# Risk of SARS-CoV-2 infection in professional settings, shops, shared transport, and leisure activities in France, 2020–2022

Simon Galmiche, Tiffany Charmet, Arthur Rakover, Olivia Cheny, Faïza Omar, Christophe David, Alexandra Mailles, Fabrice Carrat, Arnaud Fontanet

► To cite this version:

Simon Galmiche, Tiffany Charmet, Arthur Rakover, Olivia Cheny, Faïza Omar, et al.. Risk of SARS-CoV-2 infection in professional settings, shops, shared transport, and leisure activities in France, 2020-2022. BMC Public Health, 2024, 24 (1), pp.2411. 10.1186/s12889-024-19651-y. pasteur-05272223

HAL Id: pasteur-05272223

https://pasteur.hal.science/pasteur-05272223v1

Submitted on 22 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH

Open Access

<figure><img src="image_2.png" /></figure>

Check for
updates

# Risk of SARS-CoV-2 infection in professional settings, shops, shared transport, and leisure activities in France, 2020–2022

Simon Galmiche<fnref n="1" />,<fnref n="2" />*, Tiffany Charmet<fnref n="1" />, Arthur Rakover<fnref n="1" />, Olivia Chény<fnref n="3" />, Faïza Omar<fnref n="4" />, Christophe David<fnref n="4" />, Alexandra Mailles<fnref n="5" />, Fabrice Carrat<fnref n="6" /> and Arnaud Fontanet<fnref n="1" />,<fnref n="7" />

**Abstract**

**Purpose** The aim of the study was to identify settings associated with SARS-CoV-2 transmission throughout the COVID-19 pandemic in France.

**Methods** Cases with recent SARS-CoV-2 infection were matched with controls (4:1 ratio) on age, sex, region, population size, and calendar week. Odds ratios for SARS-CoV-2 infection were estimated for nine periods in models adjusting for socio-demographic characteristics, health status, COVID-19 vaccine, and past infection.

**Results** Between October 27, 2020 and October 2, 2022, 175,688 cases were matched with 43,922 controls. An increased risk of infection was documented throughout the study for open-space offices compared to offices without open space (OR range across the nine periods: 1.12 to 1.57) and long-distance trains (1.25 to 1.88), and during most of the study for convenience stores (OR range in the periods with increased risk: 1.15 to 1.44), take-away delivery (1.07 to 1.28), car-pooling with relatives (1.09 to 1.68), taxis (1.08 to 1.89), airplanes (1.20 to 1.78), concerts (1.31 to 2.09) and night-clubs (1.45 to 2.95). No increase in transmission was associated with short-distance shared transport, car-pooling booked over platforms, markets, supermarkets and malls, hairdressers, museums, movie theatres, outdoor sports, and swimming pools. The increased risk of infection in bars and restaurants was no longer present in restaurants after reopening in June 2021. It persisted in bars only among those aged under 40 years.

**Conclusion** Closed settings in which people are less likely to wear masks were most affected by SARS-CoV-2 transmission and should be the focus of air quality improvement.

**ClinicalTrials.gov (03/09/2022)** NCT04607941.

**Keywords** SARS-CoV-2, Case-control studies, Infectious disease transmission, Occupational exposure, Workplace, Travel, Leisure activities

<footnote>*Correspondence:</footnote>

<footnote>Simon Galmiche
simon.galmiche@pasteur.fr</footnote>

<footnote n="1">¹Emerging Diseases Epidemiology Unit, Institut Pasteur, Université Paris
Cité, 25 rue du Docteur Roux, Paris 75015, France</footnote>

<footnote n="2">²Sorbonne Université, Ecole Doctorale Pierre Louis de Santé Publique,
Paris 75006, France</footnote>

<footnote n="3">³Clinical Research Coordination Office, Institut Pasteur, Université Paris
Cité, Paris 75015, France</footnote>

<footnote n="4">⁴Department of Public Affairs – Public Statistics, Institut Ipsos, Paris
75013, France</footnote>

<footnote n="5">⁵Santé Publique France, Saint-Maurice 94410, France</footnote>

<footnote n="6">⁶Sorbonne Université, Inserm, IPLESP, Hôpital Saint-Antoine, AP-HP,
Paris 75012, France</footnote>

<footnote n="7">⁷Unité PACRI, Conservatoire National des Arts et Métiers, Paris
75003, France</footnote>
**Introduction**

Identifying settings where transmission of SARS-CoV-2
occurs and quantifying their respective contribution has
been central to advise evidence-based mitigation strate-
gies such as social distancing policy, testing practices,
contact tracing, and information to the public [1]. As
the impact of the pandemic recedes in most countries,
knowledge on the settings of transmission can help guide
improvement in air quality and individual protection
approaches, particularly for elderly or immunocompro-
mised people. Furthermore, drawing all available infor-
mation from the SARS-CoV-2 pandemic is essential
to preparedness efforts: in case of emergence of a new
respiratory virus, knowledge derived from SARS-CoV-2
will support a timely and evidence-based public health
response.

Throughout the pandemic, numerous factors poten-
tially affecting where SARS-CoV-2 transmission may
occur have undergone significant changes, including
non-pharmaceutical interventions, vaccine coverage, or
the circulating strain.

Several study designs have been used to address this
question. Outbreak reports have generated crucial early
evidence, particularly in settings with low community
transmission, allowing accurate contact tracing [2–6].
Other studies have aimed to screen all contacts of a series
of cases and identify in which settings contacts were
more likely to result in transmission [7, 8]. These designs
often require the correct identification of contacts, which
can be difficult for SARS-CoV-2 in case of long-distance
airborne transmission [9] or superspreading event: a
study in Hong Kong reported that approximately 20% of
cases were responsible for 80% of secondary cases [10].
It is especially challenging in locations where unrelated
people interact closely, for instance in public transport.
Other studies have estimated the risk or odds ratios of
infection associated with different settings, through
cross-sectional seroprevalence estimates [11–13] or case-
control designs [14–17].

While many studies have identified the risk associated
with venues such as bars, night-clubs, or public trans-
port, none have provided a long-term outlook to assess
potential changes through the pandemic. In the present
study, which was conducted over a two-year period, we
used a case-control design to identify settings associ-
ated with the risk of SARS-CoV-2 infection in France and
assess how these evolved through the pandemic.

**Methods**

We conducted an online case-control study in mainland
France from October 2020 to October 2022. The meth-
ods of the study have been reported before [18–20]. We
included cases aged 18 and above with recently diag-
nosed SARS-CoV-2 infection reported in a national

information system: all cases diagnosed through RT-PCR
or rapid antigen tests were centralized by the national
health insurance system (Caisse nationale d'assurance
maladie, CNAM). The CNAM sent email invitations
to cases identified within the past week who had previ-
ously provided their email address (approximately 55%
of all people affiliated with the CNAM, who represent
about 89% of the population of mainland France). Both
RT-PCR and rapid antigen tests were available free of
charge without prescription for the whole duration of the
study. After providing consent, participants completed
an online questionnaire about sociodemographic infor-
mation, health status, household description, and recent
exposures of interest. The questions focused on the 10
days preceding the onset of the symptoms (or testing if
asymptomatic). This period was reduced to 7 days after
the emergence of the omicron variant given its shorter
incubation period [21]. Following the participation of
the cases, controls were enrolled by Ipsos, a market and
opinion research company, and matched with cases using
a frequency-matched procedure. Matching criteria were
age (18–29, 30–54, ≥55 years old), sex (male or female as
self-reported), region (largest administrative subnational
division), size of population in the area of residence,
and week of exposure to account for local transmission
dynamics.

We did not include potential cases and controls who
were under a legal status of curatorship or guardian-
ship at the time of participation. Until February 2021,
we included only controls without a past episode of
SARS-CoV-2 infection. Eligibility for controls was then
extended to people without ongoing SARS-CoV-2 infec-
tion. We excluded cases and controls reporting an epi-
sode of SARS-CoV-2 infection in the past two months
(other than the one leading to their participation for
cases). To limit recall errors, we excluded cases who filled
the questionnaire over 30 days after the onset of symp-
toms (or testing if asymptomatic). We allowed repeated
participation after at least two months since the last par-
ticipation from January 2022.

