Discussion – Key Debates, Viewpoints & Interviews

Theorising – The Social Definition of the Corona Pandemic Sandra Maria Pfister
Theorising – Praise of Biopolitics? The Covid-19 Pandemic and the Will for Self-Preservation Jörn Ahrens
Theorising – Problematising Categories: Understanding the Covid-19 Pandemic through the Sociology of Risk and Uncertainty (RN22) Patrick Brown
Theorising – Crises? What Crises? Conceptualising Breakdowns in Practice Theory Deborah Giustini
Theorising – If We Lose Our Humanity, We Lose Ourselves Mirjana Ule
Theorising – “It’s (Not) the End of the World as We Know It and I (Don’t) Feel Fine”: Through the Looking Glass Mirror of the Coronapocalypse Victor Roudometof
Working – Somewhere Over the Rainbow: Pandemic, Equal Pay and the Sociologist as Expert Hazel Conley
Working – Fashion in the Time of Corona: What Can the Sociology of Fashion Reveal? Anna-Mari Almila
Working – Work Disruption in a Context of Pandemics: Social Bonds and the ‘Crisis Society’ (RN17) Claudia Marà
Gendering – Coronavirus (Covid-19) and Femicide Shalva Weil
Gendering – Budgeting Gender Equality: The Israeli Central Bank and Finance Ministry, and the Covid-19 Crisis Orly Benjamin
Gendering – Be Safe, Take Care: On the Matters of a (Feminist) Pandemic Ellie Walton
Living – Overcoming the Unsouled City Carlos Fortuna
Living – Cities in Lockdown: A Few Comments on Urban Decline and Revival under the Covid-19 Pandemic Maciej Kowalewski
Living – Six Researchers in Search of A Meaning In Lockdown: A Collective Essay (RN03) Lyudmila Nurse
Living – Irony: One of the Italian Ways to Cope with Pandemic Fear and Isolation? Marta Fanasca
Living – Home Confinement and Deterioration of Social Space: Quasi-Ethnographic Notes from Córdoba Jorge Ruiz Ruiz
Masking – “I Wear My Mask for You” - A Note on Face Masks Annerose Böhrer
Masking – Corona-Masquerade, or: Unmasking the New Sociology of Masks David Inglis
Masking – The Sick and the Masks Cornelia Mayr
Health, Illness and Medicine – Together Apart? Securing Health Amid Health Inequality During the Covid-19 Outbreak in Europe (RN16) Ellen Annandale
Health, Illness and Medicine – From AIDS to Coronavirus: Who has the Right to Care? Jaime García-Iglesias
Health, Illness and Medicine – Coronavirus News: What Do All Those Numbers Mean? (RN21) Kathrin Komp-Leukkunen
Health, Illness and Medicine – Ethical Principles versus Algorithms and AI Medical Biases in Pandemics Ana María López Narbona
Health, Illness and Medicine – The Double Exclusion of Older Adults During the Covid-19 Pandemic Alexander Seifert
Political Economy and Politics – Covid-19, Critical Political Economy, and the End of Neoliberalism? (RN06) Bernd Bonfert
Political Economy and Politics – It’s the End of the World... As We Know It: The Last Capitalist Pandemic? Mariano Féliz
Political Economy and Politics – The Corona-Shuttle: Arriving Mentally in the Anthropocene? Ludger Pries
Political Economy and Politics – Pandemic Diplomacy: Peace in our Time? (RN08) Ilan Kelman
Being Cosmopolitan and Anti-Cosmopolitan – The Covid-19 Pandemic as a Cosmopolitan Moment Peter Holley
Being Cosmopolitan and Anti-Cosmopolitan – The Complex Risks of Covid-19: The Demand to Move from the ‘Society of Normalisation’ to Global Medical Surveillance Sergey A. Kravchenko
Sociological Experiencing and Reflecting – Letter to a Godchild Clemence Fourton
Sociological Experiencing and Reflecting – The Covid-19 Emergency and the Sociological Memory Teresa Consoli
Sociological Experiencing and Reflecting – Contemplative Diary Krzysztof Tomasz Konecki
Sociological Experiencing and Reflecting – The Loss of World in Times of Corona Martin Repohl

Health, Illness and Medicine – Coronavirus News: What Do All Those Numbers Mean? (RN21)

Issue 45: Pandemic (Im)Possibilities vol. 1 Tue 2 Jun 2020

Kathrin Komp-Leukkunen, University of Helsinki, Finland
RN21 Quantitative Methods Co-coordinator 2019-2021

The newspapers are full of articles on the coronavirus pandemic. Many of us start our days by reading these articles to see how the pandemic developed while we were sleeping. However, the wealth of information can be overwhelming. We learn the numbers of new infections, recoveries, and deaths from across the world. The newspapers use these numbers to draw horror scenarios and praise improvements - and they sometimes contradict one another in what the numbers mean. Such contradictions prevent us from understanding the situation. This article helps to solve this problem. It explains some of the statistical principles behind the numbers. This knowledge allows us to form our own opinions on what the newspapers report.

