Come Die a Million a Deaths Rise Again

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Open Access

Peer-reviewed

Research Article

Visualizing omicron: COVID-19 deaths vs. cases over fourth dimension

• Rima Arnaout

Visualizing omicron: COVID-xix deaths vs. cases over time

• Ramy Arnaout,
• Rima Arnaout

10

• Published: April 19, 2022
• https://doi.org/10.1371/journal.pone.0265233

Figures

Abstract

For nearly of the COVID-19 pandemic, the daily focus has been on the number of cases, and secondarily, deaths. The about recent wave was caused by the omicron variant, commencement identified at the end of 2021 and the ascendant variant through the showtime part of 2022. South Africa, one of the start countries to experience and report data regarding omicron (variant 21.K), reported far fewer deaths, even as the number of reported cases rapidly eclipsed previous peaks. However, as the omicron wave has progressed, time series show that it has been markedly dissimilar from prior waves. To more readily visualize the dynamics of cases and deaths, information technology is natural to plot deaths per million against cases per million. Dissimilar the time-series plots of cases or deaths that take become daily features of pandemic updates during the pandemic, which have time as the x-axis, in a plot of deaths vs. cases, time is implicit, and is indicated in relation to the starting point. Hither we nowadays and briefly examine such plots from a number of countries and from the world as a whole, illustrating how they summarize features of the pandemic in ways that illustrate how, in most places, the omicron wave is very different from those that came before. Code for generating these plots for any country is provided in an automatically updating GitHub repository.

Citation: Arnaout R, Arnaout R (2022) Visualizing omicron: COVID-xix deaths vs. cases over time. PLoS I 17(4): e0265233. https://doi.org/x.1371/journal.pone.0265233

Editor: Etsuro Ito, Waseda University: Waseda Daigaku, Nippon

Received: January 24, 2022; Accepted: February 24, 2022; Published: April xix, 2022

Copyright: © 2022 Arnaout, Arnaout. This is an open up access commodity distributed nether the terms of the Creative Commons Attribution License, which permits unrestricted utilise, distribution, and reproduction in whatever medium, provided the original author and source are credited.

Data Availability: Data and code are freely available via the rarnaout/Covidcycles repository (https://github.com/rarnaout/Covidcycles). Information is likewise available at the following: https://github.com/owid/covid-19-data/raw/master/public/information/jhu/biweeklycasespermillion.csv https://github.com/owid/covid-19-data/raw/master/public/data/jhu/biweeklydeathspermillion.csv https://ourworldindata.org/covid-vaccinations https://covariants.org/per-country https://www.gisaid.org/.

Funding: The authors (R.A. and R.A.) were funded by the NIH (1R01AI148747 and 1R01HL150394) and the Gordon and Betty Moore Foundation. The funders had no role in study pattern, data drove and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Visualization is an essential tool for summarizing and making sense of data. During the COVID-19 pandemic, fourth dimension-series plots of cases and deaths have become fixtures of news reports, social media posts, and dashboards [1–iii]. In time-series plots, the x-centrality is time and the y-axis is the variable of interest, for example daily new cases per 1000000 population. Information about a second variable of interest can be presented every bit a 2nd line plotted confronting the same x-axis, with or without a secondary y-centrality, but this is not the only way to nowadays two variables. For example, if the want is to depict the viewer's attention to the ratio of the 2 variables, the ratio can be plotted over time; however, the absolute magnitudes are lost if only the ratio is plotted.

An alternative visualization approach is to plot the two variables against each other as a scatterplot, one on the 10-axis, the other on the y-. In such a plot, time has no centrality of its own, but tin can be represented visually every bit a gradient of weight, width, or color along the line, and/or tin be conveyed using arrows or arrowheads along the line (similar to how vector fields are often displayed). Such plots are standard in the written report of dynamical systems, leading for case to the phase diagrams of differential equations [4, 5]. They are useful for drawing attending to commonalities and differences in the relationship between a pair of variables over time. In this spirit, nosotros plotted twenty-four hour period-by-day deaths vs. cases for the COVID-19 pandemic to date, to meet what such plots might illustrate about the pandemic, and possibly its about-term tendencies.

Materials and methods

Search

Image searches for "COVID-19 cases vs. deaths scatterplot" and simple variants thereof were performed on the Google, DuckDuckGo, and Brave search engines on January 10, 2022. For each search engine, the number of the first 20 results that (1) were plots of both cases and deaths (2) as a function of each other (as opposed to over fourth dimension) (3) at multiple time points was recorded.

