by Keith Stewart © GraphCube | 07 September 2021

COVID Deaths Skyrocket in Peru
In May 2021, international news agencies began reporting alarming increases in COVID death rates in Peru. Some of these recent increases result from difficulties with infection reporting and other health-care system challenges inherent during a pandemic. While Peru struggles to understand the infection and to control the mortality rate, the new COVID lambda variant emerges, further driving up the death rate to the highest on the planet. In Peru the lambda variant is now responsible for 96% of all new COVID cases. As of June 2021, lambda infection spillover is now contributing to at least 30% of new COVID cases in neighboring Chile, 18% in Ecuador, and more than 26% in Argentina (see Chart #1). Researchers report that Lambda mutations involving immune response evasion could be an indication of potential virus spread.

Chart #1 – Lambda Variant Cases

At first glance, this increase in COVID death rate is perplexing. Early into COVID spread, Peru mandated a border lock-down and strict curfews on its citizens. A year-on, the usual pandemic culprits – overwhelmed hospitals, vaccine logistics difficulties, and socio-economic factors combined with poorly-understood effects of the Lambda variant – collectively play a role in the astonishingly high death rate. The World Health Organization (WHO) still only list lambda as a Variant of Interest (VOI). The United States has largely ignored lambda, focusing primarily on the delta variant explosion, despite lambda now showing up in Texas and South Carolina. So far, a cohesive tangible explanation for lambda spread in Peru and South America has yet to emerge.

Searching for Answers
In an effort to unravel the nature of the lambda variant spread, our team at GraphCube launched an update of our South American data assets first produced in 2016. We challenged ourselves with two goals:
  • Could we leverage our assets with recent COVID data to create a data fabric which we could overlay throughout the region?
  • Could our data fabric aid with understanding the COVID dynamics and the unfolding lambda characteristics?
To begin, we first created a data layer of socio-economic measures. Once the first layer was validated, we created additional data layers, iterating through available data assets until we had enough layers to begin weaving together a rudimentary data fabric. Here, in Table #1, are the initial data layers we created and the associated questions we hope to answer:

Table #1 – Data Layers
Data Layers Key Questions
Lambda Variant
COVID Deaths
What are the relationships between the lambda variant, COVID cases & deaths per country?
Income Levels
Vaccination Rates
Are income levels, population, urbanity, and vaccinations rates predictive of COVID infection, treatment, & deaths?
Combined Data Does the layering of available data provide insight about the dynamics of this pandemic?


Lambda Variant – COVID Cases – COVID Deaths
Analysis of Chart #1 indicates lambda emergence contributing to 27% of infections by mid-January in Peru. Just over two months later (by April 05), lambda is the dominant variant in Peru at 81% of cases and then in another month (by May 03) lambda peaks at 98% of all COVID cases.

Meanwhile, neighboring Chile began February with lambda accounting for 4% of COVID cases. In a little over two months, the lambda variant spread deeper into Chile and by April 19, lambda peaked at more than 40% of all new COVID cases.

In Argentina, Chile's eastern neighbor, lambda variant began to emerge in early April (about 2% of COVID cases) and within two months, (June 16) the lambda variant was identified in 27% of all new COVID cases throughout Argentina.

Ecuador, the least urbanized of these four cohort countries, has followed a similar trend of lambda variant emergence with cases peaking in mid-April and continuing through June with about 17% of all new COVID cases attributed to lambda.

Comparing the lambda data from Chart #1 to daily COVID death data from Chart #2 for the period between February – June 2021, several trends are both revealing and perplexing. In Peru, prior to increased vaccination rates (starting around the end of May), the COVID death rate increases in tandem with the increase in lambda cases. This can be expected as lambda approaches 90%+ of all new cases. What is unexpected is the speed and magnitude of lambda infection and daily death rates. For example, the population of Argentina is 27% greater than Peru, however the COVID daily deaths in Peru are between 200% – 300% higher than Argentina. This disparity in deaths is despite the fact that both nations are on a similar vaccination trajectory.

