The researchers compared the results of three related mathematical models of disease transmission that they used to analyze data emerging from local and national governments, including one that measures the dynamic reproduction number — the average number of susceptible people infected by one previously infected person. The models all highlight the dangers of relaxing public health measures too soon.
“Distancing efforts that appear to have succeeded in the short term may have little impact on the total number of infections expected over the course of the pandemic,” said lead author Andrea Bertozzi, a distinguished professor of mathematics who holds UCLA’s Betsy Wood Knapp Chair for Innovation and Creativity.
