| Autor: |
Childs ML; Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, CA 94305, USA., Kain MP; Department of Biology, Stanford University, Stanford, CA 94305, USA.; Natural Capital Project, Woods Institute for the Environment, Stanford University, Stanford, CA 94305, USA., Harris MJ; Department of Biology, Stanford University, Stanford, CA 94305, USA., Kirk D; Department of Biology, Stanford University, Stanford, CA 94305, USA.; Department of Zoology, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4., Couper L; Department of Biology, Stanford University, Stanford, CA 94305, USA., Nova N; Department of Biology, Stanford University, Stanford, CA 94305, USA., Delwel I; Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA., Ritchie J; Department of Computer Science, Stanford University, Stanford, CA 94305, USA., Becker AD; Department of Biology, Stanford University, Stanford, CA 94305, USA., Mordecai EA; Department of Biology, Stanford University, Stanford, CA 94305, USA. |
| Abstrakt: |
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 ([Formula: see text]) remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number ([Formula: see text]) 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. |
