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How to remove the testing bias in CoV-2 statistics

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  • Klaus Wälde

    (Johannes Gutenberg University)

Abstract

BACKGROUND. Public health measures and private behaviour are based on reported numbers of SARS-CoV-2 infections. Some argue that testing influences the confirmed number of infections. OBJECTIVES/METHODS. Do time series on reported infections and the number of tests allow one to draw conclusions about actual infection numbers? A SIR model is presented where the true numbers of susceptible, infectious and removed individuals are unobserved. Testing is also modelled. RESULTS. Official confirmed infection numbers are likely to be biased and cannot be compared over time. The bias occurs because of different reasons for testing (e.g. by symptoms, representative or testing travellers). The paper illustrates the bias and works out the effect of the number of tests on the number of reported cases. The paper also shows that the positive rate (the ratio of positive tests to the total number of tests) is uninformative in the presence of non-representative testing. CONCLUSIONS. A severity index for epidemics is proposed that is comparable over time. This index is based on Covid-19 cases and can be obtained if the reason for testing is known.

Suggested Citation

  • Klaus Wälde, 2020. "How to remove the testing bias in CoV-2 statistics," Working Papers 2021, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    • Handle: RePEc:jgu:wpaper:2021
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    References listed on IDEAS

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    1. Mitze, Timo & Kosfeld, Reinhold & Rode, Johannes & Wälde, Klaus, 2020. "Face masks considerably reduce COVID-19 cases in Germany," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124587, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Mitze, Timo & Kosfeld, Reinhold & Rode, Johannes & Wälde, Klaus, 2020. "Face Masks Considerably Reduce COVID-19 Cases in Germany: A Synthetic Control Method Approach," IZA Discussion Papers 13319, Institute of Labor Economics (IZA).
    3. Diamond, Peter A, 1982. "Aggregate Demand Management in Search Equilibrium," Journal of Political Economy, University of Chicago Press, vol. 90(5), pages 881-894, October.
    4. Donsimoni Jean Roch & Glawion René & Plachter Bodo & Wälde Klaus, 2020. "Projecting the spread of COVID-19 for Germany," German Economic Review, De Gruyter, vol. 21(2), pages 181-216, June.
    5. Jean Roch Donsimoni & René Glawion & Bodo Plachter & Constantin Weiser & Klaus Wälde, 2020. "Should Contact Bans Be Lifted in Germany? A Quantitative Prediction of Its Effects," CESifo Working Paper Series 8242, CESifo.
    6. Alexis Akira Toda, 2020. "Susceptible-Infected-Recovered (SIR) Dynamics of COVID-19 and Economic Impact," Papers 2003.11221, arXiv.org, revised Mar 2020.
    7. Mortensen, Dale T, 1982. "Property Rights and Efficiency in Mating, Racing, and Related Games," American Economic Review, American Economic Association, vol. 72(5), pages 968-979, December.
    8. Jean Roch Donsimoni & René Glawion & Bodo Plachter & Klaus Wälde & Constantin Weiser, 2020. "Should Contact Bans Have Been Lifted More in Germany?," CESifo Economic Studies, CESifo Group, vol. 66(2), pages 115-133.
    9. Pissarides, Christopher A, 1985. "Short-run Equilibrium Dynamics of Unemployment Vacancies, and Real Wages," American Economic Review, American Economic Association, vol. 75(4), pages 676-690, September.
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    Cited by:

    1. Chen, Chinchih & Frey, Carl Benedikt & Presidente, Giorgio, 2023. "Disease and democracy: Political regimes and countries responsiveness to COVID-19," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 290-299.

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    More about this item

    Keywords

    Covid-19; number of tests; reported number of CoV-2 infections; (correcting the) bias; SIR model; unbiased epidemiological severity index;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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