A demographic scaling model for estimating the total number of COVID-19 infections

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Understanding how widely COVID-19 has spread is critical information for monitoring the pandemic. The actual number of infections potentially exceeds the number of confirmed cases.

We develop a demographic scaling model to estimate COVID-19 infections, based on minimal data requirements: COVID-19-related deaths, infection fatality rates (IFRs), and life tables. As many countries lack IFR estimates, we scale them from a reference country based on remaining lifetime to better match the context in a target population with respect to age structure, health conditions and medical services. We introduce formulas to account for bias in input data and provide a heuristic to assess whether local seroprevalence estimates are representative for the total population.

Across 10 countries with most reported COVID-19 deaths as of 23 July 2020, the number of infections is estimated to be three [95% prediction interval: 2–8] times the number of confirmed cases. Cross-country variation is high. The estimated number of infections is 5.3 million for the USA, 1.8 million for the UK, 1.4 million for France, and 0.4 million for Peru, or more than one, six, seven and more than one times the number of confirmed cases, respectively. Our central prevalence estimates for entire countries are markedly lower than most others based on local seroprevalence studies.

The national infection estimates indicate that the pandemic is far more widespread than the numbers of confirmed cases suggest. Some local seroprevalence estimates largely deviate from their corresponding national mean and are unlikely to be representative for the total population.
Original languageEnglish
JournalInternational Journal of Epidemiology
Issue number6
Pages (from-to)1963–1971
Number of pages9
Publication statusPublished - 8 Dec 2020
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 5200 Other social sciences
  • COVID-19
  • infection
  • prevalence
  • bias assessment
  • local seroprevalence studies
  • indirect estimation

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