Lawmaking

Plots were fabricated using Python (run across Availability beneath) and OmniGraffle vii.xix.2 on an Apple tree M1 MacBook Air (2021) and updates on GitHub.

Results

Few plots of deaths vs. cases over time during the COVID-19 pandemic

Although plotting ii variables against each other over fourth dimension is a well established exercise (equally mentioned in the Introduction), plotting deaths vs. cases over time appears to be uncommon during the COVID-19 pandemic (Tabular array one). Web searches revealed occasional plots of total deaths vs. full cases, but only two plot of deaths vs. cases over fourth dimension: ane for a one-month window from April 2020 posted by the user prograft to the COVID-xix Data Visualizations subreddit [8], and a 2d from an RStudio blog (titled "Not Useful") [ix, ten].

Plots of decease vs. cases illustrate country-specific COVID-19 waves

We plotted deaths per million vs. cases per one thousand thousand from the beginning of the pandemic until the time of writing, for 16 countries, colored by wave (please notation that both x- and y- axes may be scaled differently from graph to graph). Nosotros comment on several examples.

S Africa (Fig 1A).

The plot for Southward Africa conspicuously illustrates the main pattern of counterclockwise loops (Fig 1A). Each loop describes a wave of the pandemic. In each loop, cases rise, and so deaths ascent, then cases fall, and finally deaths fall. The peak number of cases is e'er to the right (the engagement characterization indicates the meridian for each wave). The wave ends with the population back virtually the origin of the plot, with few cases and few deaths. The waves occurred roughly every six months, with ii mid-summer and ii mid-wintertime peaks.

Fig ane. South Africa and Israel.

All plots are colored by wave. Waves are labeled past variant if a single known variant predominated. Date labels appear at the peak cases-per-million for each wave. Arrowheads indicate the menses of fourth dimension. Dotted lines in (a) are guides to the eye regarding the orientation of the loops, indicating the progressive flattening of the last three waves.

https://doi.org/10.1371/journal.pone.0265233.g001

South Africa's first wave (purple) reached its height number of cases on July xiv, 2020 (labeled). The beginning of the second wave (blue) coincided with the appearance of the SARS-CoV-two beta variant, and indeed beta dominated this wave, with its frequency in the population reaching its peak frequency (0.97) nigh coincident with the peak in cases. The third moving ridge (dark-green) was similarly dominated by the delta variant, and the fourth (blood-red) dominated by omicron. For the beta, delta, and now omicron waves, reported cases peaked effectually 4,000–5,000 cases per million.

Information technology is interesting to note that the blue (beta), green (delta), and cherry (omicron) loops get progressively flatter: this indicates falling case mortality, from 125 cases per million for beta to only around 25 cases per one thousand thousand for omicron. Thus in South Africa, the beta wave was deadlier per capita than the delta wave, fifty-fifty though the delta variant is known to be quite virulent and led to substantial bloodshed around the world. Besides, note that each wave reaches further to the right. Thus, over the concluding three waves, the virus has been de facto less virulent but more infectious.

It is tempting to attribute this design at least in office to herd immunity from natural infection and not vaccination, since just roughly a quarter of the Due south African population was fully vaccinated as of early January 2022. Otherwise, it is reasonable to look to have seen college mortality for the delta wave. Note that herd amnesty is unlikely to be the entire caption, since laboratory investigation has shown that omicron is inherently both less pathogenic and more transmissible than delta, regardless of a person's prior vaccination or infection status [10–13]. In general for any land, the caption for the dynamics observed in these plots is almost certainly similarly multifactorial, with reporting, vaccination status, prior covid infection, policy (eastward.g. lockdowns), demographics, and health status all contributing.

Israel (Fig 1B).

Israel, a more vaccinated state (64% fully vaccinated as of January 10, 2022, meaning they have received a unmarried-dose vaccine or both doses of a 2-dose vaccine), provides an interesting comparison to South Africa (Fig 1B). It as well had 4 main waves, with the 2d (bluish) outpacing the first (purple) in both cases and deaths. (Israel's second wave was caused by the alpha variant.) As in South Africa, Israel's delta wave (green) too had more daily cases per 1000000 but fewer deaths per million, and its fourth principal wave (ruby), the omicron wave, fix a new record for daily cases per million but without a notable ascension in deaths per million so far. The case peaks for Israel's previous main waves all came a week to a few months afterward South Africa's. Information technology remains to exist seen whether Israel's fourth wave volition follow the South African pattern, as it has so far.

