Pushkinskaya st. 43. office 10
Rostov-on-Don, Russia
e-mail: info@hjournal.ru 
tel. +7(863) 269-88-14

cubsEN (2)

Why Mandatory Mass Testing for COVID-19 is a Poor Policy

Why Mandatory Mass Testing for COVID-19 is a Poor Policy

Journal of Economic Regulation, , Vol. 11 (no. 4),

In this note I describe simple logic behind COVID-19 mass testing, which explains why any underlying policy is economically unsubstantiated. The application of basic probability theory shows that unless the testing accuracy is close to a hundred percent, even a small number of false positives introduces significant bias into random tests making them extremely unreliable, which is further aggravated by the presence of false negatives. For example, at 5% false positive rate, for a random person living in the USA without any symptoms or previous contact with infected people, the likelihood of actually having COVID-19 after testing positive is only 32.63%. This probability increases with lower false positive rates and higher infection rates. Still, at 3% false positive rate, a randomly selected person only in 12 states will have a probability higher than 50% (up to 56%) of having COVID-19 after testing positive. Assuming independence of tests, in some states (e.g. Vermont) a person who has no reason to suspect the disease may need to test a dozen times to make sure that he/she is actually sick.

Keywords: COVID-19; Medical Testing; Public Policy

  • Bechara, A., Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and economic behavior, 52(2), 336–372.
  • Bennett, D. J. (2009). Randomness. Harvard University Press.
  • Casscells, W., Schoenberger, A., Graboys, T. B. (1978). Interpretation by physicians of clinical laboratory results. New England Journal of Medicine, 299(18), 999–1001.
  • Cohen, A. N., Kessel, B. (2020). False positives in reverse transcription PCR testing for SARS-CoV-2. medRxiv.
  • Cosmides, L. (1989). The logic of social exchange: Has natural selection shaped how humans reason? Studies with the Wason selection task. Cognition, 31(3), 187–276.
  • De Martino, B., Kumaran, D., Seymour, B., Dolan, R. J. (2006). Frames, biases, and rational decision-making in the human brain. Science, 313(5787), 684–687.
  • Floresco, S. B., Onge, J. R. S., Ghods-Sharifi, S., Winstanley, C. A. (2008). Corticolimbic-striatal circuits subserving different forms of cost-benefit decision making. Cognitive, Affective, & Behavioral Neuroscience, 8(4), 375–389.
  • Joyce, J. (2003). Bayes’ theorem.
  • Lalkhen, A. G., McCluskey, A. (2008). Clinical tests: sensitivity and specificity. Continuing Education in Anaesthesia Critical Care & Pain, 8(6), 221–223.
  • Surkova, E., Nikolayevskyy, V., Drobniewski, F. (2020). False-positive COVID-19 results: hidden problems and costs. The Lancet Respiratory Medicine.
  • Thaler, R. H., Ganser, L. J. (2015). Misbehaving: The making of behavioral economics. New York: WW Norton.
  • West, C. P., Montori, V. M., Sampathkumar, P. (2020, June). COVID-19 testing: the threat of false-negative results. In Mayo Clinic Proceedings (Vol. 95, No. 6, pp. 1127–1129). Elsevier.
  • Winichakoon, P., Chaiwarith, R., Liwsrisakun, C., Salee, P., Goonna, A., Limsukon, A., Kaewpoowat, Q. (2020). Negative nasopharyngeal and oropharyngeal swabs do not rule out COVID-19. Journal of clinical microbiology, 58(5).
  • Xiao, A. T., Tong, Y. X., Zhang, S. (2020). False-negative of RT-PCR and prolonged nucleic acid conversion in COVID-19: rather than recurrence. Journal of medical virology.
Publisher: Ltd. "Humanitarian perspectives"
Founder: Ltd. "Humanitarian perspectives"
Online ISSN: 2412-6047
ISSN: 2078-5429