Range of reproduction number estimates for COVID-19 spread

Biochem Biophys Res Commun. 2021 Jan 29;538:253-258. doi: 10.1016/j.bbrc.2020.12.003. Epub 2020 Dec 9.


To monitor local and global COVID-19 outbreaks, and to plan containment measures, accessible and comprehensible decision-making tools need to be based on the growth rates of new confirmed infections, hospitalization or case fatality rates. Growth rates of new cases form the empirical basis for estimates of a variety of reproduction numbers, dimensionless numbers whose value, when larger than unity, describes surging infections and generally worsening epidemiological conditions. Typically, these determinations rely on noisy or incomplete data gained over limited periods of time, and on many parameters to estimate. This paper examines how estimates from data and models of time-evolving reproduction numbers of national COVID-19 infection spread change by using different techniques and assumptions. Given the importance acquired by reproduction numbers as diagnostic tools, assessing their range of possible variations obtainable from the same epidemiological data is relevant. We compute control reproduction numbers from Swiss and Italian COVID-19 time series adopting both data convolution (renewal equation) and a SEIR-type model. Within these two paradigms we run a comparative analysis of the possible inferences obtained through approximations of the distributions typically used to describe serial intervals, generation, latency and incubation times, and the delays between onset of symptoms and notification. Our results suggest that estimates of reproduction numbers under these different assumptions may show significant temporal differences, while the actual variability range of computed values is rather small.

Keywords: COVID-19; Generation time; Renewal equation; Reproduction number; SEIR-Based model.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Basic Reproduction Number
  • COVID-19 / epidemiology*
  • COVID-19 / transmission*
  • Humans
  • Models, Statistical
  • Stochastic Processes