Tracking the Covid-19 pandemic : Simple visualization of the epidemic states and trajectories of select European countries &amp; assessing the effects of delays in official response

Kevorkian, A.; Grenet, T.; Gallee, H.

doi:10.1101/2020.03.14.20035964

Cited by 12 publications

(14 citation statements)

References 3 publications

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“…Their finding was that lockdown is a government's second best option, after testing. As regards a cross-country perspective, there are comparisons between different patterns of the virus [7], but, to the best of our knowledge, no empirical evidence on the impact of lockdown measures has been provided. Using a qualitative approach, Cruz and Dias [8] investigate COVID-19 in China, Italy, Brazil, and the US, suggesting that "not all relevant actions were taken, in a timely manner, to efficiently address the spread of COVID-19".…”

Section: Introduction and Research Questionmentioning

confidence: 99%

The Efficacy of Lockdown Against COVID-19: A Cross-Country Panel Analysis

Alfano

Ercolano

2020

Appl Health Econ Health Policy

427

386

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Background There has been much debate about the effectiveness of lockdown measures in containing COVID-19, and their appropriateness given the economic and social cost they entail. To the best of our knowledge, no existing contribution to the literature has attempted to gauge the effectiveness of lockdown measures over time in a longitudinal crosscountry perspective. Objectives This paper aims to fill the gap in the literature by assessing, at an international level, the effect of lockdown measures (or the lack of such measures) on the numbers of new infections. Given this policy's expected change in effectiveness over time, we also measure the effect of having a lockdown implemented over a given number of days (from 7 to 20 days). Methods We pursue our objectives by means of a quantitative panel analysis, building a longitudinal dataset with observations from countries all over the world, and estimating the impact of lockdown via feasible generalized least squares fixed effect, random effects, generalized estimating equation, and hierarchical linear models. Results Our results show that lockdown is effective in reducing the number of new cases in the countries that implement it, compared with those countries that do not. This is especially true around 10 days after the implementation of the policy. Its efficacy continues to grow up to 20 days after implementation. Conclusion Results suggest that lockdown is effective in reducing the R0, i.e. the number of people infected by each infected person, and that, unlike what has been suggested in previous analyses, its efficacy continues to hold 20 days after the introduction of the policy.

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Section: Introduction and Research Questionmentioning

confidence: 99%

The Efficacy of Lockdown Against COVID-19: A Cross-Country Panel Analysis

Alfano

Ercolano

2020

Appl Health Econ Health Policy

427

386

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“…The logarithm transformation facilitates comparisons of large and small outbreaks; it demonstrates the fundamental similarity of the epidemic process independent of population size, without scale-related distortions that affect classic epidemic curves. While this method is not specific to any particular disease, it was described independently by several teams in the context of the COVID-19 pandemic, including the authors of this paper, and the authors of the site Covid Trends [3,7].…”

Section: Discussionmentioning

confidence: 99%

“…Finally, whether these plots may help in forecasting epidemic trajectories is unclear. In a previous analysis that used the plot of log(N) as a function of log(C) applied to cases of COVID-19 in Italy in March 2020, the power law predicted correctly the number of new cases recorded during the following week, before the lockdown effect became visible [3]. To what extent this method can be successfully generalized is currently not established.…”

Section: Limitationsmentioning

confidence: 99%

“…In this general case the plot of log(N(t)) over log(C(t)) is not constrained to be a straight line with unit slope. However straight lines, when applicable, represent distinctive growth patterns of epidemics, i.e., the timedependence of C(t) [3]. A straight line with slope s indicates a power law relation of the type N(t) = k C(t) s ; this implies that each time C(t) increases two-fold or tenfold, N(t) is multiplied by 2 s or 10 s .…”

Section: A) Logarithm Of New Daily Events As a Function Of The Logarimentioning

confidence: 99%

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Alternative graphical displays for the monitoring of epidemic outbreaks, with application to COVID-19 mortality

