# Social Inequalities and Vulnerability of Population Facing the COVID-19: The Case of Seine-Saint-Denis in Ile-De-France.

Stephane Goutte, Thomas Porcher, Thomas Peran

► To cite this version:

Stephane Goutte, Thomas Porcher, Thomas Peran. Social Inequalities and Vulnerability of Population Facing the COVID-19: The Case of Seine-Saint-Denis in Ile-De-France.. SSRN Electronic Journal, 2020, 10.2139/ssrn.3605881. hal-04482307

HAL Id: hal-04482307

https://hal.uvsq.fr/hal-04482307

Submitted on 28 Feb 2024

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
Social inequalities and vulnerability of population
facing the COVID-19: the case of
*Seine-Saint-Denis in Île-de-France.*

Stéphane GOUTTE*

Université Paris-Saclay, UVSQ, CEMOTEV, 78280, Guyancourt, France

Thomas PÉRAN

Executive Director at ROLACC Geneva, 211 route de Ferney, CH1218, Le Grand-Saconnex
Research Fellow at Université de Paris.

Thomas PORCHER

PSB, Paris School of Business, 59 rue Nationale, 75013, Paris, France.

May 20, 2020

Abstract

The vast majority of research focuses on the individual factors leading to coronavirus mortality. Numerous studies have shown that the age of the population is the dominant factor explaining mortality. Other more recent work has added gender, comorbidity, ethnicity and obesity. Based on the most populous and dense region of France — Île-de-France, grouping 8 heterogeneous departments in terms of wealth — our study seeks to identify whether economic and financial or structural factors related to housing can explain a faster circulation of the virus during social distancing like lockdown, and therefore lead to excess mortality. We show that agglomerations with higher precariousness indicators (unemployment benefit income, poverty rate, social minima in income, little or no graduate in the workforce) and less suitable housing (potentially unworthy housing, household size, overcrowded housing) are more at risk, including if their population is younger. Our study therefore provides political leaders with a number of indications allowing them to take effective measures in the event of a second wave of COVID-19 or forthcoming coronavirus pandemics.

*Keywords:* Pandemic; COVID-19; Social distancing; Health system; Territorial vulnerabilities; Poverty; Housing

JEL classification: I14; I18; J14; H12; R11

<footnote>*Corresponding author: stephane.goutte@uvsq.fr</footnote>
# 1 Introduction

On the 11th of March 2020, the Director General of the World Health Or-
ganization (WHO) declared the COVID-19 epidemic as an ongoing pandemic,
while more than 110 states were heavily affected worldwide, totalizing approx-
imately 120,000 cases of coronavirus (WHO, 2020a). Very quickly, researchers
from around the World devoted their work to the study of this new virus, by
mainly using three different approaches. First, a race against the clock was
launched by epidemiologists to find a vaccine (Shoenfeld, 2020; Cohen, 2020;
Thanh, Andreadakis et al., 2020) and reach in the earliest possible delay a
satisfactory level of collective immunity (Altmann, Douek and Boyton, 2020).
Second, the medical profession devoted itself to studying the effects of the virus
on the health of individuals. Finally, the major part of researchers has tried to
identify the most effective ways to staunch this global scourge. Relative to this
last axis in which this current study fully fits, we aim to understand what are
the factors of transmission of the virus (Li, Xu et al., 2020) and worsening of the
situation health (Di Lorenzo and Di Trolio, 2020), but also to assess the extent
of the economic consequences that the health crisis inevitably causes (McKee
and Stuckler, 2020; Yue, Shao et al., 2020).

Fundamental elements to understand the COVID-19 phenomenon are im-
mediately revealed by the first studies. It is thus demonstrated that the source
of the pandemic is to be located in the Chinese city of Wuhan inside Hubei
Province and that in the category of elderly individuals (NHS England, 2020)
the highest mortality rate is to be deplored. Extremely fast, these results are
refined to allow a precise identification of other comorbidity factors (Bacon,
Bates et al., 2020). Thus, for example, it seems very likely that patients suffer-
ing from other pathologies such as diabetes (Klonoff and Umpierrez, 2020)
or asthma (Abrams and Szepler, 2020; WHO, 2020b) are more affected than
healthiest patients, but also that the rhesus of the blood group and the ethnic
origin of the patients (Mihm, 2020; NHS England, 2020; Webb Hooper, Nápoles
and Pérez-Stable, 2020) could constitute a medical field fostering the mortality
of the virus. In other words, a standard “robot portrait” of the patient most at
risk in front of COVID-19 is already drafted.

