How personal experiences shaped risk judgments during COVID-19

Figures & data

Figure 1. Panel A: COVID-19 incidence in Germany across a two-year time period, starting in March 2020 (the onset of the pandemic in Germany). Vertical dashed lines mark key events: lockdowns, beginning of vaccination rollout, and vaccination progress. The numbers from 1 to 17 indicate the survey waves. Panel B: Average risk judgments in each of the 17 survey waves, plotted separately for ‘direct experience’ and ‘no experience’ of a COVID-19 infection. Error bars represent bootstrapped 95% CIs.

Figure 2. Average risk judgments in each of the 17 survey waves, plotted separately for (in)direct experience and no experience of COVID-19 infection. Error bars represent bootstrapped 95% CIs.

Figure 3. Proportions of participants with each category of experience across the 17 survey waves.

Figure 4. Panel A: Media mentions of ‘COVID*’ and ‘Corona*’ in major German print and online media outlets. Panel B: Sentiment analysis for positive and negative words found in Bild.de and Spiegel.de. Panel C: Google searches for ‘Corona*’ in Germany.

Figure 5. Risk judgments across seven age groups. Error bars represent bootstrapped 95% CIs.

Figure 6. Average fear ratings, plotted separately for ‘direct experience’ and ‘no experience’ of a COVID-19 infection. Error bars represent bootstrapped 95% CIs.

Table

Original German: Wie groß schätzen Sie das Risiko ein, dass Sie selbst oder Mitglieder Ihrer Familie sich künftig mit dem Corona-Virus anstecken? (0: ‘sehr klein’ − 10: ‘sehr groß’)

English translation: What is your estimate of the risk that you or members of your family will become infected with the coronavirus in the future? (0: ‘very small’ − 10: ‘very large’).

Table

Original German, waves 1–4: In vielen Ländern, darunter auch in Deutschland, sind Personen am sogenannten Corona-Virus erkrankt. Wurde bei Ihnen oder in Ihrem Umfeld eine Infektion festgestellt, gab es einen Verdacht auf Infektion oder keinen Hinweis auf eine Infektion?	English translation, waves 1–4: In many countries, including Germany, people have been infected with the coronavirus. Have you or anyone close to you had a confirmed infection, a suspected infection, or no indication of infection?
Original German, wave 5 onwards: Wurde bei Ihnen selbst oder in Ihrem Umfeld seit Ausbruch der Corona-Pandemie eine COVID-19 Infektion festgestellt?	English translation, wave 5 onwards: Have you or anyone close to you been diagnosed with a COVID-19 infection since the outbreak of the corona pandemic?

Table

Original German: Infektion festgestellt. Verdacht auf Infektion. Keine Hinweise auf Infektion. Trifft nicht zu/weiss nicht.

English translation: Confirmed infection. Suspected infection. No evidence of infection. Does not apply/don’t know.

Table

Original German: Bei mir selbst. In meiner Familie (Partner, Kind, Eltern, Geschwister). In meinem Freundes- und Bekanntenkreis. In dem Betrieb, in dem ich arbeite. In der Schule oder KITA meines Kindes. In meiner Nachbarschaft.

English translation: I was diagnosed. [direct] Someone in my family (partner, child, parents, siblings) was diagnosed. [family] In my circle of friends and acquaintances. [network] In my workplace, someone was diagnosed. [network] In my child’s school or Kindergarden someone was diagnosed. [network] In my neighborhood someone was diagnosed. [network]