Even more contradictory, the infection rate per 1 million population is higher in Argentina compared with Peru (as discussed in later in Chart #4) yet the death rate in Peru is consistently 400% higher than that of Argentina through most of the first half of 2021. After mid-May and up until mid-June, there is a roughly 300% drop in daily deaths in Peru while lambda rates decrease only about 15% during this period. Alternatively, in Argentina, daily deaths continue to climb (50% – 80%) through late June along with Lambda increases of about 10% – 12%. By the very end of June, daily deaths start to slowly decline in Argentina.

Ecuador and Chile, each about half the population of Peru, both experienced similar lambda and daily death trends. Lambda variant increases move in tandem with daily deaths increases. However, the situation in Chile is a paradox. Starting in March, Chile embarked on a very aggressive vaccination push; within four months (by July) more than 60% of the population was fully vaccinated. Consequently, daily deaths were held between about a 10% – 15% increase through the middle of July, avoiding the large death spikes in most neighboring countries. However, during this time period, lambda cases in Chile remained constant between %30 – 35% of new COVID cases whereas in Ecuador lambda cases hovered in the 17% range, about half that of Chile.

Three key paradoxes of this disease persist: (1) how are the high rates of lambda variant in Peru related to high COVID mortality; (2) why do increases in COVID cases and the lambda variants seems largely unresponsive to increased vaccination rates in Argentina; and (3) what are the dynamics between high vaccination rates and COVID and lambda cases stabilized in Chile.

Chart #2 – COVID Daily Deaths & Vaccination %

Income – Population – Urbanity – Vaccinations
In comparing the broad economic fabric across South America (see Chart #3), we elected to use the Purchasing Power Parity (PPP) data to normalize a regional per capita income for each country in order to present a representative economic analysis.

For Venezuela (VE), the following data provisions apply.

VENEZUELA: the availability of economic and COVID data from Venezuela is inconsistent. We were unable to perform accurate analyses with these data sets. Accordingly, Venezuela data is included herein for informational purposes only.

Data in the Country and Regional Income - Chart #3 reveals a significant income difference among the three highest income countries compared to the three lowest income countries (see NOTE ➜). Specifically, the three high income countries - Argentina, Chile, and Uruguay - capture 200% – 300% more per capita income than their three low-income cohorts - Bolivia, Ecuador, and Peru. Both Brazil and Colombia trend very closely to the regional averages for PPP and NOM per-capita income.

Comparing Chart #3 income data with Table #2 urban population data (column #6), a positive correlation exists between the percent of population living in urban environments with a higher per-capita income. There is a slight deviation with Paraguay in terms of its higher than anticipated per-capita income based on its low urban %; this is expected given that Paraguay is the largest global exporter of electric energy, primarily from its two hydroelectric facilities. Electricity trans-border exports account for more than 20% of Paraguay's GDP and thus higher per-capita income.

In terms of COVID vaccination % (fully vaccinated) and per-capita income, again a generally positive correlation exists among South American counties with higher income countries more fully vaccinated. Later in the pandemic, an anomaly to this correlation exists with Argentina (high income) consistently underperforming, relative to its high-income cohorts, at below 30% fully vaccinated by late August. Conversely, Ecuador (low income) has surpassed its low-income cohorts at 40% fully vaccinated by late August.

Throughout May and the first half of June, the % of lambda variant in new COVID cases rises only for Argentina (270% increase) and Ecuador (225% increase). Yet, the 7-day averaged death rate increases 157% for Argentina while decreasing 42% for Ecuador despite a 2% higher vaccination rate for Argentina during this period.

The two highest income countries (Chile and Uruguay) have achieved the greatest regional vaccination rates (51% and 43%) and have managed to largely suppress death rates whereas their high-income cohort (Argentina) has a similar vaccination rate (8.4%) and death rate (201 per 100K) as its middle-income and higher-population cohort Colombia. Interestingly, Uruguay has the highest COVID case rate per million population in all of South America, as well as the highest % of urban population, yet the third lowest death rate. This could be a result of Uruguay's aggressive vaccination efforts beginning and June and faster access to health care services due to high urbanization.

Once again, Peru presents an anomaly to the data trends evident in other South American countries. In terms of income, population, urbanity, and vaccination rates, Peru clusters predictably between its lower-income and middle-income cohorts. However, in terms of lambda variant and death rates, Peru is off-the-charts ranging from 200% – 400% higher than any other South American country.