On the other hand, geographical studies show that if not a single continent
is sheltered<fnref n="1" /> (Hopman, Allegranzi and Mehtar, 2020; Gilbert, Pullano et al.,
2020), we observe within the West the highest morbidity rate, according to
the following top Global ranking: United States of America (≥ 79,500 deaths),
United Kingdom (≥ 31,900 deaths), Italy (≥ 30,500 deaths), Spain (≥ 26 600),
France (≥ 26,300)<fnref n="2" />. Therefore, in order to better understand the vectors of
the virus transmission as well as the morbidity factors, it seems interesting to
conduct comparative studies at the three continental, regional and State levels.

<footnote n="1">¹It also seems that not a single country has been sheltered and that the few localities where no deaths have been recorded have chosen not to report the cases. Refer to figures from Johns Hopkins University which are widely accepted by the Global scientific Community. Available at: https://coronavirus.jhu.edu/map.html (accessed 10 May 2020).</footnote>

<footnote n="2">²Figures updated to May 11, 2020.</footnote>
<figure><img src="image_2.png" /><figcaption>Figure 1: Map of Île-de-France</figcaption></figure>

However, the first observations establish that biases exist and that it there-
fore could be reasonable to limit comparative analyzes to territorial units with
the same human and climatic characteristics (Desjardins, Hohl and Delmelle,
2020; Liu, Zhou et al., 2020). For example, the observation is made that in
Sub-Saharan Africa, the contamination and transmission/propagation rates are
extremely low compared to the countries in the North and West of the Globe
(Martinez-Alvarez, Jarde et al., 2020; Nuwagira and Muzoora, 2020). The posi-
tive effects of various factors are discussed in this regard, such as the protective
role of previous injections of Malaria vaccine on populations exposed to COVID-
19 (Sargin and Yavasoglu, 2020). Moreover, the global death Report show that
the counting of national deaths is not uniform. Some countries report exclu-
sively deaths in hospital (like France at the early stage of the pandemic) while
others merge deaths in hospital, domestic and nursing homes (like Germany).
Accordingly, an international study seems to be unrealistic at the moment.

Compared to the other research papers mentioned above, our study proposes
an approach to the structural factors of contamination/contagion/mortality,
rather than a focus on human factors. In other words, it is a question of asking
whether, in addition to genetics and geography, new elements can be put forward
to explain the excess mortality in certain populations. For this, we have limited
our research to Île-de-France (see Figure (2.1)), a French region built of eight
departments<fnref n="3" />, which has the unique characteristic of not constituting a cluster
of contamination due to an identifiable and outstanding event.

<footnote n="3">³In France, administrative levels in order of importance (ascending order) are municipalities/agglomerations (35 357 units), departments (101 units) and regions (18 units).</footnote>
Indeed, certain territorial units have formed clusters of contamination largely responsible for the degree of contamination at the National level. For example, in the French city of Mulhouse belonging to the Grand Est region, a major gathering of faithful evangelists is considered to be responsible for a non-negligible part of the whole excess mortality linked to COVID-19 among the State. In addition, the Île-de-France region is highly populated with 12 174 880 million inhabitants (19% of the whole French population) and socially heterogeneous (ethnicity, professional qualification of workers, graduate of higher education, quality of the health system...) but its boarders stand inside a small geographic area with no climatic ecosystems effects. Under these conditions, Île-de-France constitutes a relevant field of study for the various structural factors other than individual ones like age or comorbidities promoting the contamination/contagion/mortality tryptic.

# 2 Data and Approach

Starting with a large panel of 66 variables<fnref n="4" /> which were representative of the economic, financial or structural factors related to housing in Île de France and its population. This data collects, analyses and disseminates information on the French economy and society. We finally select using a Principal Component Analysis (PCA) a closer panel of 30 variables which are all more significant in term of segmentation of our Departments (and in particular the Seine-Saint-Denis).

## 2.1 Principal Components Analysis

Thus in order to characterize the best set of discriminant variables we proceed with a principal component analysis. This approach allows us to best capture the explanatory and segmenting power of the variables we have.