Which Country Has More Cases?
A worrisome characteristic of the Coronavirus pandemic is its high number of infections. This number differs widely across countries, with some reporting hundreds of thousands of infections, while others are still in the double digits. There are many reasons for these country-differences, and one reason is a statistical one: the countries have different population sizes. If only relatively few people live in a country, then only a few can be infected. In contrast, if a large number of people live in a country, then many can be infected. To solve this problem, one can compare the number of infections in a country to the number of people living in that country. Percentages do exactly that. They describe how many out of every 100 people are affected.

Figure 1 illustrates this principle. This figure shows the situation in two countries: country A and country B. In each country, only one person is infected. This gives both countries the same number of infections. However, country B has a bigger population than country A. This gives both countries a different percentage of infections. Country A, with the small population, has an infection rate of 50 per cent; whereas country B, with the bigger population, has an infection rate of only 10 per cent. We need to keep this circumstance in mind when comparing countries in the pandemic. The higher the percentage, the wider the virus has spread.

Figure 1: Comparing infections across countries
Figure 1: Comparing infections across countries

How Fast is the Virus Spreading?
There are two ways to measure how fast the virus is spreading. First, you can simply count the cases. This count tells us how many people in a country got newly infected, admitted to hospital, died, or recovered in a day. This information is important to determine, for example, whether a country has enough hospital beds and intensive care places for those who are ill.

A second way of measuring how fast the virus is spreading is to calculate percentages. This approach tells us whether the virus is spreading quickly or slowly, and whether the speed with which it spreads changes. In other words, it can tell us whether the situation is getting better or worse. To do this, the percentage compares the number of new infections in a day to the total number of infections of the previous day.

Figure 2 illustrates this principle, using the example of an infection development over three days. On both days 2 and 3, the rate of new infections was 50 per cent. This number means that every 100 people in one day infected 50 new people. The figure highlights an important insight: the same percentage can represent a different number of new infections on different days! The reason is that the number of new infections in a day is always compared to the number of total infections on the previous day – and the number of total infections changes from day to day. To find out how fast the virus is spreading, have a look at the percentage and compare the percentages of two days. If the second day has a higher percentage than the first day, then the virus is spreading faster. However, if the second day has a lower percentage than the first day, then the virus is spreading slower. Measures such as social distancing, quarantines and lockdowns are working, and we are getting a grip on the situation.

Figure 2: The increase in infections from one day to the next
Figure 2: The increase in infections from one day to the next

Why Don’t the Numbers Go Down?
If the numbers of infections and deaths you read about get higher every day, then this is not necessarily a cause for alarm. It may simply be due to the way of calculating the numbers. The reason is that sometimes the numbers of infections, hospital admissions, deaths, and recoveries describe one day only, and sometimes they describe everything that happened since the beginning of the pandemic. The number of new infections in a day can be lower than the number of new infections on the previous day. However, the total number of infections since the beginning of the crisis cannot be lower than it was the previous day.

Figure 3 shows this difference. It illustrates how the total number of infections develops in a population over three days. This number increases every day, simply because it adds all the new cases to the total of the previous cases. The figure shows one of the pitfalls of this calculation: the number of total infections may go up, even when the number of new infections goes down. In the figure, there were 3 new infections on day 2, and only 1 new infection on day 3. However, the total number of infections reaches a new maximum every day. Therefore, the total number of infections, hospital admissions, deaths, or recoveries should not be used to judge how fast the virus is spreading. It can, by definition, only be the same or higher than it was the previous day. This circumstance makes for eye-catching newspaper headlines, but that is it. To find out how fast the virus is spreading, instead look at the percentage change from one day to another.

Figure 3: How the total number of infections develops
Figure 3: How the total number of infections develops

I hope those explanations help you understand what the newspaper discussions mean. Stay healthy!

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