Computing a Lower Bound for the Total Size of the COVID-19 Infected Population Iceberg Using the General Age-Group Distribution

Shahar, Y.; Mokryn, O.

The COVID-19 pandemic has raised questions regarding the total prevalence of infection, referred to as the (Infection) Iceberg. Accurate estimates of country-specific Iceberg sizes support better pandemic monitoring, evaluation of proximity to herd immunity, estimation of infection fatality rates (IFRs), and assessment of risks due to infection by asymptomatic individuals. Previous suggestions included surveying the population for COVID-19 symptoms, and a few countries performed randomized serological testing. We suggest a new method for setting a lower bound on the Iceberg size, by finding the Pivot group, the population sub-group with the highest relative risk for being confirmed as positively infected in the RT-PCR test. We differentiate between susceptibility to infection, which we assume to be uniform across all population sub-groups, and susceptibility to developing symptoms and complications, which may differ between sub-group. Our key assumption is that the Pivot sub-group's proportion within the infected Iceberg is similar to its proportion within the population (or even lower). We compute the minimal Iceberg Factor that allows for a sufficient number of people from the Pivot sub-group within the presumed Iceberg to explain the portion of the Pivot group that is confirmed positive, and also the minimal Iceberg Factor when allowing for a statistically insignificant deviation from the population's Pivot group proportion. We demonstrate, using the UK and Spain serological surveys, that our key assumption holds, and that the actual Iceberg factors fit our predictions. We compute minimal Iceberg factors, and when possible, assessed IFRs and serology-based IFRs, for nine countries.