To study the evolution of the risk associated with the
exposures of interest over the course of the study, we
divided the study period into nine shorter periods, based
on incidence, important non-pharmaceutical interven-
tions (stay-at-home orders, curfews, sanitary pass, i.e.
a proof of COVID-19 vaccination, past infection, or a
recent negative test required to visit a series of places),
and the circulating strain (see supplementary methods
for further description of periods and non-pharmaceuti-
cal interventions throughout the study).

**Statistical analysis**

For a better matching of controls with cases on the tim-
ing of exposure, considering that controls were initially
included after the screening of cases, we proceeded to an exact matching procedure of four cases for one control on the calendar week of the outcome date (symptom onset or testing if asymptomatic for cases, questionnaire completion for controls). To account for the random selection of cases in the matching procedure, as those outnumbered controls more than four times, we generated 100 databases of series of four cases matched with one control for each period.

We fitted unconditional logistic regression models to estimate the odds ratios of SARS-CoV-2 infection for the exposures of interest in a model including the matching variables, as well as health-related variables, including COVID-19 vaccine status, past SARS-CoV-2 infection, sociodemographic characteristics (level of education, socio-professional category) and household description. The choice of the adjusting variables was guided by subject matter knowledge to include all measured causes of the exposures, the outcome (SARS-CoV-2 infection), or both, relying on the disjunctive cause criterion [22]. The exposures of interest were included as follows: workplace (work in an office, open-space arrangement, complete or partial remote office), gatherings (professional, private, or religious), retail settings (shops, hairdresser, beauty salon), shared transport (short-distance or long-distance transport, car-pooling), and leisure activities (cultural venues, sports facilities, bars, restaurants, parties, divided into night-clubs or private parties from period 5 onward). No estimate was produced for bars, restaurants, indoor sports facilities, and cultural venues for periods 2 and 3 as they were mostly closed then (odds ratios were estimated for the first period thanks to the inclusion of participants before the start of the stay-at-home orders).

These models were fitted for each of the 100 databases per period. We extracted the coefficients, exponentiated them and retained the median for the point estimate and the 2.5th and 97.5th percentiles for the 95% confidence interval. In complementary models, we explored potential interactions of bars, restaurants, and parties with age categorized as <40 or ≥40 years (p-value for interaction estimated with the median of the 100 estimates). To investigate how viral circulation in the country of destination could affect the risk associated with airplane travel, we calculated the mean daily incidence rate obtained from Ourworldindata.org in the country of destination over period 4 (summer of 2021, emergence of the delta variant) [23].

All statistical analyses were performed using Stata 16.0 (StataCorp, College Station, USA). (See supplementary methods for further description of statistical analysis.)

## Ethics approval

This study received ethics approval from the ethics committee Comité de Protection des Personnes Sud Ouest et

Outre Mer 1 on September 21, 2020 as required by French regulation on clinical research. The data protection authority, the Commission Nationale de l'Informatique et des Libertés (CNIL) authorized the processing of data on October 21, 2020. Informed consent was obtained from all participants.

This report follows the STROBE reporting guidelines for observational studies.

## Results

From October 27, 2020 to October 2, 2022, we sent 11,612,450 email invitations to people with recent SARS-CoV-2 infection, and included 691,454 cases (6.0%) and 57,065 controls. After exclusion of participants with a reported episode of infection in the past two months and cases who responded to the questionnaire over 30 days after symptom onset, and matching of four cases for one control, we included 175,688 cases and 43,922 controls (Fig. 1). The main socio-demographic and health status characteristics are summarized in Table 1. The study population was characterized by a lower proportion of men (33.8% vs. 47.6% in the general population aged 20 and over in France), a higher representation of people aged between 40 and 49 years (26.8% vs. 16.7%) and of residents of the Ile-de-France region (where Paris is located) (22.7% vs. 18.3%).

We identified several settings associated with an increased risk of infection, including professional settings, shops, shared transport, and leisure activities (Fig. 2, Tables S1-S3).

Regarding the workplace, we found a consistently increased risk associated with working in an open-space office compared with a non-open-space office environment (OR range through the nine periods of the study: 1.12 to 1.57). Remote office was associated with a decreased risk of infection when done only partially in the preceding days (0.72 to 0.90), but often not when the few days spent at the workplace were in an open-space environment (0.72 to 1.09). The risk varied through the study for people reporting working fully remotely, with an OR ranging between 0.67 and 1.64 depending on the period.

The visit of shops was overall not associated with any increased risk of infection. The only exceptions are convenience stores and take-away deliveries for which the risk remained increased through most of the study (periods 4 to 9, OR range: 1.15 to 1.44, and 1.07 to 1.28, respectively). Notably, we found no increased risk in retail facilities involving closer and longer contacts such as hairdressers or beauty salons.

Analyses on shared transport show that most short-distance transport such as buses, tramways or short-distance trains did not increase the risk of infection, except for the metro in which the risk was regularly moderately
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Flow chart of participant enrollment and matching of cases and controls. Legend: Study conducted in mainland France between October 2020 and October 2022</figcaption></figure>

increased (periods 3, and 7 to 9, OR range: 1.07 to 1.19). On the other hand, long-distance trains and airplanes were associated with a notably increased risk, consistently for train (1.25 to 1.88) and for most of the study for airplanes (periods 2 to 6, and 9, OR range: 1.20 to 1.78). The models were adjusted on abroad travel, suggesting that the effect for airplane was not mediated through visit to a high-incidence country. In a sensitivity analysis, the risk for airplane travel remained increased after adjustment on the mean incidence in the country of destination (period 4: OR 1.58, 95%CI 1.41–1.78, compared with OR 1.67, 95%CI 1.52–1.88 without adjustment). Car travels also appeared to favor transmission in certain circumstances, with increased risks for taxi rides throughout the study in the periods when they were investigated (periods 3 to 9, OR range: 1.08 to 1.89), as well as for car-pooling, but only when traveling with relatives (periods 4 to 9, OR range: 1.09 to 1.68), not when the car-pooling was organized with unrelated persons through a dedicated platform (periods 4 to 9, OR range: 0.45 to 0.59).

Of all the cultural and sports facilities we investigated, we found an increased risk mainly for the attendance of concerts (periods 5 to 9, OR range: 1.31 to 2.09), and less consistently for theatres (periods 6, 7, and 9, OR range: 1.20 to 1.45) and the practice of sports indoors (periods 1, 5, and 7, OR range: 1.11 to 1.23). We found no increased risk associated with other settings such as museums, movie theatres, swimming pools, or martial arts facilities.

We initially identified an increased risk associated with bars and restaurants (as the questionnaire did not

distinguish them at first): period 1, OR 1.97 (95%CI 1.84–2.07). As bars and restaurants reopened in the spring of 2021, we found an increased risk for bars (OR 1.57, 95%CI 1.50–1.64) but not for restaurants (OR 0.95, 95%CI 0.89–0.99). The risk gradually decreased afterward for bars, and they were no longer at risk from October 2021 (period 6) onward. However, there were significant interactions of bars with age categorized as under 40 years or 40 and above, with a persistently increased risk in those aged under 40 until the first omicron BA.1 wave (periods 4 to 7, OR range: 1.23 to 2.17) (Tables S5, S6). Attending parties was initially consistently associated with an increased risk, particularly in those aged under 40 (periods 1 to 4, OR range: 1.33 to 3.24) (Tables S4, S5). When we could distinguish private parties from parties in night-clubs, as those reopened in the summer of 2021, we found no increased risk for private parties (no interaction with age, Tables S5, S6). In contrast, the risk was high for night-clubs (e.g., in period 6, OR 2.95, 95%CI 2.64–3.28). It decreased gradually through the various omicron waves in 2022 but remained increased in the last period of the study (omicron BA.4/5 wave, OR 1.54, 95%CI 1.41–1.66), regardless of the age category (Tables S5, S6).