Perneger

Kevorkian

Grenet

et al. 2020

BMC Med Res Methodol

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Background Classic epidemic curves – counts of daily events or cumulative events over time –emphasise temporal changes in the growth or size of epidemic outbreaks. Like any graph, these curves have limitations: they are impractical for comparisons of large and small outbreaks or of asynchronous outbreaks, and they do not display the relative growth rate of the epidemic. Our aim was to propose two additional graphical displays for the monitoring of epidemic outbreaks that overcome these limitations. Methods The first graph shows the growth of the epidemic as a function of its size; specifically, the logarithm of new cases on a given day, N(t), is plotted against the logarithm of cumulative cases C(t). Logarithm transformations facilitate comparisons of outbreaks of different sizes, and the lack of a time scale overcomes the need to establish a starting time for each outbreak. Notably, on this graph, exponential growth corresponds to a straight line with a slope equal to one. The second graph represents the logarithm of the relative rate of growth of the epidemic over time; specifically, log10(N(t)/C(t-1)) is plotted against time (t) since the 25th event. We applied these methods to daily death counts attributed to COVID-19 in selected countries, reported up to June 5, 2020. Results In most countries, the log(N) over log(C) plots showed initially a near-linear increase in COVID-19 deaths, followed by a sharp downturn. They enabled comparisons of small and large outbreaks (e.g., Switzerland vs UK), and identified outbreaks that were still growing at near-exponential rates (e.g., Brazil or India). The plots of log10(N(t)/C(t-1)) over time showed a near-linear decrease (on a log scale) of the relative growth rate of most COVID-19 epidemics, and identified countries in which this decrease failed to set in in the early weeks (e.g., USA) or abated late in the outbreak (e.g., Portugal or Russia). Conclusions The plot of log(N) over log(C) displays simultaneously the growth and size of an epidemic, and allows easy identification of exponential growth. The plot of the logarithm of the relative growth rate over time highlights an essential parameter of epidemic outbreaks.

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“…In the first months of 2020, the pandemic first-waves spread of the coronavirus disease 2019 (COVID- 19) has affected most of the countries worldwide [1]. This disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first reported in end 2019 in Hubei province, China [2], [3].…”

Section: -Introductionmentioning

confidence: 99%

Monitoring the propagation of COVID-19-pandemic first waves

Knafo

2020

Preprint

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A phenomenological approach is proposed to monitor the propagation of the COVID-19pandemic first waves. A large set of data collected at a worldwide scale during the first months of 2020 is compiled into series of semi-logarithmic plots, for a selection of thirty-two countries from the five continents. Three regimes are identified in the propagation of an epidemic wave: a pre-epidemic regime 1, an exponential-growth regime 2, and a resorption regime 3. A two-parameters scaling of the first-wave death variation reported in China is used to fit those reported in other countries. Comparison is made between the propagation of the pandemic in different countries, which are classified in four groups, from group A where the pandemic first waves were contained efficiently, to group D where the pandemic first waves widely spread. Group A is mainly composed of Asian countries, where fast and efficient measures have been applied. Group D is composed of Western-Europe countries and the United States of America, where late decisions and confused political communication (pandemic seriousness, protection masks, herd immunity etc.) led to significant death tolls. The threat of large resurging epidemic waves after a hasty lockdown lift is discussed, in particular for the countries from group D, where the number of contagious people remained high in the beginning of May 2020. The situation is opposite in Asian countries from group A, where the number of contagious people was successfully maintained to a low level. In particular, the results obtained by Hong Kong and South Korea are highlighted, and the measures taken there are presented as virtuous examples that other countries may follow.

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Tracking the Covid-19 pandemic : Simple visualization of the epidemic states and trajectories of select European countries & assessing the effects of delays in official response

Cited by 12 publications

References 3 publications

The Efficacy of Lockdown Against COVID-19: A Cross-Country Panel Analysis

The Efficacy of Lockdown Against COVID-19: A Cross-Country Panel Analysis

Alternative graphical displays for the monitoring of epidemic outbreaks, with application to COVID-19 mortality

Monitoring the propagation of COVID-19-pandemic first waves

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