Italy, Denmark, Sweden, and the United Kingdom (Fig 2).

These European countries testify variations on a different pattern from that seen in South Africa and State of israel: a highly deadly wave in the terminate of 2020 and the beginning of 2021, with a prominent double height visible as a loop inside a loop in Italia, the United Kingdom, and Sweden (Fig 2A, 2C and 2D). In all cases, this coincided with the blastoff variant displacing the previously predominant 20E/EU1 strain and ascension to near fixation. The similarity of the blueprint is notable given the policy differences among these countries in how to address the pandemic, especially in Sweden. For Sweden, the unusual spikiness of the January 2021 loop is likely a reporting antiquity.

In Denmark (Fig 2B), the abrupt inflection point as the curve turns from orange to red coincided with the rapid shift from the delta variant, a highly virulent strain, to omicron, which biomedical research suggests is less pathogenic [xiv, 15] despite antibody escape [16–xviii], especially in previously exposed/highly vaccinated populations [xviii, 19] (Denmark is lxxx% fully vaccinated), consequent with the South African experience. The plot for Denmark is thus interpreted equally a "loop-pulled-taut" around the inflection signal, with a somewhat like (though less pronounced) effect in Italian republic. This is consequent with the temporary autumn of deaths-per-million equally cases rise, before the rise again as the omicron loop continues. The slight uptick coincided with expansion of the omicron 21.L variant, which had reached a prevalence of approximately 50% past January 24, 2022 [seven].

Note that even with the compression of the x scale in these plots, relative to the plots for Due south Africa and Israel to a higher place, the dynamics—i.e. the loop patterns—of these European countries have been different. The reason for the compression is of course the keen number of omicron cases (cherry), which for each of these countries dwarfs the daily cases per meg seen previously. Note also that the y scales are fairly comparable. The density of the blackness dots indicates the omicron wave peaking. However, in every case it has begun according to a very unlike trajectory from the previous waves.

Germany and the netherlands (Fig 3).

These neighboring countries share the double loop of their young man European countries (Fig 2) only are notable for an boosted loop in the final months of 2021 and the outset of 2022 (cherry-red). In both cases, the delta variant withal accounted for betwixt half and 90% of cases through the period shown. The wave seen subsiding in these plots merged into the omicron wave, creating some other loop within a loop as the omicron moving ridge hits these countries. Compare to the plot for Denmark (Fig 2B).

Japan and South korea (Fig 4).

Japan (Fig 4A) barely completed a wave dominated by the alpha variant (blue) earlier entering its delta-variant wave (green). Although January 2022 reporting [7] for nearby South korea (Fig 4B) indicates it is still in its delta moving ridge, transitioning from delta 21I to delta 21J, the trajectory of its latest wave (blood-red) is consequent with omicron having brainstorm to have a major effect. Note the small absolute numbers in both these countries.

India and People's republic of china (Fig v).

The plots for the ii most populous countries look very different from each other. By the fourth dimension of this writing, Bharat's relatively prominent delta wave resolved (green) and its omicron wave had begun (carmine), not unlike many other countries (Fig 5A). Communist china, where the SARS-CoV-2 virus was first reported, has reported almost no cases or deaths per 1000000 since early in the pandemic (notation the axis scales), a notable outlier among the countries presented here (Fig 5B).

Australia and New Zealand (Fig half-dozen).

Australia (Fig 6A) is notable for depression case and expiry rates but high bloodshed per reported instance, visible in the steepness of the yellow loop, which was dominated by the delta variant (even on an uncompressed ten-calibration). In December 2021-January 2022, Commonwealth of australia decided omicron (red) would be uncontainable and changed from a strategy featuring lockdowns to one geared toward blunting the event on mortality, but its omicron loop, which peaked Jan eighteen, has been by far its worst to date. New Zealand (Fig 6B), the country with the smallest population in the countries discussed here (5.ane million people, vs. 5.8 1000000 for 2d-lowest Denmark), has reported very few cases and deaths per million throughout the pandemic, including a very pocket-sized delta wave (red). The choppiness of the line may exist due to these pocket-size accented numbers.

