Research Article Summary: Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions

Dehning, Jonas; Zierenberg, Johannes; Spitzner, F. Paul; Wibral, Michael; Jp, Neto; Wilczek, Michael; Priesemann,

doi:10.1101/2020.04.02.20050922

Cited by 88 publications

(63 citation statements)

References 43 publications

(66 reference statements)

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“…6a). This matches with estimations of the RKI [31] and findings by other groups which are also currently studying the situation in Germany [9,22]. This naturally results in a significant uncertainty of the projection since small deviations of the parameters can make the difference between further growing active case numbers or slowly declining numbers.…”

Section: Ld Partial Economic and Social Activitiessupporting

confidence: 87%

The impact of current and future control measures on the spread of COVID-19 in Germany

Barbarossa

Fuhrmann

Meinke

et al. 2020

Preprint

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The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020 [37]. In view of most recent information on testing activity [32], we present here an update of our initial work [4]. In this work, mathematical models have been developed to study the spread of COVID-19 among the population in Germany and to asses the impact of non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended here to account for undetected infections, as well as for stages of infections and age groups. The models are calibrated on data until April 5, data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases and reduced contact to risk groups.

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Section: Ld Partial Economic and Social Activitiessupporting

confidence: 87%

The impact of current and future control measures on the spread of COVID-19 in Germany

Barbarossa

Fuhrmann

Meinke

et al. 2020

Preprint

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“…Linking back to the potential of NPI to control COVID19 until vaccination or medicine is available we close with an observation relating to a recent study [8]. Our analyses covered the same time span as Dehning et al [8] and we also picked up a systematic pattern of contact reduction which we interpret as a weekly cycle (see Statistical modeling of trends and weekly oscillations). Indeed, the dates of the interventions analyzed by Dehning et al are confounded with day of the week; the three dates refer to three Sundays in March.…”

Section: Discussionsupporting

confidence: 59%

Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics

Engbert

Rabe

Kliegl

et al. 2020

Preprint

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Newly emerging pandemics like COVID-19 call for better predictive models to implement early and precisely tuned responses to their deep impact on society. Standard epidemic models provide a theoretically well-founded description of dynamics of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which challenges containment strategies, in particular, due to delayed epidemic responses to non-pharmaceutical interventions. However, spatial heterogeneity questions the adequacy of modeling epidemic outbreaks on the level of a whole country. Here we show that sequential data assimilation of a stochastic version of the standard SEIR epidemic model captures dynamical behavior of outbreaks on the regional level. Such regional modeling of epidemics with relatively low numbers of infected and realistic demographic noise accounts for both spatial heterogeneity and stochasticity. Based on adapted regional models, population level short-term predictions can be achieved. More realistic epidemic models that include spatial heterogeneity are within reach via sequential data assimilation methods.

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“…Here, σ represents the width of the likelihood p(D(t) | ϑ) between the time-varying reported populationD(t) and the simulated affected population D(t, ϑ). We adopt a Student's t-distribution for the likelihood between the data and the model predictions [8,24] with a confirmed case number-dependent width,…”

Section: Machine Learningmentioning

confidence: 99%

The reproduction number of COVID-19 and its correlation with public health interventions

Linka

Peirlinck

Kuhl

2020

Preprint

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Throughout the past four 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 and uncertainty quantification. During the early outbreak, the reproduction number was 4.5±21.4, with maximum values of 6.5 and 5.9 in Spain and France. As of today, it has dropped to 0.7±20.2, with minimum values of 0.4 and 0.3 in Austria and France. We found a strong correlation between passenger air travel and the reproduction number with a time delay of 12.6±22.7 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.

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Research Article Summary: Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions

Cited by 88 publications

References 43 publications

The impact of current and future control measures on the spread of COVID-19 in Germany

The impact of current and future control measures on the spread of COVID-19 in Germany

Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics

The reproduction number of COVID-19 and its correlation with public health interventions

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