Chart #3 – Country & Regional Income

Table #2 – South America COVID & Socioeconomic Data
Country  Cases1 Death Rate2 Total Deaths % Vaccinated3 % Urban4 NOM5 PPP6 Population 
Argentina - AR95,40420191,4388%92%$9,890$22,99744,938,712
Bolivia - BO36,28314216,4145%70%$3,823$7,79011,428,245
Brazil - BR85,235240509,28212%87%$7,010$15,642210,147,125
Chile - CL79,46716831,79751%88%$12,612$23,45517,574,003
Colombia - CO78,968201102,63610%81%$6,744$16,26450,372,424
Ecuador - EC25,10412521,3777%64%$6,155$11,70117,684,536
Paraguay - PY57,28016712,0863%62%$6,230$15,0307,359,000
Peru - PE61,046595191,2868%78%$5,845$11,51632,824,358
Uruguay - UY103,8561535,41343%96%$17,819$24,5163,518,552
Regional (AverageA TotalT) 69,183A248A981,729T12.4%A84.3%A$7,407A$16,226A395,846,955T
1 2 3 4 5 6 7 8 9

1 Total COVID cases per 1 million population
2 Death Rate: Total COVID deaths per 100K population
3 % of population fully vaccinated
4 % of population living in an urban environment
5 Per-Capita Income – 2019/2020 (Nominal-globalized)
6 Per-Capita Income – 2019/2020 (PPP-localized)
A Regional Average
T Regional Total



Combined Data Layering
Building a Data Fabric
We decided to take a fresh approach analyzing regional COVID trends and patterns by configuring multiple data sets into a single data fabric. Our strategy was to analyze COVID dynamics that may provide deeper insight into the characteristics of the Lambda variant. We constructed a four-dimensional data configuration after numerous iterations of available data sets. To accomplish our configuration, we layered the following four components (see below and Table #3) into Bubble Chart #4:
COVID cases

Vaccination rates

COVID death rates

Urban population rates
 See Table #3 

Chart #4 Summary
Chart #4 is a South American data fabric without the Peru layer. The exclusion of Peru data allows for a more detailed regional cohort analysis (due to the skewing effects from the extreme death rate in Peru). However, for a complete data fabric and for comparison purposes, Chart #5 displays the entire data fabric including Peru.

Based on the Bubble Chart #4 plotted data, we can infer some general COVID-related trends. We present these trends as initial discussion points for further research into the Lambda variant and COVID variant mutations in particular.

First, there appear to be three distinct quadrants in the chart where countries are grouped: the first quadrant includes countries with the lowest % of urbanization and among the lowest COVID death rates (Paraguay, Bolivia, Ecuador), the second quadrant includes countries with the highest % urbanization and the highest COVID death rates (Argentina, Brazil, Colombia), and the third quadrant includes countries with the highest % of urbanization and relative lower COVID death rates (Uruguay and Chile).

Second, when we combine the above quadrant grouping with vaccination rates, several patterns are revealed. In particular, among the countries with the highest COVID infection rates (bubble size) and the highest % of urbanization (position on Y axis), the two countries with the highest vaccination rates (Uruguay and Chile) have the lowest COVID death rates. Alternatively, continuing with the highest % of urbanization, the three countries with the medium and lowest vaccination rates and among the highest COVID infection rates all have the highest COVID death rates (Argentina, Brazil, Colombia). Interestingly, of these three countries, Brazil has a slightly higher vaccination rate than its cohorts (Colombia and Argentina) and approximately the same COVID infection rates and % urbanization, yet Brazil is an outlier with a 20% higher COVID death rate. For the three countries (Paraguay, Bolivia, Ecuador) with the lowest % urbanization and among the lowest COVID infection rates and lowest death rates, it appears that the individual vaccination rates correspond tightly to the death rates. Among the three quadrant cohorts, Paraguay with the lowest vaccination rates (3.4%) has the highest death rate whereas Ecuador with the highest cohort vaccination rate (7.2%) has the lowest death rate with Bolivia between these two countries in terms on vaccination rates and death rates. Note that the COVID infection rate alone is not entirely predictive of the death rate.