North America (Fig 7).

The plot for the The states (Fig 7A) illustrates a substantial blunting of the alpha wave (blue) relative to the previous wave (purple), but its delta moving ridge (green) was deadlier per instance, without the flattening seen for South Africa (Fig 1A). The loop for the delta wave was blunter but otherwise similarly shaped to the January, 2021 wave ix months before, indicating a like per-example fatality rate. Like Kingdom of denmark, Frg, and the Netherlands, the death rate remained loftier into the starting time of the omicron wave, but the additional death due to the omicron wave has been minimal and so far, and the omicron wave has peaked. Canada (Fig 7B) is similar except that its delta wave (green) is shallower than its blastoff wave (bluish), with the omicron wave starting time shallower notwithstanding, reminiscent of Due south Africa. The choppiness around the omicron peak may reflect abandonment of case reporting around the summit. The blunting of successive waves in Mexico (Fig 7C) behave an even stronger resemblance to that seen for South Africa and State of israel. Mexico reported being 56% fully vaccinated as of Jan 2022, with Cuba (Fig 7D) at 86%, Canada at 78%, and the United States at 62%.

The globe (Fig 8).

Worldwide the picture is of a devastating kickoff wave (purple), a slightly shallower second wave (blue), and blunted third wave (light-green), and very high cases at the showtime of the fourth wave, dominated by omicron in virtually all countries visualized. New waves have striking every three to five months. Again, the distinctiveness of the omicron wave is clear: very high cases per 1000000 without a substantial rise in death charge per unit per million, at least as of this writing. The density of black dots in the heart of January strongly suggests that the omicron wave will acme worldwide by February 1, 2022.

Comparison to bivariate plots (Fig 9).

When visualized as univariate time series, the infection dynamics of Kingdom of denmark, Finland, and France appear like (summit two rows, plotted through mid-February 2022). In dissimilarity, the bivariate plots illustrate noun differences among the 3 countries (bottom row), most notably the curious clockwise cycle of Republic of finland's omicron wave (curved arrow), unique in the countries nosotros examined. Contrast to the counterclockwise cycles of Denmark, a fellow Nordic country, and France (curved arrows).

Discussion

The x-y scatterplot with time as an implicit variable is not a new invention. Withal, it has been little used in relation to cases and deaths in the COVID-19 pandemic. This communication may be considered a humble re-introduction, with cursory mention of some of the observations that such visualization can facilitate. The nearly hitting observation is the difference of the nascent omicron moving ridge to those that have come earlier, besides as the ease in visualizing unlike infection dynamics compared to univariate time serial plots. Too evident is a full general flattening of subsequent waves, following the initial outbreak in 2020, most evident in South Africa and Israel and to a bottom extent in Canada and Japan. Finally, regional differences and other patterns are clearly visible.

Given the history of this type of plot (for example, its utility in modeling pressure-volume or flow-volume dynamics for cardiopulmonary systems), it is interesting that it has been little used and so far in the pandemic. We speculate that the main reason is that because the waves have taken identify on the relatively slow timescale of months, and there have been relatively few of them to against which to see patterns ("small n"), their potential utility did not emerge until long afterwards time series had become fixtures of COVID-xix reporting. However, going forward at that place are a number of variations that may show interesting. These including sub-analysis by age, race, gender, and vaccination status. Substituting excess deaths per 1000000 in place of deaths per million is another example. It may likewise be fruitful to explore how the rich dynamics illustrated by these plots correlate with, or tin be predicted by, reporting, vaccination status, public policy (e.one thousand. lockdowns, worker compensation), and health condition.

We used biweekly cases and deaths, i.eastward. running averages, to meliorate visualize trends without the noisiness and sampling granularity of daily reporting (e.grand. weekend dips). The reader's attending has been called to the differences in scale betwixt the plots, which is sometimes worthy of note. The plots could likewise have been presented on the aforementioned calibration, although differences in rates of testing and reporting, known to differ beyond countries, would have to be taken into account for fair conclusions to exist drawn.

In conclusion, plotting deaths per 1000000 vs. cases per million over time, with appropriate annotation, is a potentially useful way to visualize waves of infection in the COVID-19 pandemic.

Acknowledgments

This work would not have been possible without adept data collection and curation at all levels, together with the selfless delivery to openness and sharing that resulted in this data being publicly available.

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