小柯机器人

德国马克斯普朗克研究所Viola Priesemann课题组利用干预模型揭示了新冠病毒传播管控策略的有效性。该研究于2020年5月15日在线发表于《科学》。

Title: Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions

Author: Jonas Dehning, Johannes Zierenberg, F. Paul Spitzner, Michael Wibral, Joao Pinheiro Neto, Michael Wilczek, Viola Priesemann

Issue&Volume: 2020/05/15

Abstract: Abstract As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyze the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detect change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we can quantify the effect of interventions, and we can incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.

DOI: 10.1126/science.abb9789

Source: https://science.sciencemag.org/content/early/2020/05/14/science.abb9789