Abstract
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 () remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number () 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 an early epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics.
Highlights
COVID-19, caused by the emerging virus SARS-CoV-2, rapidly expanded across the globe, overwhelmed healthcare systems, and has led to just under 4.0 million deaths with the pandemic still underway as of July 2021 [1]
We seek to answer the following retrospective questions: what did the model suggest about epidemic metrics, dynamics, and the impact of non-pharmaceutical interventions, and how did these estimates change over time? How accurately did the model predict epidemic dynamics going forward? For how long was the model accurate enough to be useful, and what limited its longer-term accuracy?
Many early COVID-19 models played a critical role in highlighting the importance of social distancing to governments and to the public (e.g. [2,3,4,5])
Summary
COVID-19, caused by the emerging virus SARS-CoV-2, rapidly expanded across the globe, overwhelmed healthcare systems, and has led to just under 4.0 million deaths with the pandemic still underway as of July 2021 [1]. In addition to limited information, unreliable data and model trade-offs, a hurdle in the use of models early in the COVID-19 epidemic was the rapidity and heterogeneity of policy changes (especially in locations where interventions varied at a local level, for example in the USA). We seek to answer the following retrospective questions: what did the model suggest about epidemic metrics, dynamics, and the impact of non-pharmaceutical interventions, and how did these estimates change over time? How accurately did the model predict epidemic dynamics going forward? For how long was the model accurate enough to be useful, and what limited its longer-term accuracy?
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