The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models

Mathematical models of epidemics are important tools for predicting
epidemic dynamics and evaluating interventions. Yet, because early models
are built on limited information, it is unclear how long they will
accurately capture epidemic dynamics. Using a stochastic SEIR model of
COVID-19 fitted to reported deaths, we estimated transmission parameters
at different time points during the first wave of the epidemic
(March–June, 2020) in Santa Clara County, California. Although our
estimated basic reproduction number (R0) remained stable from early April
to late June (with an overall median of 3.76), our estimated effective
reproduction number (RE) varied from 0.18 to 1.02 in April before
stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model
accurately predicted dynamics through June; however, the model did not
predict rising summer cases after shelter-in-place orders were relaxed in
June, which, in early July, was reflected in cases but not yet in deaths.
While models are critical for informing intervention policy early in an
epidemic, their performance will be limited as epidemic dynamics evolve.
This paper is one of the first to evaluate the accuracy of a nearly
epidemiological compartment model over time to understand the value and
limitations of models during unfolding epidemics.