## Discussion

This case-control study provides a long-term perspective on the settings most associated with the risk of SARS-CoV-2 infection in mainland France between October 2020 and October 2022. We identified increased risks for
<table><caption>**Table 1** Socio-demographic description of the study population (case-control study in mainland France, October 2020 to October 2022)</caption><thead><tr><th></th><th>Cases, n (%)</th><th>Controls, n (%)</th><th>p-value</th></tr></thead><tbody><tr><td><strong>Total</strong></td><td><strong>175,688</strong></td><td><strong>43,922</strong></td><td></td></tr><tr><td><strong>Male sex</strong></td><td>59,380 (33.8)</td><td>14,845 (33.8)</td><td>&gt; 0.99</td></tr><tr><td><strong>Age (years)</strong></td><td></td><td></td><td></td></tr><tr><td>18-29</td><td>21,244 (12.1)</td><td>5311 (12.1)</td><td>&gt; 0.99</td></tr><tr><td>30-39</td><td>31,888 (18.2)</td><td>7972 (18.2)</td><td></td></tr><tr><td>40-49</td><td>47,032 (26.8)</td><td>11,758 (26.8)</td><td></td></tr><tr><td>50-59</td><td>37,960 (21.6)</td><td>9490 (21.6)</td><td></td></tr><tr><td>60-69</td><td>24,796 (14.1)</td><td>6199 (14.1)</td><td></td></tr><tr><td>≥ 70</td><td>12,768 (7.3)</td><td>3192 (7.3)</td><td></td></tr><tr><td><strong>Population in the area of residence</strong></td><td></td><td></td><td></td></tr><tr><td>&lt; 5000 inhabitants</td><td>44,828 (25.5)</td><td>11,207 (25.5)</td><td>&gt; 0.99</td></tr><tr><td>5000-19,999 inhabitants</td><td>13,984 (8.0)</td><td>3496 (8.0)</td><td></td></tr><tr><td>20,000-99,999 inhabitants</td><td>17,800 (10.1)</td><td>4450 (10.1)</td><td></td></tr><tr><td>Over 100,000 inhabitants</td><td>61,956 (35.3)</td><td>15,489 (35.3)</td><td></td></tr><tr><td>Greater Paris area</td><td>37,120 (21.1)</td><td>9280 (21.1)</td><td></td></tr><tr><td><strong>Region of residence</strong></td><td></td><td></td><td></td></tr><tr><td>Ile-de-France</td><td>39,848 (22.7)</td><td>9962 (22.7)</td><td>&gt; 0.99</td></tr><tr><td>Auverge-Rhône-Alpes</td><td>23,384 (13.3)</td><td>5846 (13.3)</td><td></td></tr><tr><td>Occitanie</td><td>17,860 (10.2)</td><td>4465 (10.2)</td><td></td></tr><tr><td>Provence-Alpes-Côte d'Azur and Corsica</td><td>15,788 (9.0)</td><td>3947 (9.0)</td><td></td></tr><tr><td>Grand Est</td><td>15,336 (8.7)</td><td>3834 (8.7)</td><td></td></tr><tr><td>Nouvelle-Aquitaine</td><td>15,132 (8.6)</td><td>3783 (8.6)</td><td></td></tr><tr><td>Hauts-de-France</td><td>14,800 (8.4)</td><td>3700 (8.4)</td><td></td></tr><tr><td>Pays de la Loire</td><td>8580 (4.9)</td><td>2145 (4.9)</td><td></td></tr><tr><td>Bretagne</td><td>8324 (4.7)</td><td>2081 (4.7)</td><td></td></tr><tr><td>Normandie</td><td>6124 (3.5)</td><td>1531 (3.5)</td><td></td></tr><tr><td>Bourgogne-Franche-Comté</td><td>5876 (3.3)</td><td>1469 (3.3)</td><td></td></tr><tr><td>Centre-Val de Loire</td><td>4636 (2.6)</td><td>1159 (2.6)</td><td></td></tr><tr><td><strong>Education level</strong></td><td></td><td></td><td></td></tr><tr><td>No diploma</td><td>3682 (2.1)</td><td>767 (1.7)</td><td>&lt; 0.001</td></tr><tr><td>Pre-high school diploma</td><td>25,995 (14.8)</td><td>7722 (17.6)</td><td></td></tr><tr><td>High-school diploma</td><td>31,741 (18.1)</td><td>10,559 (24.0)</td><td></td></tr><tr><td>Bachelor's degree</td><td>62,909 (35.8)</td><td>16,340 (37.2)</td><td></td></tr><tr><td>Master's degree or higher</td><td>45,149 (25.7)</td><td>7056 (16.1)</td><td></td></tr><tr><td>Missing</td><td>6212 (3.5)</td><td>1478 (3.4)</td><td></td></tr><tr><td><strong>Health conditions</strong></td><td></td><td></td><td></td></tr><tr><td>Diabetes mellitus</td><td>6157 (3.5)</td><td>2304 (5.2)</td><td>&lt; 0.001</td></tr><tr><td>Hypertension</td><td>20,476 (11.7)</td><td>5773 (13.1)</td><td>&lt; 0.001</td></tr><tr><td>Chronic respiratory disease</td><td>14,650 (8.3)</td><td>3132 (7.1)</td><td>&lt; 0.001</td></tr><tr><td>Coronary artery disease</td><td>2114 (1.2)</td><td>431 (1.0)</td><td>&lt; 0.001</td></tr><tr><td><strong>Body-mass index (kg/m²)</strong></td><td></td><td></td><td></td></tr><tr><td>Healthy weight (≥ 18.5 &amp; &lt;25)</td><td>89,392 (50.9)</td><td>20,909 (47.6)</td><td>&lt; 0.001</td></tr><tr><td>Underweight (&lt; 18.5)</td><td>5409 (3.1)</td><td>1880 (4.3)</td><td></td></tr><tr><td>Overweight (≥ 25 &amp; &lt;30)</td><td>52,824 (30.1)</td><td>13,111 (29.9)</td><td></td></tr><tr><td>Obesity (≥ 30)</td><td>28,061 (16.0)</td><td>8023 (18.3)</td><td></td></tr><tr><td><strong>Housing</strong></td><td></td><td></td><td></td></tr><tr><td>Individual house</td><td>106,786 (60.8)</td><td>25,775 (58.7)</td><td>&lt; 0.001</td></tr><tr><td>Apartment</td><td>68,223 (38.8)</td><td>18,004 (41)</td><td></td></tr><tr><td>Shelter</td><td>585 (0.3)</td><td>118 (0.3)</td><td></td></tr><tr><td>Nursing home</td><td>92 (0.1)</td><td>25 (0.1)</td><td></td></tr><tr><td><strong>Living with a child</strong></td><td></td><td></td><td></td></tr><tr><td>Attending daycare</td><td>4946 (2.8)</td><td>928 (2.1)</td><td>&lt; 0.001</td></tr></tbody></table>
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Total</th><th>Cases, n (%)</th><th>Controls, n (%)</th><th>p-value</th></tr></thead><tbody><tr><td></td><td>175,688</td><td>43,922</td><td></td></tr><tr><td>Attended for by a professional in-home caregiver</td><td>5772 (3.3)</td><td>904 (2.1)</td><td>&lt; 0.001</td></tr><tr><td>Attending preschool</td><td>16,467 (9.4)</td><td>3327 (7.6)</td><td>&lt; 0.001</td></tr><tr><td>Attending primary school</td><td>29,027 (16.5)</td><td>6185 (14.1)</td><td>&lt; 0.001</td></tr><tr><td>Attending middle school</td><td>26,795 (15.3)</td><td>6183 (14.1)</td><td>&lt; 0.001</td></tr><tr><td>Attending high school</td><td>21,990 (12.5)</td><td>5447 (12.4)</td><td>0.54</td></tr><tr><td>Attending university</td><td>14,711 (8.4)</td><td>4075 (9.3)</td><td>&lt; 0.001</td></tr><tr><td><strong>Past SARS-CoV-2 infection</strong></td><td></td><td></td><td></td></tr><tr><td>No past infection</td><td>165,798 (94.4)</td><td>37,376 (85.1)</td><td>&lt; 0.001</td></tr><tr><td>Past infection 61–180 days</td><td>8085 (4.8)</td><td>2968 (7.1)</td><td></td></tr><tr><td>Past infection &gt; 180 days</td><td>1805 (1.1)</td><td>3578 (8.5)</td><td></td></tr><tr><td><strong>COVID-19 vaccine, time since last dose</strong></td><td></td><td></td><td></td></tr><tr><td>Unvaccinated</td><td>49,989 (28.5)</td><td>13,503 (30.7)</td><td>&lt; 0.001</td></tr><tr><td>1 dose, &lt; 90 days</td><td>3288 (2.0)</td><td>870 (2.1)</td><td></td></tr><tr><td>1 dose, 90–179 days</td><td>552 (0.4)</td><td>260 (0.7)</td><td></td></tr><tr><td>1 dose, &gt; 179 days</td><td>615 (0.4)</td><td>264 (0.8)</td><td></td></tr><tr><td>2 doses, &lt; 90 days</td><td>7301 (4.9)</td><td>3024 (8.1)</td><td></td></tr><tr><td>2 doses, 90–179 days</td><td>13,718 (9.2)</td><td>3150 (8.5)</td><td></td></tr><tr><td>2 doses, &gt; 179 days</td><td>7254 (5.2)</td><td>2092 (6.0)</td><td></td></tr><tr><td>3 doses, &lt; 90 days</td><td>26,324 (17.7)</td><td>6794 (18.3)</td><td></td></tr><tr><td>3 doses, 90–179 days</td><td>40,047 (32.4)</td><td>6810 (22.0)</td><td></td></tr><tr><td>3 doses, &gt; 179 days</td><td>16,697 (13.5)</td><td>2925 (9.5)</td><td></td></tr><tr><td>4 doses, &lt; 90 days</td><td>2728 (2.4)</td><td>560 (2.0)</td><td></td></tr><tr><td>Missing date of last dose</td><td>7162 (4.8)</td><td>3669 (9.9)</td><td></td></tr><tr><td><strong>Study period (onset date)</strong></td><td></td><td></td><td></td></tr><tr><td>1 (10/01/2020)</td><td>7308 (4.2)</td><td>1827 (4.2)</td><td></td></tr><tr><td>2 (12/04/2020)</td><td>19,636 (11.2)</td><td>4909 (11.2)</td><td></td></tr><tr><td>3 (04/09/2021)</td><td>9008 (5.1)</td><td>2252 (5.1)</td><td></td></tr><tr><td>4 (06/14/2021)</td><td>11,264 (6.4)</td><td>2816 (6.4)</td><td></td></tr><tr><td>5 (08/14/2021)</td><td>4820 (2.7)</td><td>1205 (2.7)</td><td></td></tr><tr><td>6 (10/02/2021)</td><td>11,248 (6.4)</td><td>2812 (6.4)</td><td></td></tr><tr><td>7 (12/20/2021)</td><td>44,136 (25.1)</td><td>11,034 (25.1)</td><td></td></tr><tr><td>8 (03/18/2022)</td><td>39,652 (22.6)</td><td>9913 (22.6)</td><td></td></tr><tr><td>9 (05/20/2022) (end date: 10/02/2022)</td><td>28,616 (16.3)</td><td>7154 (16.3)</td><td></td></tr></tbody></table>

