The reproduction number of COVID-19 and its correlation with public health interventions
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ORIGINAL PAPER
The reproduction number of COVID-19 and its correlation with public health interventions Kevin Linka1 · Mathias Peirlinck1 · Ellen Kuhl1 Received: 4 May 2020 / Accepted: 6 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Throughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22 ± 1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67 ± 0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24 ± 2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health. Keywords COVID-19 · Epidemiology · SEIR model · Reproduction number · Machine learning
1 Motivation Since the beginning of the new coronavirus pandemic in December 2020, no other number has been discussed more controversially than the reproduction number of COVID-19 [36]. Epidemiologists use the basic reproduction number R0 to quantify how many new infections a single infectious individual creates in an otherwise completely susceptible population [13]. The public media, scientists, and political decision makers across the globe have started to adopted the basic reproduction number as an illustrative metric to explain and justify the need for community mitigation strategies and political interventions [21]: An outbreak will continue for R0 > 1 and come to an end for R0 < 1 [25]. While the concept of R0 seems fairly simple, the reported basic
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Ellen Kuhl [email protected] Kevin Linka [email protected] Mathias Peirlinck [email protected]
1
Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
reproduction number for COVID-19 varies hugely depending on country, culture, calculation, stage of the outbreak [36]. Knowing the precise number of R0 is important, but challenging, because of limited data and incomplete reporting [12]. It is difficult–if not impossible–to measure R0 directly [50]. The earliest COVID-19 study that followed the first 425 cases of the Wuhan outbreak via direct contact tracing reported a basic reproduction number of 2.2 [33]. However, especially during the early stages of the outbreak, information was limited because of i
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