<footnote n="4">⁴Taken from the statistical database of The National Institute of Statistics and Economic Studies (INSEE).</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 2: Projection of IDF Departments on the two main PCA axis</figcaption></figure>

The Figure 2.1 shows the best representation (projection) in two dimensions of our 8 departments regarding the set of available variables. We see clearly that the department of Seine-Saint-Denis is far away from the others (in the upper left position) which argues in favor of a significant difference in term of variable values from the others departments in Ile de France. Thus, this prove that a study of the specificities of this departments is useful and relevant to understand and explain the reasons and factors which brought to this excess of mortality in Seine-Saint-Denis. Moreover, we can see that the most distant and therefore different departments with respect to Seine-Saint-Denis are Paris and Haut de Seine. We can see in Figure 2.1, the cumulative variability obtained by taking a representation in this only two main factor axis equals 82.70%. This prove the statistical relevance of this projection.
<figure><img src="image_4.png" /><figcaption>Figure 3: Cumulative Variability obtained for each factor axis</figcaption></figure>

If we want to go more deeply in the explanation of this segmentation, we
can take a look on the weights of each variables given by each two axis. These
results are provided in Table 1. We can see that a positive value on the first
axis (i.e. horizontal) characterizes:

* a high share of graduates of higher education in pop. out of school 15 years or more at a level of 96.50%

• Average household size at a level of 95.96%

* a high value of the aging index at a level of 93.60%

* a high average hourly net salary at a level of 85.45%

This means that a department with a high coordinate in this Factor 1 ex-
hibits all this points; and higher is its coordinate higher is these factors. The
projection of IDF Departments on the these two main PCA axis are stated in
Table 2.

The Departments of Paris and Haut de Seine which take a high value in
this axis projection are so fundamentally and intrinsically characterized and
determined by a population with a high level of education, with a higher salary
than the other departments and an older population than the other one. This
last factor is of course the main reason why the mortality are important in both
departments.

Conversely, the Seine-Saint-Denis department which takes the most negative
value in this projection is so characterized by a younger population with a less
level of education and so an average value of salary at the end. But, as we show
previously, its excess of surmortality dues to COVID-19 is the higher. We so
have to look the interpretation of the second axis (i.e. vertical) since the Seine-
Saint-Denis is again isolated than the other departments in the upper region
(i.e. positive values).

We can see that a positive value in this factor characterizes:

• a high number of main residences overcrowded at a level of 96.00%.

• a high share of private park accommodation potentially unworthy (PPPI) at a level of 91.44%

- a high number of people living in an apartment in a household of at least 4 people at a level of 88.83%

• a high poverty rate at a level of 88.20%

* a high value of share of unemployment benefits in the rev. avail. at a level of 72.74%

