2020
DOI: 10.1007/s00466-020-01880-8
|View full text |Cite
|
Sign up to set email alerts
|

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

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 reproducti… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

3
133
1

Year Published

2020
2020
2022
2022

Publication Types

Select...
5
3

Relationship

1
7

Authors

Journals

citations
Cited by 168 publications
(137 citation statements)
references
References 61 publications
3
133
1
Order By: Relevance
“…We assume that the symptomatic and asymptomatic contact rates are equal, β = β s = β a , and that the differences between symptomatic and asymptomatic transmission manifest themselves exclusively in the infectious periods C s = 1/γ s and C a = 1/γ a . For illustrative purposes, rather than using a dynamic contact rate of Gaussian random walk type as we have proposed in the main body of this manuscript, we adopt a smooth, monotonically decreasing dynamic contact rate of hyperbolic tangent type [43],…”
Section: Appendixmentioning
confidence: 99%
See 1 more Smart Citation
“…We assume that the symptomatic and asymptomatic contact rates are equal, β = β s = β a , and that the differences between symptomatic and asymptomatic transmission manifest themselves exclusively in the infectious periods C s = 1/γ s and C a = 1/γ a . For illustrative purposes, rather than using a dynamic contact rate of Gaussian random walk type as we have proposed in the main body of this manuscript, we adopt a smooth, monotonically decreasing dynamic contact rate of hyperbolic tangent type [43],…”
Section: Appendixmentioning
confidence: 99%
“…The copyright holder for this this version posted August 29, 2020. ; https://doi.org/10.1101/2020.05.23.20111419 doi: medRxiv preprint rate β t = 0.10 /days, the adaptation time t * = 18.61 days, and the adaptation speed T = 10.82 /days from the mean values across the 27 countries of the European union [43]. Similar to the main body of this manuscript, we select a latent period of A = 1/α = 2.5 days and a symptomatic infectious period of C a = 1/γ a = 6.5 days.…”
Section: Appendixmentioning
confidence: 99%
“…In our model, we incorporated the daily R t values in order to account for the crowd behavior, such as using face coverings, social distancing or sheltering in place. Although this is not very common in mathematical models of epidemiology, Buckman et al [31], Kiamari et al [32] and Linka et al [33] utilize the effective reproduction number R t in their compartmental SIR models as where β is the contact rate and 1/ γ is the infectious period, [31, 33].…”
Section: Mathematical Modelmentioning
confidence: 99%
“…However, it would be more flexible and accurate to use a time dependent measure computed from the daily reported cases, namely effective reproduction number R t or R ( t ), to catch the dynamics of the disease [28, 29, 30]. Indeed, the transmission rate can be written in terms of the effective reproduction number as a time dependent function in some SIR models [31, 32, 33].…”
Section: Introductionmentioning
confidence: 99%
“…For example, the static reproduction number which is used in traditional mechanistic model has been singled out as the common cause of model failure in COVID-19 modeling [30]. A more dynamic and flexible approach is called for [3], [30] and with this expectation in mind, we are witnessing a new generation of epidemic models that combine mechanistic models with a dynamic measure of epidemic parameters that is driven by statistics and machine learning [31], [32].…”
Section: Introductionmentioning
confidence: 99%