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 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.

Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19

“…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],…”

“…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.…”

Assessment of Vaccination and Underreporting on COVID-19 Infections in Turkey Based On Effective Reproduction Number

“…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].…”

“…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].…”

Memory-Dependent Forecasting of COVID-19: The Flexibility of Extrapolated Kernel Least Mean Square Algorithm

“…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].…”