Computing the Prevalence and Severity of an Epidemic Using only the Distribution of Simple Tests for Infection Confirmation

  1. Yuval Shahar
  2. Osnat Mokryn

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Abstract

Epidemics and Pandemics such as COVID-19 require estimating total infection prevalence. Accurate estimates support better monitoring, evaluation of proximity to herd immunity, estimation of infection fatality rates (IFRs), and assessment of risks due to infection by asymptomatic individuals, especially in developing countries, which lack population-wide serological testing.

We suggest a method for estimating the infection prevalence by finding the Pivot group , the population sub-group with the highest susceptibility for being confirmed as positively infected. We differentiate susceptibility to infection , assumed to be uniform across all population sub-groups (a key assumption), from susceptibility to developing symptoms and complications , which differs between sub-groups (e.g., by age). We compute the minimal infection-prevalence factor by which the number of positively confirmed patients should be multiplied that allows for a sufficient number of Pivot-group infections that explains the number of Pivot group confirmations.

We applied the method to the COVID-19 pandemic, using UK and Spain serological surveys. Our key assumption held, and actual infection-prevalence factors were consistent with our predictions. We computed minimal infection-prevalence factors, and when possible, assessed IFRs and serology-based IFRs, for the COVID-19 pandemic in eight countries.

Estimating a lower bound for an epidemic’s infection prevalence using our methodology is feasible, and the assumptions underlying it are valid. The use of our methodology is often necessary for developing countries, especially in the early phases of an epidemic when serological data are not yet available or when new mutations of a known virus appear.

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    SciScore for 10.1101/2021.05.04.21256588: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.05.04.21256588 on medRxiv