sophie: UBEL-Doctoral Training Partnership ES/P000592/1.

Cite As Get BibTex

Robert Manning Smith, Sarah Wise, and Sophie Ayling. Calibration in a Data Sparse Environment: How Many Cases Did We Miss? (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 50:1-50:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.GIScience.2023.50

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Modeling methodologies
Keywords
  • Agent Based Modelling
  • Infectious Disease Modelling
  • COVID-19
  • Zimbabwe
  • SARS-CoV-2
  • calibration

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