on-site office work, particularly in open space environments, professional meetings, concerts, theatres, long-distance public transit, as well as bars and night-clubs. On the other hand, we found no increased risk for most sports and cultural activities, religious gatherings, shops, and short-distance public transport.

All settings associated with an increased risk of infection in our study are characterized by varying degrees of common characteristics: mostly indoor settings with little air renewal, where contacts are close, numerous, often maskless, sometimes including singing or shouting, and usually last more than a mere few minutes. These factors are in line with knowledge on the conditions allowing transmission of SARS-CoV-2, through direct contact, large droplets, or fine aerosols [9, 24], often indoors [25].

Apart from bars, restaurants, and night-clubs for which the risk decreased through the course of the study, the associations remained overall relatively stable. Following

the implementation of the sanitary pass in most indoor places and long-distance transport in August 2021 (period 5 onwards), we observed a slight decrease in the risk associated with airplane travel, long-distance train, and bars, suggesting the sanitary pass contributed to decrease the risk of transmission in those environments. The odds ratios increased again in period 6 (October 2 to December 19, 2021). While this increase must be interpreted with caution given its limited amplitude, it may also result from a rapidly waning vaccine effectiveness on SARS-CoV-2 transmission [26], as the majority of the adult population was vaccinated between June and August 2021. Other factors could explain the decrease of the risk for bars and restaurants: night-clubs were closed from March 2020 until July 2021, and people may have been more prone to attend parties with closer and longer interactions in bars and restaurants during that time than in the later periods. The interaction of parties and bars
<table><thead><tr><th>Period</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th></tr></thead><tbody><tr><td>Onset date</td><td>10/01/2020</td><td>12/04/2020</td><td>04/09/2021</td><td>06/14/2021</td><td>08/14/2021</td><td>10/02/2021</td><td>12/20/2021</td><td>03/18/2022</td><td>05/19/2022</td></tr><tr><td>Main non-pharmaceutical interventions</td><td>Stay-at-home <sup>a</sup></td><td>Curfew</td><td>Stay-at-home</td><td>-</td><td>Sanitary pass <sup>b</sup></td><td>Sanitary pass <sup>b</sup></td><td>Sanitary / Vaccine pass <sup>b</sup></td><td>-</td><td>-</td></tr><tr><td>Predominant strain(s)</td><td>Historical</td><td>Historical - Alpha</td><td>Alpha</td><td>Alpha - Delta</td><td>Delta</td><td>Delta</td><td>Omicron BA.1</td><td>Omicron BA.2</td><td>Omicron BA.5</td></tr><tr><td>Mask mandates</td><td colspan="4">In most public or private spaces open to the public</td><td colspan="4"></td><td></td></tr><tr><td></td><td colspan="4"></td><td colspan="4">Adjusted odds ratio</td><td>Mean prevalence in controls (%)</td></tr><tr><td>Workplace</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No remote office, no open space</td><td>1 (ref)</td><td>1 (ref)</td><td>1 (ref)</td><td>1 (ref)</td><td>1 (ref)</td><td>1 (ref)</td><td>1 (ref)</td><td>1 (ref)</td><td>1 (ref)</td><td>10.9</td></tr><tr><td>No remote office, open space</td><td>1.33</td><td>1.12</td><td>1.36</td><td>1.57</td><td>1.47</td><td>1.26</td><td>1.19</td><td>1.23</td><td>1.33</td><td>5.7</td></tr><tr><td>Partial remote office, no open space</td><td>0.77</td><td>0.86</td><td>0.76</td><td>0.76</td><td>0.72</td><td>0.77</td><td>0.90</td><td>0.86</td><td>0.86</td><td>7.4</td></tr><tr><td>Partial remote office, open space</td><td>0.89</td><td>0.72</td><td>0.74</td><td>1.01</td><td>0.85</td><td>1.09</td><td>1.00</td><td>0.98</td><td>0.95</td><td>6.9</td></tr><tr><td>Complete remote office</td><td>0.67</td><td>0.95</td><td>0.84</td><td>1.15</td><td>1.64</td><td>1.01</td><td>1.15</td><td>1.00</td><td>1.00</td><td>5.3</td></tr><tr><td>Working in a non-office setting</td><td>0.97</td><td>1.04</td><td>0.9</td><td>1.07</td><td>1.19</td><td>1.18</td><td>1.31</td><td>1.19</td><td>1.35</td><td>27.3</td></tr><tr><td>Not working</td><td>0.60</td><td>0.70</td><td>0.83</td><td>1.59</td><td>1.86</td><td>1.52</td><td>0.85</td><td>0.58</td><td>0.84</td><td>30.6</td></tr><tr><td>On leave during period of interest</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.82</td><td>1.18</td><td>1.58</td><td>9.2</td></tr><tr><td>Gatherings</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Professional meeting</td><td>1.14</td><td>1.01</td><td>0.96</td><td>0.88</td><td>0.94</td><td>1.25</td><td>1.14</td><td>1.11</td><td>1.31</td><td>23.5</td></tr><tr><td>Private meeting</td><td>1.75</td><td>0.91</td><td>0.84</td><td>0.78</td><td>0.66</td><td>0.87</td><td>0.93</td><td>0.82</td><td>0.82</td><td>48.7</td></tr><tr><td>Religious gathering</td><td>1.01</td><td>1.00</td><td>1.00</td><td>0.90</td><td>0.74</td><td>1.16</td><td>1.05</td><td>0.94</td><td>0.96</td><td>4.2</td></tr><tr><td>Retail, shops</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Any shop</td><td>0.84</td><td>0.56</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>80.9</td></tr><tr><td>Mall</td><td>-</td><td>-</td><td>0.66</td><td>0.77</td><td>0.73</td><td>0.72</td><td>0.68</td><td>0.72</td><td>0.79</td><td>23.4</td></tr><tr><td>Market</td><td>-</td><td>-</td><td>0.84</td><td>0.91</td><td>0.91</td><td>0.9</td><td>0.85</td><td>0.88</td><td>0.97</td><td>14.0</td></tr><tr><td>Convenience store</td><td>-</td><td>-</td><td>0.92</td><td>1.23</td><td>1.25</td><td>1.44</td><td>1.18</td><td>1.15</td><td>1.17</td><td>35.0</td></tr><tr><td>Supermarket</td><td>-</td><td>-</td><td>0.57</td><td>0.52</td><td>0.54</td><td>0.58</td><td>0.59</td><td>0.57</td><td>0.66</td><td>63.3</td></tr><tr><td>Other shop</td><td>-</td><td>-</td><td>0.62</td><td>0.51</td><td>0.72</td><td>0.61</td><td>0.66</td><td>0.71</td><td>0.88</td><td>4.7</td></tr><tr><td>Beauty salon</td><td>-</td><td>-</td><td>closed</td><td>1.12</td><td>0.84</td><td>0.69</td><td>0.77</td><td>0.72</td><td>0.80</td><td>6.1</td></tr><tr><td>Hairdresser</td><td>-</td><td>-</td><td>0.52</td><td>0.61</td><td>0.56</td><td>0.61</td><td>0.57</td><td>0.64</td><td>0.56</td><td>13.7</td></tr><tr><td>Take-away delivery</td><td>-</td><td>-</td><td>0.97</td><td>1.07</td><td>1.19</td><td>1.13</td><td>1.11</td><td>1.12</td><td>1.28</td><td>30.5</td></tr><tr><td>Home delivery</td><td>-</td><td>-</td><td>1.13</td><td>1.01</td><td>0.99</td><td>0.82</td><td>0.91</td><td>0.85</td><td>0.80</td><td>16.3</td></tr></tbody></table>