Table

Original German: Angst davor, …	English translation: Fear, …
… dass ich selbst oder Mitglieder meiner Familie sich mit dem neuen Corona-Virus anstecken werden.	… that I or members of my family will get infected with the new Coronavirus.
… dass Deutschland immer häufiger von Wetterextremen wie Dürre, Hitzewellen oder Starkregen betroffen wird.	… that Germany will increasingly be affected by extreme weather events such as droughts, heat waves, or heavy rainfall.
… dass sich der politische Extremismus ausbreitet.	… that political extremism is spreading. rainfall.
… dass der Staat wegen der Schuldenlast der Corona-Krise dauerhaft Steuern erhöht oder Leistungen kürzt.	… that the state will permanently increase taxes or cut benefits due to the debt burden of the Corona crisis.
… dass der Klimawandel dramatische Folgen für die Menschheit hat.	… that climate change will have dramatic consequences for humanity.
… dass ich einen Unfall im Strassenverkehr erleide.	… that I will be involved in a traffic accident.
… dass ein schweres Naturereignis (wie zum Beispiel Starkregen, Überschwemmung oder Erdrutsch) das Gebäude, in dem ich wohne, stark beschädigt oder vollständig zerstört.	… that a natural disaster (such as heavy rainfall, floods, or landslides) will damage or destroy the building where I live.
… dass Deutschland bei der Digitalisierung hinterherhinkt.	… that Germany is lagging behind in digitalization.
… dass ich schwer an Krebs, einem Herzinfarkt oder einem Schlaganfall erkranke.	… that I will fall seriously ill with cancer, a heart attack, or a stroke.
… in ein Flugzeug zu steigen.	… of flying.
… dass ich persönlich einmal wirtschaftlich in Not gerate, zum Beispiel durch Verlust des Arbeitsplatzes oder von Erspartem oder aufgrund sonstiger Ereignisse.	… that I will experience economic hardship due to losing my job, my savings, or other events.
… dass die Folgeschäden eines schweren Naturereignisses (wie zum Beispiel Starkregen, Überschwemmung oder Erdrutsch) mich finanziell überfordern.	… that the consequences of a natural disaster (such as heavy rainfall, floods, or landslides) will cause me financial hardship.

Table

Original German: Für wie groß halten Sie die Wahrscheinlichkeit, dass das neue Corona-Virus bei Ihnen bis Ende dieses Jahres eine lebensbedrohliche Erkrankung auslöst? (0: ‘überhaupt nicht wahrscheinlich’ − 100: ‘absolut wahrscheinlich’)

English translation: How likely do you think it is that the new corona virus will cause you a life-threatening illness by the end of this year? (0: ‘not at all likely’ − 100: ‘absolutely likely’)

Figure A1. Fear of a life-threatening disease by age group. Error bars represent bootstrapped 95% CIs.

Table A1. Demographic characteristics of participants (N = 15,015).

Characteristic	n	%
Gender
Female	7,690	51.2
Male	7,325	48.8
Age groups (in years)
18–25	1,671	11.1
26–35	2,288	15.2
36–45	2,308	15.4
46–55	2,676	17.8
56–65	3,041	20.3
66–75	2,448	16.3
76–85	580	3.9
School-leaving qualification
Primary school or lower-track secondary school	2,671	17.8
Intermediate-track secondary school	7,112	47.4
College-track secondary school	5,024	33.5
Left school without qualifications	100	0.7
Still in school	108	0.7

Notes. The school-leaving qualification was translated from the following German school levels in brackets: Primary school or lower-track secondary school [Haupt- oder Volksschul-Abschluss]; Intermediate-track secondary school [Mittlere Reife oder Abschluss der polytechnischen Oberschule]; College-track secondary school [Abitur, Fachhochschulreife (Gymnasien oder erweiterte Oberschule)]; Left school without qualifications [Von der Schule abgegangen ohne Schulabschluss]; Still in school [Noch in Schulausbildung].

Table A2. Number of participants per wave.

Wave	Sample size	Percentage of participants in wave x and wave 1
1	11,200	NA
2	10,823	89
3	10,560	89
4	10,415	89
5	10,942	89
6	10,150	88
7	10,115	89
8	10,034	89
9	10,028	87
10	9,813	88
11	9,586	88
12	9,590	86
13	9,260	87
14	9,122	87
15	9,234	87
16	2,211	86
17	2,072	86

Note: The last column indicated the percentage of participants in a given wave who are also participating in wave 1, e.g. 89% of participants from wave 2 also participated in wave 1.

Table A3. Multilevel model predicting risk judgment by experience of infection (two-way split: direct or no experience).

