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COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing

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  • Norman E. Fenton
  • Martin Neil
  • Magda Osman
  • Scott McLachlan

Abstract

COVID-19 testing strategies are primarily driven by medical need - focusing on people already hospitalized with significant symptoms or on people most at risk. However, such testing is highly biased because it fails to identify the extent to which COVID-19 is present in people with mild or no symptoms. If we wish to understand the true rate of COVID-19 infection and death, we need to take full account of the causal explanations for the resulting data to avoid highly misleading conclusions about infection and death rates. We describe how causal (Bayesian network) models can provide such explanations and the need to combine these with more random testing in order to achieve reliable data and predictions for the both policy makers and the public.

Suggested Citation

  • Norman E. Fenton & Martin Neil & Magda Osman & Scott McLachlan, 2020. "COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing," Journal of Risk Research, Taylor & Francis Journals, vol. 23(7-8), pages 862-865, August.
    • Handle: RePEc:taf:jriskr:v:23:y:2020:i:7-8:p:862-865
    DOI: 10.1080/13669877.2020.1756381
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    Cited by:

    1. Zhu, Chunli & Wu, Jianping & Liu, Mingyu & Wang, Linyang & Li, Duowei & Kouvelas, Anastasios, 2021. "Recovery preparedness of global air transport influenced by COVID-19 pandemic: Policy intervention analysis," Transport Policy, Elsevier, vol. 106(C), pages 54-63.
    2. Simon Risanger & Bismark Singh & David Morton & Lauren Ancel Meyers, 2021. "Selecting pharmacies for COVID-19 testing to ensure access," Health Care Management Science, Springer, vol. 24(2), pages 330-338, June.
    3. Karla Soria-Barreto & Sofia Ruiz-Campo & Ahmad Samed Al-Adwan & Sergio Zuniga-Jara, 2021. "University Students Intention to Continue Using Online Learning Tools and Technologies: An International Comparison," Sustainability, MDPI, vol. 13(24), pages 1-23, December.

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