**Fig. 2** Adjusted odds ratios of SARS-CoV-2 infection in a case-control study in France. Legend: The study period was divided in nine shorter study periods based on incidence, the circulating strains (two strains are indicated when the period includes the rise of a new strain), and the main non-pharmaceutical interventions. The colors of the cells refer to 95% confidence intervals' width: in shades of blue if the upper bound of the 95% CI is <1, in shades of red if the lower bound of the 95% CI is >1. Cells are uncolored if the 95% confidence interval includes 1. The empty cells reflect the modifications of the questionnaire through the course of the study. Cases and controls were matched with a 4:1 ratio on sex (female or male), age (in 10-year-age categories), region, size of population of the area of residence, and week of exposure. To account for random selection of cases as those outnumbered controls more than four times, we generated 100 databases for each period with matched sets of 4 cases per control. The odds ratios were estimated in multivariable logistic regression models for each of the periods, adjusting for all the variables shown in the figure as well as the matching variables, household characteristics (number of people in the household, presence of children, type of housing), professional category, health status (body-mass index, smoking status, hypertension, diabetes mellitus, chronic respiratory disease, coronary artery disease, immunosuppression), past episode of infection (categorized as 61–180 days prior or >180 days prior), and COVID-19 vaccine status (number of doses and time since last dose divided in <90 days, 90–179 days, ≥ 180 days). The odds ratio and the 95% confidence intervals estimates were inferred through the 50th, 2.5th, and 97.5th percentiles of the 100 estimates for each period. All variables shown here are dummy variables except for one combined variable regarding the workplace. (a): The stay-at-home orders started on 10/30/2020; bars, restaurants, night-clubs, non-essential shops, and cultural venues were then closed. (b): The sanitary pass was a proof of vaccine, past infection, or a recent negative test to enter a series of public spaces; the vaccine pass was implemented on 01/24/2022 to include only proofs of vaccine or past infection. (c): Estimated in people exposed before the start of the stay-at-home orders on 10/30/2020
<table><thead><tr><th>Period</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th></tr></thead><tbody><tr><td>Onset date</td><td>10/01/2020</td><td>12/04/2020</td><td>04/09/2021</td><td>06/14/2021</td><td>08/14/2021</td><td>10/02/2021</td><td>12/20/2021</td><td>03/18/2022</td><td>05/19/2022</td></tr><tr><td>Shared transport</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Metro</td><td>0.92</td><td>1.00</td><td>1.19</td><td>1.02</td><td>1.07</td><td>1.07</td><td>1.07</td><td>1.16</td><td>1.15</td><td>13.3</td></tr><tr><td>Tram</td><td>0.72</td><td>0.77</td><td>0.88</td><td>0.72</td><td>0.71</td><td>0.68</td><td>0.87</td><td>0.84</td><td>0.84</td><td>8.0</td></tr><tr><td>Short-distance bus</td><td>0.65</td><td>0.65</td><td>0.63</td><td>0.75</td><td>0.58</td><td>0.61</td><td>0.80</td><td>0.79</td><td>0.85</td><td>15.0</td></tr><tr><td>Short-distance train</td><td>0.91</td><td>0.64</td><td>0.46</td><td>0.6</td><td>0.98</td><td>0.94</td><td>0.91</td><td>0.85</td><td>0.91</td><td>6.8</td></tr><tr><td>Airplane travel</td><td>0.69</td><td>1.78</td><td>1.35</td><td>1.67</td><td>1.2</td><td>1.44</td><td>1.07</td><td>1.00</td><td>1.53</td><td>2.5</td></tr><tr><td>Boat travel</td><td>0.58</td><td>0.83</td><td>0.98</td><td>0.90</td><td>1.80</td><td>0.61</td><td>1.50</td><td>1.20</td><td>1.40</td><td>0.3</td></tr><tr><td>Long-distance bus</td><td>1.23</td><td>0.64</td><td>0.70</td><td>0.72</td><td>1.18</td><td>0.88</td><td>1.29</td><td>1.25</td><td>1.20</td><td>1.5</td></tr><tr><td>Long-distance train</td><td>1.41</td><td>1.86</td><td>1.88</td><td>1.36</td><td>1.32</td><td>1.80</td><td>1.61</td><td>1.38</td><td>1.25</td><td>4.0</td></tr><tr><td>Car-pooling</td><td>1.47</td><td>1.74</td><td>1.06</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>10.3</td></tr><tr><td>Car-pooling with relatives</td><td>-</td><td>-</td><td>-</td><td>1.27</td><td>1.19</td><td>1.85</td><td>1.10</td><td>1.09</td><td>1.18</td><td>21.0</td></tr><tr><td>Car-pooling booked over platform</td><td>-</td><td>-</td><td>-</td><td>0.48</td><td>0.48</td><td>0.45</td><td>0.49</td><td>0.59</td><td>0.45</td><td>2.0</td></tr><tr><td>Taxi</td><td>-</td><td>-</td><td>1.29</td><td>1.54</td><td>1.88</td><td>1.22</td><td>1.14</td><td>1.11</td><td>1.08</td><td>4.4</td></tr><tr><td>Leisure activities</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Any cultural venue</td><td>0.94</td><td>closed</td><td>closed</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.7</td></tr><tr><td>Movie theatre</td><td>-</td><td>closed</td><td>closed</td><td>0.66</td><td>0.66</td><td>0.71</td><td>0.66</td><td>0.67</td><td>0.53</td><td>9.4</td></tr><tr><td>Concert</td><td>-</td><td>closed</td><td>closed</td><td>0.85</td><td>1.51</td><td>1.31</td><td>2.09</td><td>1.68</td><td>1.45</td><td>2.1</td></tr><tr><td>Museum</td><td>-</td><td>closed</td><td>closed</td><td>0.59</td><td>0.61</td><td>0.87</td><td>0.91</td><td>1.13</td><td>0.95</td><td>2.8</td></tr><tr><td>Theatre</td><td>-</td><td>closed</td><td>closed</td><td>0.85</td><td>1.03</td><td>1.45</td><td>1.2</td><td>1.04</td><td>1.25</td><td>2.0</td></tr><tr><td>Indoor sports practice</td><td>1.23</td><td>closed</td><td>closed</td><td>1.08</td><td>1.2</td><td>1.05</td><td>1.11</td><td>1.02</td><td>0.86</td><td>7.0</td></tr><tr><td>Swimming pool</td><td>0.87</td><td>closed</td><td>closed</td><td>0.76</td><td>0.84</td><td>0.84</td><td>0.82</td><td>0.97</td><td>0.97</td><td>5.8</td></tr><tr><td>Indoor martial arts facility</td><td>-</td><td>closed</td><td>closed</td><td>0.42</td><td>0.44</td><td>0.85</td><td>0.93</td><td>1.01</td><td>0.58</td><td>1.3</td></tr><tr><td>Indoor gymnastics facility</td><td>-</td><td>closed</td><td>closed</td><td>0.92</td><td>0.71</td><td>0.84</td><td>0.92</td><td>0.97</td><td>0.59</td><td>3.8</td></tr><tr><td>Outdoor sports practice</td><td>-</td><td>-</td><td>0.72</td><td>0.75</td><td>0.78</td><td>0.68</td><td>0.77</td><td>0.72</td><td>0.71</td><td>28.0</td></tr><tr><td>Bar or restaurant<sup>c</sup></td><td>1.97</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>15.7</td></tr><tr><td>Bar</td><td>-</td><td>closed</td><td>closed</td><td>1.57</td><td>1.28</td><td>1.04</td><td>1.00</td><td>0.84</td><td>0.89</td><td>14.6</td></tr><tr><td>Restaurant</td><td>-</td><td>closed</td><td>closed</td><td>0.95</td><td>0.93</td><td>0.88</td><td>0.95</td><td>0.93</td><td>0.85</td><td>33.0</td></tr><tr><td>Party</td><td>1.15</td><td>1.66</td><td>1.84</td><td>2.66</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.7</td></tr><tr><td>Night-club</td><td>-</td><td>closed</td><td>closed</td><td>-</td><td>2.34</td><td>2.95</td><td>1.72</td><td>1.45</td><td>1.54</td><td>2.3</td></tr><tr><td>Private party</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.65</td><td>0.82</td><td>0.87</td><td>0.75</td><td>0.65</td><td>13.5</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 2** (Continued)</figcaption></figure>