This means that the Seine-Saint-Denis is highlighted by very difficult eco-
nomic and health conditions: overcrowded housing, potentially unworthy; a
low-income population, mostly from income linked to unemployment benefits.
Hence, conditions socio-economics which bring surmortality in period of pan-
demic.
<table><caption>Table 1: Explication Weights of each variables on the two main axis factor</caption><thead><tr><th></th><th>F1</th><th>F2</th></tr></thead><tbody><tr><td>Unemployment rate</td><td>0,381558</td><td>0,5903711</td></tr><tr><td>People 65y and more</td><td>0,700227</td><td>0,2170222</td></tr><tr><td>People 75y and more</td><td>0,684784</td><td>0,1869943</td></tr><tr><td>Aging index</td><td>0,936031</td><td>0,002343</td></tr><tr><td>Population density</td><td>0,697611</td><td>0,2897714</td></tr><tr><td>Average household size</td><td>0,959639</td><td>0,0040854</td></tr><tr><td>Median standard of living</td><td>0,659438</td><td>0,3188606</td></tr><tr><td>Share of taxed tax households</td><td>0,49332</td><td>0,488439</td></tr><tr><td>Share of unemployment benefits in the rev. avail.</td><td>0,24914</td><td>0,7274552</td></tr><tr><td>Part des prestations logement dans le rev. disp. 2016</td><td>0,343963</td><td>0,6314759</td></tr><tr><td>Share of social minima in rev. avail.</td><td>0,315023</td><td>0,6619699</td></tr><tr><td>Taux de pauvreté 2016</td><td>0,107143</td><td>0,8820404</td></tr><tr><td>Share of pops with little or no diploma. out of school 15 years or more</td><td>0,721121</td><td>0,2702388</td></tr><tr><td>Share of graduates of higher education in pop. out of school 15 years or more</td><td>0,965011</td><td>0,0075429</td></tr><tr><td>Share of apartments in total housing</td><td>0,556234</td><td>0,3294512</td></tr><tr><td>Share of houses in total housing</td><td>0,559437</td><td>0,3305705</td></tr><tr><td>Share of owners of their residences</td><td>0,351085</td><td>0,6040112</td></tr><tr><td>Share of HLM tenants in main residences</td><td>0,116735</td><td>0,518652</td></tr><tr><td>Share of workers in the number of jobs</td><td>0,741465</td><td>0,0183661</td></tr><tr><td>Activity rate by age group</td><td>0,651726</td><td>0,2236003</td></tr><tr><td>Public Service Workforce</td><td>0,670789</td><td>0,1730079</td></tr><tr><td>Average hourly net salary</td><td>0,85455</td><td>0,0579598</td></tr><tr><td>Share of admin positions. public, education, health and social action in institutions. assets</td><td>0,409242</td><td>1,106E-05</td></tr><tr><td>General practitioner</td><td>0,780782</td><td>0,0758108</td></tr><tr><td>Nurses</td><td>0,564617</td><td>0,0727833</td></tr><tr><td>Pharmacy</td><td>0,798154</td><td>0,1077442</td></tr><tr><td>Elderly accommodation</td><td>0,807258</td><td>0,0102245</td></tr><tr><td>Nursery</td><td>0,911818</td><td>0,0793671</td></tr><tr><td>Pôle emploi</td><td>0,094789</td><td>0,0435737</td></tr><tr><td>Infant School</td><td>0,191453</td><td>0,0657052</td></tr><tr><td>Elementary school</td><td>0,058083</td><td>0,1342393</td></tr><tr><td>Middle school</td><td>0,344103</td><td>0,2657882</td></tr><tr><td>High school</td><td>0,662656</td><td>0,1517911</td></tr><tr><td>Emergency service</td><td>0,683236</td><td>0,0466165</td></tr><tr><td>Number of main residences overcrowded Part (%)</td><td>0,017171</td><td>0,9595219</td></tr><tr><td>which population living in apartments Part (%)</td><td>0,570496</td><td>0,3373785</td></tr><tr><td>People living in an apartment in a household of at least 4 people</td><td>0,141567</td><td>0,828304</td></tr><tr><td>Share of private park accommodation potentially unworthy (PPPI) - Source Dhrill</td><td>0,045632</td><td>0,9143809</td></tr></tbody></table>
<table><caption>Table 2: Projection of IDF Departments on the two main PCA axis</caption><thead><tr><th></th><th>F1</th><th>F2</th></tr></thead><tbody><tr><td>Paris</td><td>9,605195</td><td>2,654331</td></tr><tr><td>Seine-et-Marne</td><td>-3,14978</td><td>-2,797914</td></tr><tr><td>Yvelines</td><td>0,442294</td><td>-4,153429</td></tr><tr><td>Essonne</td><td>-1,7583</td><td>-2,790276</td></tr><tr><td>Hauts-de-Seine</td><td>3,911152</td><td>-0,734134</td></tr><tr><td>Seine-Saint-Denis</td><td>-4,79127</td><td>7,2151162</td></tr><tr><td>Val-de-Marne</td><td>-0,45865</td><td>0,7085487</td></tr><tr><td>Val-d'Oise</td><td>-3,80063</td><td>-0,102243</td></tr></tbody></table>

# 3 Results

The list of these variables can be seen in Tables 3 to 7 in Appendix. To compare the values of these set of variables we decided to evaluate the variation in percentage of each value for each department with respect to the average of the region. It means that a value of 10% represents that this department has a value 10% higher than the average of all departments in Ile de France.
<table><thead><tr><th></th><th>Unemployment benefit in income</th><th>Poverty rate</th><th>Social minima in income</th><th>Little or no graduate in the workforce</th></tr></thead><tbody><tr><td>Seine-Saint-Denis</td><td>39,13%</td><td>84,07%</td><td>118,18%</td><td>46,12%</td></tr><tr><td>Paris</td><td>-5,14%</td><td>1,69%</td><td>-27,27%</td><td>-30,97%</td></tr><tr><td>Seine-et-Marne</td><td>-5,14%</td><td>-25,34%</td><td>-7,44%</td><td>5,85%</td></tr><tr><td>Yvelines</td><td>-14,62%</td><td>-37,57%</td><td>-47,11%</td><td>-15,24%</td></tr><tr><td>Essonne</td><td>-8,30%</td><td>-16,98%</td><td>-14,05%</td><td>-0,29%</td></tr><tr><td>Hauts-de-Seine</td><td>-11,46%</td><td>-21,48%</td><td>-33,88%</td><td>-21,76%</td></tr><tr><td>Val-de-Marne</td><td>-1,98%</td><td>7,48%</td><td>5,79%</td><td>4,70%</td></tr><tr><td>Val-d'Oise</td><td>7,51%</td><td>8,13%</td><td>5,79%</td><td>11,60%</td></tr></tbody></table>

