Fig 4 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Acceleration index for different age groups, May 13-October 25, 2020. In each panel, the blue line reproduces panel (b) in Fig 3, the dashed horizontal line represents when acceleration index equals 1, while the orange line corresponds to the age group specific to that panel. Data source: Agence Santé Publique France. https://doi.org/10.1371/journal.pone.0252443.g004
Source publication
An acceleration index is proposed as a novel indicator to track the dynamics of COVID-19 in real-time. Using data on cases and tests in France for the period between the first and second lock-downs—May 13 to October 25, 2020—our acceleration index shows that the pandemic resurgence can be dated to begin around July 7. It uncovers that the pandemic...
Contexts in source publication
Context 1
... Fig 4, we report the evolution over time of our acceleration index for nine age groups, all aggregated over space. In each panel, the blue line is a replica of panel (b) in Fig 3. ...
Context 2
... start with the former groups, the middle row in Fig 4 shows that people aged 29 to 58 experience acceleration in much the same way as the average over all age groups. This is not the case for younger and older groups. ...
Context 3
... fact that the share of confirmed cases is the second highest (22.9%) for the [20][21][22][23][24][25][26][27][28][29] age group (see Table 1 in A.3 in S1 Appendix for incidence rates among age groups) may contribute to the view that youngsters such as college students have contributed a lot to the resurgence of the virus since last summer. This view does not agree with the data in Fig 4. The age group [0][1][2][3][4][5][6][7][8][9] has experienced ups and downs since last summer. In particular, the acceleration index has started to rise again quickly in early September across France, after it went down all the way to unity at the end of summer. ...
Context 4
... another reason may be that younger kids have again been in more contact with their grand-parents, who take care of them some of the time while parents are working since the beginning of the school year. Yet in Fig 4, it is striking that the acceleration index for the age group [69-] has risen importantly since the beginning of August, to attain the highest level at the end of the data sample, around 4! A similar, though slightly less strong rise, can be seen for people aged [59][60][61][62][63][64][65][66][67][68]. Hence maybe it is not the young kids who infect their grand-parents, but it is the other way around? ...
Similar publications
Background
Most studies have focused on overweight/obesity and its secular trend, with insufficient studies on the factors influencing thinness and trends recently. To examine the trends of prevalence and sociodemographic determinants of thinness, overweight, and obesity among Chinese children and adolescents aged 7 to 18 years from 2010 to 2018....
Background
The Spring Festival is one of the most important traditional festivals in China. This study aimed to estimate the mortality risk attributable to the Spring Festival.
Methods
Between 2013 and 2017, daily meteorological, air pollution, and mortality data were collected from 285 locations in China. The Spring Festival was divided into thre...
Este artigo tem por objetivo geral traçar um perfil da judicialização da saúde na Comarca de Viçosa/MG. O trabalho tem natureza qualitativa e se baseia em dados secundários, coletados por meio de pesquisa documental. Examinou-se processos ajuizados entre 2017 e 2023 segundo características relacionadas ao autor do processo, à representação jurídica...
Objectives
The purpose of this study is to describe the genetic variants present in the Ecuadorian population and the incidence and mortality patterns of thyroid cancer in Ecuador from 2016 to 2021.
Methods
The present research constitutes a nationwide cross-sectional study encompassing all reported cases of thyroid cancer (C-73) in Ecuador from 2...
Citations
... In [4][5][6][7], we have introduced an alternative and novel measure of viral spread in the context of COVID-19-the acceleration index. This measure considers the variation of cases relative to the variation of tests and thus avoids the shortcomings and addresses the important question mentioned above. ...
... Our task here is to relate the acceleration index defined in [5,6] and the reproduction number that is estimated using the time-since-infection approach just described. The main purpose of this section is to derive a theoretical relationship between both concepts, which helps both to explain why they are different, to give a sense of the magnitude of their difference, and to state the conditions under which they are equivalent. ...
... As stressed in [6], accurate information about the dynamics of a pandemic rests on both the number of cases and the number of tests, and the former cannot be properly understood without the latter. In that paper, we introduce an acceleration index, denoted ε T at date T, which is an elasticity that measures the proportional responsiveness of cases with respect to tests. ...