with age, with higher odds ratios observed for people aged under 40, suggests the role of behavioral patterns in those environments. The summer of 2021 was also characterized by an important football European competition, during which public viewing in bars was popular and likely contributed to viral circulation. Another hypothesis is that the transmission of the more contagious delta and omicron variants did not require as favorable conditions as the previous strains, leading to a less differentiated risk between people visiting these settings and people who avoided them. Studies on the evolution of settings of transmission through the emergence of the various strains in other countries would help explore this hypothesis.

Findings of other studies on settings of transmission are overall consistent with ours. The decreased risk for people reporting working remotely was also shown in several other studies [16, 27–29], whereas the role of open-space offices has been little studied [30]. The fact that people working in open-space offices while also working partially remotely were not at lower risk of infection suggests that the benefit of remote office was offset

by the increased risk in open-space offices. The varying results for complete remote office suggest residual confounding in our analysis; these people were possibly exposed to SARS-CoV-2 transmission in other settings that they were more likely to visit, or in the household, in ways that our models could not account for. The absence of increased risk for retail facilities, as well as hairdressers and beauty salons, also reported by others [14–17, 27, 31–33], suggests that these facilities had low enough density and stringent enough measures to effectively limit transmission. Findings on shared transport, often studied altogether without distinction of the duration of the trip, have yielded conflicting evidence [12, 14, 27, 32, 34, 35]. A contact-tracing study on air travel in Ireland has underlined the role of the duration of the flight, with secondary attack rates reaching 14.9% for flights lasting over 5 h vs. 6.3% for shorter ones [36]. The contrast we found between short- and long-distance shared transport supports the importance of the duration spent onboard. Long-distance bus travels were inconsistently at increased risk, which might result from better air renewal
during the mandatory driving breaks compared to other
shared transport.

Consistent with several other studies, we found no
increased risk for cultural venues, sports facilities, and
religious gatherings, apart from a slightly increased risk
in theaters, concerts, and indoor sports facilities [14, 15,
17, 27, 31, 37]. Findings on dining and partying venues
often reported an increased risk for bars [14, 15, 17, 27,
38, 39], night-clubs and parties [17, 27, 39], whereas find-
ings are more conflicted for restaurants [15, 27, 31, 32,
38, 40]. In a case-control study in Denmark conducted in
June 2021, Munch and colleagues found an increased risk
for the attendance of restaurants or cafés only for peo-
ple reporting alcohol consumption [31]. This highlights
the role of behaviors within those settings on the risk of
transmission. Two randomized trials conducted in France
found no increased risk of SARS-CoV-2 infection in par-
ticipants of mass gatherings with strict requirements for
attendance, one at a concert [41] and one in a nightclub
[42]. These experimentations offer clues for continuation
of mass indoor gatherings in case of an emerging respira-
tory pathogen.

Our findings suggest a graduation in the risk of trans-
mission which combines degree of air renewal and
behaviors. We did not observe excess transmission in
indoor places where consistent mask-wearing was pos-
sible such as museums, movie theatres, shopping malls,
or beauty salons. In bars and restaurants, reopening in
mid-2021 was associated with recommendations for
spacing tables and opening doors and windows. In this
context, increased risk of transmission was observed
only for those younger than 40 years of age, or during
special events like the European soccer championship,
suggesting that opening doors and windows might have
been protective provided individuals avoided staying too
close to one another, and talking loudly or shouting. In
closed spaces where opening doors and windows may
be absent or limited, and mask wearing not maintained
systematically (e.g., during meals in long-distance trains,
or drinks in night-clubs), increased risk of transmission
was observed. In such places, investment in improving
air renewal or filtration would be essential to improve
air quality and decrease transmission risk. These invest-
ments are costly but have potential long-term benefits on
population health [43] and should be properly evaluated
for their feasibility and effectiveness.