Third, Peru presents an extreme case of possible COVID variant outcomes. The death rate in Peru from COVID is the highest in the world and far exceeds its continental neighbors. In terms of COVID infection rates, vaccination rates, urbanization %, and income, Peru should cluster with its cohorts Ecuador - Bolivia - Paraguay; Peru's death rate and Lambda % are anomalies that require action from research entities, health care providers, and the global community. As of this date, there is still no general consensus on the dynamics of the Lambda variant in Peru; the WHO still lists lambda as a Variant of Interest. Unlocking the specifics of COVID mutation is likely to be a powerful tool in combatting this and other similar viruses.

Fourth, while these trends require deeper analyses, rigorous statistical evaluation, and additional comparative and supporting data sets, the bubble chart may provide initial insight into the associative dynamics of the COVID pandemic. From the Bubble Chart data, we can ask further questions about the role of urbanization in the death rates associated with COVID. While it may seem intuitive that less dense population dynamics might play a role in COVID death rates, alternatively, it would seem that lower density could also mean less immediate access to health care facilities equipped to manage COVID cases and so the low-density death rate would be nullified. Additionally, even though the Bubble Chart did not include economic data, if we compare the high infection rates in Uruguay and Chile together with their lower death rates and higher vaccination rates, is it also statistically relevant that both Uruguay and Chile have the highest per-capita income in South America? For the case of Brazil, is the divergence away from its quadrant cohorts in terms of higher death rate the result of comparatively slow responses to the pandemic or inadequate health care delivery services or other logistical considerations? Additionally, when we examined population totals, we could see no direct correlations in trend dynamics. For example, Uruguay and Paraguay have the two lowest populations on the continent (excluding Suriname, Guyana, and French Guiana) but much different infection rates, vaccination rates, income, and yet similar death rates. What are the potential dynamics associated with this pattern?

Presentation Summary
Based on COVID and socio-economic data publicly available at the time of this article, there appear to be significant infection and death rate considerations associated with the Lambda variant, especially in Peru. As the global community conducts COVID pandemic forensics to learn more about this virus and its variants and mutation dynamics, other potential research considerations could include per-country health care spending, access to health care, vaccine delivery logistics, age cohorts per country, time-frame for COVID responses, infection growth dynamics and other socioeconomic considerations. This presentation has been an initial exercise in the process of reexamining data and engaging the global citizenry to become involved in understanding this pandemic and equipping humanity with the tools and knowledge to prepare for the next pandemic.

This article is in memory of my grandmother, born on September 07, 1906, who survived smallpox, diphtheria, and the 1918 Spanish Flu epidemic. My grandmother lived happily and productively until the age of 93. May we all be so lucky.

Table #3 – Chart Characteristics
Bubble Size: COVID cases per Million Population
Red Bubble: Less than 10% fully vaccinated
Yellow Bubble: Between 10% — 40% fully vaccinated
Green Bubble: Greater than 40% fully vaccinated
X-axis COVID deaths per 100,000 population
Y-axis % of population living in urban regions

Chart #4 – COVID Regional Analysis


Chart #5 – COVID Regional Analysis with Peru

Notes on Data & Procedures
For this online version, the Notes and Chart Legends are displayed inline wherever possible. Each chart includes more detailed information that you can view by moving your cursor over the cart elements. For Table #2 (South America COVID & Socioeconomic Data), columns are sortable by clicking on the column header to sort ascending and then clicking again to sort descending. Charts also include Help Icons in the bottom left margins to provide more information about the given chart; move your cursor over the Help icon to view information.

References & Disclaimer 1. PPP Income: WorldBank
2. Energy Data & Income): U.S. CIA World Factbook
3. Global COVID Data: Worldometer
4. Global COVID Data: Our World in Data
5. COVID Specific Data: US Center for Disease Control (CDC)
6. Income & Population Data: GraphCube

All conclusions and assumptions made in this article are solely those of the author. The author makes no recommendations for medical treatments, testing, vaccinations, protocols, or any other health procedures. As always, you should consult your health care provider for your health decisions. The author does not have any conflicts of interest in this article; funding is from a general interest data research fund solely provided by This article has not been peer-reviewed.

Get More Help
Click on the Create Help File button below to create a complete Help File for this document.

© GraphCube | 07 September 2021