	riskperception			riskperception
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.25	0.16 — 0.35	<0.001	1.01	0.60 — 1.43	<0.001
Infection [no experience]	−0.26	−0.28 — −0.23	<0.001	−0.25	−0.28 — −0.22	<0.001
Wave				−0.07	−0.11 — −0.03	0.001
Gender [female]				0.05	0.02 — 0.07	<0.001
Agegroup 26-35				−0.01	−0.07 — 0.05	0.830
Agegroup 36-45				0.03	−0.03 — 0.09	0.294
Agegroup 46-55				−0.02	−0.08 — 0.03	0.425
Agegroup 56-65				−0.13	−0.18 — −0.07	<0.001
Agegroup 66-75				−0.28	−0.34 — −0.22	<0.001
Agegroup 76-85				−0.37	−0.45 — −0.29	<0.001
Media				−0.10	−0.25 — 0.06	0.212
Google hits				0.14	0.02 — 0.25	0.024
Vaccination status				−0.10	−0.17 — -0.02	0.014
Covid cases				0.15	0.12 — 0.18	<0.001
Random Effects
σ^2	0.41			0.36
τ00	0.52 PNR_fix:agegroup			0.51 PNR_fix:agegroup
	0.08 wave:year_month			0.01 wave:year_month
τ11	0.17 PNR_fix:agegroup.infectionno experience			0.16 PNR_fix:agegroup.infectionno experience
ρ01	−0.32 PNR_fix:agegroup			−0.24 PNR_fix:agegroup
ICC	0.58			0.60
N	14196 PNR_fix			12412 PNR_fix
	7 agegroup			7 agegroup
	17 wave			12 wave
	22 year_month			13 year_month
Observations	138777			83326
Marginal R^2 / Conditional R^2	0.004 / 0.584			0.113 / 0.647

Note: Bold p-values indicate significant results on the 5% percent level.

Table A4. Multilevel model predicting risk judgment by experience of infection (four-way split: direct, family, network, or no experience).

	Risk perception			Risk perception
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.29	0.20 — 0.38	<0.001	1.02	0.62 — 1.43	<0.001
Infection contact [family experience]	−0.10	−0.12 — −0.07	<0.001	−0.12	−0.15 — −0.09	<0.001
Infection contact [network experience]	−0.22	−0.25 — −0.20	<0.001	−0.25	−0.28 — −0.22	<0.001
Infection contact [no experience]	−0.37	−0.40 — −0.35	<0.001	−0.36	−0.39 — −0.33	<0.001
Wave				−0.07	−0.11 — −0.03	<0.001
Gender [female]				0.05	0.02 — 0.07	<0.001
Agegroup 26-35				0.00	−0.06 — 0.06	0.986
Agegroup 36-45				0.05	−0.01 — 0.11	0.110
Agegroup 46-55				0.01	−0.05 — 0.06	0.793
Agegroup 56-65				−0.09	−0.14 — −0.03	0.002
Agegroup 66-75				−0.23	−0.28 — −0.17	<0.001
Agegroup 76-85				−0.30	−0.38 — −0.22	<0.001
Media				−0.09	−0.24 — 0.06	0.237
Google hits				0.14	0.02 — 0.25	0.021
Vaccination status				−0.10	−0.17 — −0.02	0.012
Covid cases				0.14	0.11 — 0.18	<0.001
Random Effects
σ^2	0.40			0.36
τ00	0.52PNR_fix:agegroup			0.51PNR_fix:agegroup
	0.07wave:year_month			0.01wave:year_month
τ11	0.15PNR_fix:agegroup.infection_contactfamily experience			0.17PNR_fix:agegroup.infectionno experience
	0.16PNR_fix:agegroup.infection_contactnetwork experience
	0.23PNR_fix:agegroup.infection_contactno experience
ρ01	−0.34PNR_fix:agegroup.infection_contactfamily experience			−0.29PNR_fix:agegroup
	−0.35PNR_fix:agegroup.infection_contactnetwork experience
	−0.37PNR_fix:agegroup.infection_contactno experience
ICC	0.59			0.59
N	14252PNR_fix			12412PNR_fix
	7agegroup			7agegroup
	17wave			12wave
	22year_month			13year_month
Observations	142344			83326
Marginal R^2 / Conditional R^2	0.014 / 0.592			0.122 / 0.644

Table A5. Multilevel model predicting risk judgments by media mentions and google searches.

	Risk perception
Predictors	Estimates	CI	p
(Intercept)	0.01	−0.00 — 0.02	0.160
Media	0.10	0.10 — 0.11	<0.001
Google hits	0.10	0.10 — 0.11	<0.001
Random Effects
σ^2	0.47
τ00 PNR_fix:agegroup	0.51
ICC	0.52
NPNR_fix	14371
Nagegroup	7
Observations	150124
Marginal R^2 / Conditional R^2	0.035 / 0.539