The main limitation of the present study lies in the low
participation rate (6.0%). As in other studies conducted
online, we observed notable differences between our
study population and the source population: we included
a more female population, often in intermediary age
groups, and with a higher education level [44, 45]. These
demographic factors were included in our matching

procedure or in the adjusted models, thus limiting the
risk of recruitment bias. This does however decrease
the generalizability of our findings. Furthermore, unex-
pected findings such as the intermittently increased risk
associated with complete remote office, with take-away
delivery, or the decreased risk for supermarkets illus-
trate potential recruitment bias, residual confounding,
or result from the multiplicity of comparisons. These
biases are difficult to avoid entirely in a case-control
study. We chose this design nonetheless as it enabled us
to modify the questionnaire whenever necessary (intro-
ducing questions on the vaccine status, variants, etc.).
It provided results soon after the beginning of the study
which helped evidence-based decision-making in a time
of high burden of SARS-CoV-2 in France [18, 20]. Since
the incubation period of COVID-19 only lasts a few days,
we considered the risk of recall bias, a frequent limitation
of case-control studies, would be low, although we can-
not rule out that cases recalled exposures more precisely
than controls as they retrospectively tried to identify the
circumstances of infection. Cases and controls differed
significantly regarding past infection status, an inclusion
bias whose impact was likely mitigated by the adjustment
on past infection status in the multivariable models. The
consistency of the present findings with those of other
studies using different designs, such as cross-sectional
seroprevalence estimates or prospective cohorts, also
supports the validity of our approach. Despite signifi-
cant power, our study could not assess finer exposures
or behaviors associated with certain settings. We can-
not exclude for instance that the increased risk associ-
ated with train or air travel was caused by exposures at
the train station or the airport, or that specific partying
venues might have been safe provided they implemented
distancing or testing practices.

Overall, this case-control study shows that the work-
place, long-distance shared transport, and several leisure
activities were likely settings of effective SARS-CoV-2
transmission during the pandemic in mainland France.
These findings will help focusing efforts on improve-
ment in air quality and inform pandemic preparedness
strategies.

**Supplementary Information**

The online version contains supplementary material available at https://doi.org/10.1186/s12889-024-19651-y.

Supplementary Material 1

**Acknowledgements**

We thank all those who participated in the study. We thank the Caisse
nationale d'assurance maladie, in particular Sophie Martin, Anne Lévy,
and Carole Blanc for their collaboration. We thank the Ipsos public affairs
department, particularly Nathan Jeandet. We also thank Cassandre von
Platen at the Clinical research coordination office at the Institut Pasteur.
This study has been labeled as a National Research Priority by the National
Orientation Committee for Therapeutic Trials and other research on Covid-19 (CAPNET). The investigators would like to acknowledge ANRS | Emerging infectious diseases for their scientific support, the French Ministry of Health and Prevention and the French Ministry of Higher Education, Research and Innovation for their funding and support.

## Author contributions

AF, SG, TC, FO, CD, FC, and AM designed the investigation. SG, TC, AF, and AM developed the study questionnaire. TC, FO, and CD managed the online data collection. OC, SG, and TC oversaw the adherence of the study to regulatory requirements. TC oversaw the collection of the data and maintained the database. SG, TC, AR and AF performed the statistical analyses. SG and AF drafted the first versions of the manuscript. SG, TC, AR, and AF had full access to the data reported in this study. SG and AF take responsibility for the integrity of the data and the accuracy of the data analysis. All authors critically reviewed and approved the final version of the manuscript.

## Funding

The study was funded by Institut Pasteur, Research, Action Emerging Infectious Diseases (REACTing), and the French Agency Agence nationale de recherches sur le sida et les hépatites virales - Maladies Infectieuses Emergentes (ComCor project). AF's laboratory receives support from the LabEx Integrative Biology of Emerging Infectious Diseases (IBEID) (ANR-10-LABX-62-IBEID) and the INCEPTION project (PIA/ANR-16-CONV-0005) for studies on emerging viruses. SG is funded by the INCEPTION program "Investissement d'Avenir" grant ANR-16-CONV-0005".

## Data availability

The data that support the findings of this study are available from Institut Pasteur. Restrictions apply to the availability of these data, which were used under authorized agreement for this study from the data protection authority, the Commission Nationale de l'Informatique et des Libertés (CNIL). Access to these pseudonymized data would therefore require prior authorization by the CNIL.

## Declarations

### Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. This study received ethics approval from the ethics committee Comité de Protection des Personnes Sud Ouest et Outre Mer 1 on September 21, 2020, as required by French regulation on clinical research. Informed consent was obtained from all participants included in the study.

### Consent for publication

Not applicable.

### Competing interests

Fabrice Carrat declares consulting fee from Sanofi on influenza epidemiology. Authors declare no further interests.

Received: 18 January 2024 / Accepted: 30 July 2024

Published online: 04 September 2024

## References

1. Cevik M, Marcus JL, Buckee C, Smith TC. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission Dynamics should inform policy. Clin Infect Dis. 2020;73(Suppl 2):S170-6.

2. Capon A, Houston J, Rockett R, Sheppeard V, Chaverot S, Arnott A, et al. Risk factors leading to COVID-19 cases in a Sydney restaurant. Aust N Z J Public Health. 2021;45:512-6.

3. Finci I, Siebenbaum R, Richtzenhain J, Edwards A, Rau C, Ehrhardt J, et al. Risk factors associated with an outbreak of COVID-19 in a meat processing plant in southern Germany, April to June 2020. Euro Surveill. 2022;27:2100354.

4. Shen Y, Li C, Dong H, Wang Z, Martinez L, Sun Z, et al. Community Outbreak Investigation of SARS-CoV-2 transmission among Bus riders in Eastern China. JAMA Intern Med. 2020. https://doi.org/10.1001/jamainternmed.2020.5225.

5. Speake H, Phillips A, Chong T, Sikazwe C, Levy A, Lang J, et al. Flight-Associated transmission of severe Acute Respiratory Syndrome

Coronavirus 2 corroborated by whole-genome sequencing. Emerg Infect Dis. 2020;26:2872-80.

6. Deslandes A, Calba C, Mahdjoub S, Zhu-Soubise A, Mathey D, Ardoin A. Superspreading events of SARS-CoV-2 in Paris: a retrospective analysis of data from the first wave of COVID-19 in 2020. J Infect Public Health. 2021;14:1733-8.

7. Luo L, Liu D, Liao X, Wu X, Jing Q, Zheng J, et al. Contact settings and risk for transmission in 3410 close contacts of patients with COVID-19 in Guangzhou, China: a prospective cohort study. Ann Intern Med. 2020;173:879-87.

8. Wu Y, Song S, Kao Q, Kong Q, Sun Z, Wang B. Risk of SARS-CoV-2 infection among contacts of individuals with COVID-19 in Hangzhou, China. Public Health. 2020;185:57-9.

9. Leung NHL. Transmissibility and transmission of respiratory viruses. Nat Rev Microbiol. 2021;19:528-45.

10. Adam DC, Wu P, Wong JY, Lau EHY, Tsang TK, Cauchemez S, et al. Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong. Nat Med. 2020;26:1714-9.

11. Meylan S, Dafni U, Lamoth F, Tsourti Z, Lobritz MA, Regina J, et al. SARS-CoV-2 seroprevalence in healthcare workers of a Swiss tertiary care centre at the end of the first wave: a cross-sectional study. BMJ Open. 2021;11:e049232.

12. Talaei M, Faustini S, Holt H, Jolliffe DA, Vivaldi G, Greenig M, et al. Determinants of pre-vaccination antibody responses to SARS-CoV-2: a population-based longitudinal study (COVIDENCE UK). BMC Med. 2022;20:87.

13. Costa SF, Giavina-Bianchi P, Buss L, Mesquita Peres CH, Rafael MM, Dos Santos LGN, et al. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) seroprevalence and risk factors among Oligo/Asymptomatic Healthcare Workers: estimating the impact of Community Transmission. Clin Infect Dis. 2021;73:e1214-8.

14. Cajar MD, Tan FCC, Boisen MK, Krog SM, Nolsoee R, Collatz Christensen H, et al. Behavioral factors associated with SARS-CoV-2 infection. BMJ Open. 2022;12:e056393.

