1.6 Estimation of R₀, R_E and R_T

As R₀ is by definition a measure of disease spread in a totally susceptible population, and since such populations rarely exist, estimating R₀ for a disease often presents a challenge. Measurement of R_E is conceptually simpler, because it can be done empirically by measuring new infections.

For uncommon infections such as Ebola, it is possible to directly identify how many people became infected from a single index case. While the numbers are usually small, it gives a direct measure of spread in a defined population where all individuals are susceptible. Also, in the case of Ebola, all infected contacts of the index case develop symptoms and can be identified. Consequently asymptomatic cases do not cause underestimation of R₀.

The R₀ value for COVID-19 could also be estimated from the incidence of new infections. In the earliest stages of the COVID-19 pandemic, virtually everyone was susceptible, so R₀ was similar to R_E. However, at this time accurate data depended on the availability of reliable tests and screening programmes. In the early stages of the COVID-19 pandemic, testing for infection by PCR was patchy and selective. Moreover, as COVID-19 epidemics developed in different countries, people voluntarily reduced their level of social contacts and public health measures were introduced to limit spread, ie effective contacts were reduced. This means that the most reliable direct estimates of R₀ for SARS-CoV2 come from the period before these behavioural changes and public health policies came into effect.

For endemic infections, which have reached a steady state in a population, it is possible to estimate R₀ indirectly, but this is beyond the scope of this course.