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Relationship of Test Positivity Rates with COVID-19 Epidemic Dynamics

Author

Listed:
  • Yuki Furuse

    (Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto 606-8501, Japan)

  • Yura K. Ko

    (Department of Virology, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan
    National Institute of Infectious Diseases, Tokyo 208-0011, Japan)

  • Kota Ninomiya

    (National Institute of Infectious Diseases, Tokyo 208-0011, Japan
    National Institute of Public Health, Wako 351-0197, Japan)

  • Motoi Suzuki

    (National Institute of Infectious Diseases, Tokyo 208-0011, Japan)

  • Hitoshi Oshitani

    (Department of Virology, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan)

Abstract

Detection and isolation of infected people are believed to play an important role in the control of the COVID-19 pandemic. Some countries conduct large-scale screenings for testing, whereas others test mainly people with high prior probability of infection such as showing severe symptoms and/or having an epidemiological link with a known or suspected case or cluster of cases. However, what a good testing strategy is and whether the difference in testing strategy shows a meaningful, measurable impact on the COVID-19 epidemic remain unknown. Here, we showed that patterns of association between effective reproduction number (Rt) and test positivity rate can illuminate differences in testing situation among different areas, using global and local data from Japan. This association can also evaluate the adequacy of current testing systems and what information is captured in COVID-19 surveillance. The differences in testing systems alone cannot predict the results of epidemic containment efforts. Furthermore, monitoring test positivity rates and severe case proportions among the nonelderly can predict imminent case count increases. Monitoring test positivity rates in conjunction with the concurrent Rt could be useful to assess and strengthen public health management and testing systems and deepen understanding of COVID-19 epidemic dynamics.

Suggested Citation

  • Yuki Furuse & Yura K. Ko & Kota Ninomiya & Motoi Suzuki & Hitoshi Oshitani, 2021. "Relationship of Test Positivity Rates with COVID-19 Epidemic Dynamics," IJERPH, MDPI, vol. 18(9), pages 1-10, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:4655-:d:544668
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    References listed on IDEAS

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    1. David Adam, 2020. "A guide to R — the pandemic’s misunderstood metric," Nature, Nature, vol. 583(7816), pages 346-348, July.
    2. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    Cited by:

    1. Cristhian Leonardo Urbano-Leon & Manuel Escabias, 2022. "Comparison of Positivity in Two Epidemic Waves of COVID-19 in Colombia with FDA," Stats, MDPI, vol. 5(4), pages 1-11, October.

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