We provide a novel way to correct the effective reproduction number for the time-varying amount of tests, using the acceleration index (Baunez et al., 2021) as a simple measure of viral spread dynamics. Not correcting results in the reproduction number being a biased estimate of viral acceleration and we provide a formal decomposition of the resulting bias, involving the useful notions of test and infectivity intensities. When applied to French data for the COVID-19 pandemic (May 13, 2020-October 26, 2022), our decomposition shows that the reproduction number, when considered alone, characteristically underestimates the resurgence of the pandemic, compared to the acceleration index which accounts for the time-varying volume of tests. Because the acceleration index aggregates all relevant information and captures in real time the sizable time variation featured by viral circulation, it is a more parsimonious indicator to track the dynamics of an infectious disease outbreak in real time, compared to the equivalent alternative which would combine the reproduction number with the test and infectivity intensities.
... In other words, R t cannot distinguish between regimes of increasing or decreasing acceleration/deceleration, as it depends only on the acceleration itself. Other ways of monitoring the evolution of an epidemic in terms of acceleration parameters have been recently discussed in the literature (Utsunomiya et al. 2020;Baunez et al. 2021;Rebhan 2021). These alternative metrics have the advantage that they can be obtained directly from the epidemic curves, but they also share the limitation with R t that they are based either solely on the growth acceleration or, at most, on its first derivative (Utsunomiya et al. 2020). ...
In this work we introduce a novel methodology to classify the dynamical stages of an epidemic, based on the different acceleration regimes of the corresponding growth curve. Our classification scheme is implemented by fitting the empirical data with a general class of mathematical growth models, from which we compute not only the growth acceleration but also its jerk and jounce (i.e., the first and second derivatives of the acceleration, respectively), thus allowing for a finer distinction of the epidemic stages. Using this methodology, we analyze the cumulative curves of deaths attributed to COVID-19 in the 26 Brazilian States and the Federal District, up until August 21, 2020. The online application ModInterv COVID-19, which automatically implements the classification scheme and which can be accessed via an internet browser or a mobile app, was used to investigate the epidemic stages in each of the Brazilian federal units. The analysis revealed that almost all states in the Northern and Northeastern regions were already in the saturation phase, meaning that the epidemic was relatively under control, whereas in all Southern states and in most states in the Midwest the epidemic was still accelerating or showed only a slight deceleration. The Southeastern region presented a great diversity of epidemic stages, with each state being found at a different stage, ranging from acceleration to saturation. It is argued that understanding this heterogeneous geographical distribution of the epidemic is relevant for public health authorities, as it may help in devising more effective strategies against the COVID-19 pandemic in a continental country like Brazil.
... Existing vaccines against SARS-CoV-2 currently on the market in Western societies are said to have a high efficacy in reducing the number of symptomatic cases, and also in cutting transmission (e.g. is believed to spread faster than other variants" (Planas et al. 2021) and the US-based Centers for Disease Control and Prevention (CDC) indicates for example that fully vaccinated people with the Delta variant can spread the virus, although it appears for a shorter period of time. 1 It is therefore of great importance for any public health authority to be able to observe correctly the viral spread, including among the vaccinated population. ...
... An alternative approach is to monitor whether the pandemic is decelerating or accelerating, that is, whether harm (here represented as the number of cases) is accelerating or decelerating (Taleb 2012 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint In Baunez et al. [1] we propose an acceleration index, which is the percentage change in cases given a percentage change in tests. The intuition is that if we increase tests by, say, one percent and find more than one percent of cases, we infer that the pandemic is accelerating. ...
Objectives: This note provides an assessment of COVID-19 acceleration among groups with difference
vaccine status in France.
Methods: We assess viral acceleration using a novel indicator introduced in Baunez et al. (2021).
The acceleration index relates the percentage change of tests that have been performed on a given
day to the percentage change in the associated positive cases that same day. We compare viral
acceleration among vaccinated and unvaccinated individuals in France over the period May 31st -
August 29, 2021.
Results: Once the state of the epidemic within each groups is accounted for, it turns out that
viral acceleration has since mid-July converged to similar levels among vaccinated and unvaccinated
individuals in France, even though viral speed is larger for the latter group compared to the former.
Conclusion: Our results call for an increasing testing effort for both vaccinated and unvaccinated
individuals, in view of the fact that viral circulation is currently accelerating at similar levels for
both groups in France.