15. Munch PK, Espenhain L, Hansen CH, Müller L, Krause TG, Ethelberg S. Societal activities associated with SARS-CoV-2 infection: a case-control study in Denmark, November 2020. Epidemiol Infect. 2021;150:e9.

16. Leite A, Leão T, Soares P, Severo M, Moniz M, Lucas R, et al. A case-control study of contextual factors for SARS-CoV-2 transmission. Front Public Health. 2021;9:772782.

17. Levi M, Cereda G, Cipriani F, Voller F, Baccini M. Case-control study on the routes of transmission of SARS-CoV-2 after the Third Pandemic Wave in Tuscany, Central Italy. Int J Environ Res Public Health. 2023;20:1912.

18. Galmiche S, Charmet T, Schaeffer L, Paireau J, Grant R, Chény O, et al. Exposures associated with SARS-CoV-2 infection in France: a nationwide online case-control study. Lancet Reg Health Eur. 2021;7:100148.

19. Charmet T, Schaeffer L, Grant R, Galmiche S, Chény O, Von Platen C, et al. Impact of original, B.1.1.7, and B.1.351/P.1 SARS-CoV-2 lineages on vaccine effectiveness of two doses of COVID-19 mRNA vaccines: results from a nationwide case-control study in France. Lancet Reg Health Eur. 2021;8:100171.

20. Grant R, Charmet T, Schaeffer L, Galmiche S, Madec Y, Von Platen C et al. Impact of SARS-CoV-2 Delta variant on incubation, transmission settings and vaccine effectiveness: results from a nationwide case-control study in France. Lancet Reg Health Eur. 2021;10:100278.

21. Galmiche S, Cortier T, Charmet T, Schaeffer L, Chény O, von Platen C, et al. SARS-CoV-2 incubation period across variants of concern, individual factors, and circumstances of infection in France: a case series analysis from the ComCor study. Lancet Microbe. 2023;4:e409-17.

22. VanderWeele TJ. Principles of confounder selection. Eur J Epidemiol. 2019;34:211-9.

23. Ourworldindata. COVID-19 Data Explorer. Our World in Data. https://ourworldindata.org/explorers/coronavirus-data-explorer. Accessed 20 Jun 2023.

24. Greenhalgh T, Jimenez JL, Prather KA, Tufekci Z, Fisman D, Schooley R. Ten scientific reasons in support of airborne transmission of SARS-CoV-2. Lancet. 2021;397:1603-5.

25. Bulfone TC, Malekinejad M, Rutherford GW, Razani N. Outdoor Transmission of SARS-CoV-2 and other respiratory viruses: a systematic review. J Infect Dis. 2021;223:550-61.

26. Eyre DW, Taylor D, Purver M, Chapman D, Fowler T, Pouwels KB, et al. Effect of Covid-19 vaccination on transmission of alpha and Delta variants. N Engl J Med. 2022;386:744-56.

27. Hoskins S, Beale S, Nguyen V, Boukari Y, Yavlinsky A, Kovař J, et al. Relative contribution of essential and non-essential activities to SARS-CoV-2 transmission
following the lifting of public health restrictions in England and Wales.
Epidemiol Infect. 2022;151:e3.

28. Meza E, Cummings KJ, Vergara XP, Lai KW, Lim E, Lamba K, et al. Evaluating the association between in-person work and the risk of SARS-CoV-2 infection through June 2021. Am J Ind Med. 2023;66:222-32.

29. Shah MM, Spencer BR, Feldstein LR, Haynes JM, Benoit TJ, Saydah SH et al. Occupations Associated with SARS-CoV-2 infection and vaccination, U.S. blood donors, July 2021-December 2021. Clin Infect Dis. 2022;ciac883.

30. Weissberg D, Böni J, Rampini SK, Kufner V, Zaheri M, Schreiber PW, et al. Does respiratory co-infection facilitate dispersal of SARS-CoV-2? Investigation of a super-spreading event in an open-space office. Antimicrob Resist Infect Control. 2020;9:191.

31. Munch PK, Espenhain L, Hansen CH, Krause TG, Ethelberg S. Case-control study of activities associated with SARS-CoV-2 infection in an adult unvaccinated population and overview of societal COVID-19 epidemic counter measures in Denmark. PLoS ONE. 2022;17:e0268849.

32. Smith-Jeffcoat SE, Sleweon S, Koh M, Khalil GM, Schechter MC, Rebolledo PA, et al. Risk-factors for exposure Associated with SARS-CoV-2 detection after recent known or potential COVID-19 exposures among patients seeking Medical Care at a large Urban, Public Hospital in Fulton County, Georgia - A Cross-sectional Investigation. Front Public Health. 2022;10:809356.

33. Klein C, Borsche M, Balck A, Föh B, Rahmöller J, Peters E, et al. One-year surveillance of SARS-CoV-2 transmission of the ELISA cohort: a model for population-based monitoring of infection risk. Sci Adv. 2022;8:eabm5016.

34. Ellingjord-Dale M, Kalleberg KT, Istre MS, Nygaard AB, Brunvoll SH, Eggesbø LM, et al. The use of public transport and contraction of SARS-CoV-2 in a large prospective cohort in Norway. BMC Infect Dis. 2022;22:252.

35. Amer F, Gil-Conesa M, Carlos S, Ariño AH, Carmona-Torre F, Martínez-González MA, et al. Behavioral and personal characteristics Associated with risk of SARS-CoV-2 infection in a Spanish University Cohort. Am J Epidemiol. 2023;192:1463-74.

36. White PF, Boland M, O'Sullivan MB, Bambury N, Deignan P, Dean J, et al. Transmission of SARS-CoV-2 arising from international flights arriving in Ireland in December 2020: a descriptive analysis using national surveillance data. Public Health. 2022;204:49-53.

37. Kahltert C, Persi R, Güsewell S, Egger T, Leal-Neto O, Sumer J et al. Non-occupational and occupational factors associated with specific SARS-CoV-2 antibodies among hospital workers - a multicentre cross-sectional study. Clin Microbiol Infection: Official Publication Eur Soc Clin Microbiol Infect Dis. 2021;27.

38. Arashiro T, Arima Y, Muraoka H, Sato A, Oba K, Uehara Y, et al. Behavioral factors associated with SARS-CoV-2 infection in Japan. Influenza Other Respir Viruses. 2022;16:952-61.

39. Cummings KJ, Clarkberg M, Busche J, Travis AJ, Meredith GR, Weisfuse I, et al. Case-control study to identify risk factors for SARS-CoV-2 infection among university students in the northeastern USA. Transbound Emerg Dis. 2022;69:e2688-93.

40. Lin A, Vittinghoff E, Olgin J, Peyser N, Aung S, Joyce S, et al. Predictors of incident SARS-CoV-2 infections in an international prospective cohort study. BMJ Open. 2021;11:e052025.

41. Delaugerre C, Foissac F, Abdoul H, Masson G, Choupeaux L, Dufour E, et al. Prevention of SARS-CoV-2 transmission during a large, live, indoor gathering (SPRING): a non-inferiority, randomised, controlled trial. Lancet Infect Dis. 2022;22:341-8.

42. Luong Nguyen LB, Goupil de Bouillé J, Menant L, Noret M, Dumas A, Salmona M et al. A randomised controlled trial to study the transmission of SARS-CoV-2 and other respiratory viruses during indoor clubbing events (ANRS0066s ITOC study). Clin Infect Dis. 2023;ciad603.

43. Cheek E, Guercio V, Shrubsole C, Dimitroulopoulou S. Portable air purification: review of impacts on indoor air quality and health. Sci Total Environ. 2021;766:142585.

44. Smith W. Does Gender Influence Online Survey Participation? A Record-Linkage Analysis of University Faculty Online Survey Response Behavior. Online Submission. 2008.

45. Jang M, Vorderstrasse A. Socioeconomic status and racial or ethnic differences in participation: web-based survey. JMIR Res Protoc. 2019;8:e11865.

## Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.