Our study provides interesting results. We note in Figure 3 that the link between the population over age of 75 and excess mortality is not absolute.
<figure><img src="image_5.png" /><figcaption>Figure 4: Link between Age and Excess mortality</figcaption></figure>

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /><figcaption>Figure 5: Economic Inequalities</figcaption></figure>

Two departments with the highest population deltas over age of 75, Paris (+14.38%) and Yvelines (+14.38%), are among the departments with the lowest excess mortality (respectively +73,90% and +66.60%). Conversely, while Seine-Saint-Denis department displays the lowest delta on the population over 75 (-38.51%) it shows the highest excess mortality (+128.10%). Theoretically, the standard observation would have been the opposite side: The high mortality rate observed among people over 75s in France, representing 78.3% of deaths with an average age of 81.2 (Santé Publique France, 2020), should have led to a negative ranking on such departments. The Val-d'Oise is also a department with a negative delta regarding the population over 75 years old (-15.54%) but with the fourth excess mortality in Île-de-France (+88.6%). Seine-et-Marne department has also a smaller population of over 75 (-7.41%) associated to an excess mortality rate of +71.70%.
<figure><img src="image_8.png" /><figcaption>Figure 6: Inequalities linked to housing</figcaption></figure>

Our study allows us to identify a broader number of factors, first, and to point out the specificity of each department with a significant excess mortality despite their more advantageous demography compared to others, second. By taking economic, social and financial variables that can reveal the insecurity of department populations, such as unemployment benefits, poverty rate, minimum social benefits or level of education, and by adding variables specific to the structure of housing, we offer a chance to implement tailor-made structural policies. For instance, in regard to unemployment benefit income, we observe that Seine-Saint-Denis and Val-d'Oise are the only departments to have a positive delta (respectively +39.13% and +7.51%), with a very clear demarcation for Seine-Saint-Denis (see Figure 3). Among the cluster, all the other departments have negative delta (see unemployment benefit income in Figure 3). We come to the same observation according to the poverty rate, where four departments have positive deltas with a clear demarcation of the Seine-Saint-Denis (+84.07%), then we rank Val-d'Oise (+8.12%) and Val-de-Marne (+7.48%) (see Poverty rate in Figure 3). We find similar result at the observation of social minima where three departments (Seine-Saint-Denis, Val-d'Oise and Val-de-Marne) have all positive deltas with a clear demarcation for Seine-Saint-Denis (+118.20%), Val-d'Oise and Val-de-Marne tied (+5.79%) (see Social minima in income in Figure 3). Finally, concerning the part of individuals without diploma into the workforce, Seine-Saint-Denis still occupies the first
place with a delta of +46.12% compared to the average of the cohort, followed
by Val-d'Oise (+11.60%), Seine-et-Marne (5.84%) and Val-de-Marne (+4.69%)
(see graph little or no graduate in the work force in Figure 3). Based on the anal-
ysis of economic and financial variables, the first conclusion that can be drawn is
that there are therefore several common points between Seine-Saint-Denis and
Val-d'Oise, two departments having a smaller population of 75+ but suffering a
significant excess mortality, despite social distancing measures implemented by
the French Government. Indeed, since the promulgation of the Law 2020-290 of
March 23, 2020 said ‘Emergency to face the epidemic of COVID-19’, prolonged
by the Law 2020-546 of May 11, 2020⁵, the French Government is authorized
to rule into legislative matters by decree when it comes to fight against the epi-
demic in France. In addition, regarding inequalities related to the structure of
housing, specifically the unworthily housing, the two departments with positive
deltas are Seine-Saint-Denis (+104.77%) and Paris (+77.47%) (see Potentially
unworthy housing in Figure 3). For the average size of households, five de-
partments have a positive delta: Seine-Saint-Denis (+9.47%), Seine-et-Marne
(+5.26%), Yvelines (+1.05%), Essonne (+5.26%) and Val-d'Oise (+9.47%) (see
Household size in Figure 3). Finally, four departments have positive deltas
at the variable “overcrowded main residences”: Seine-Saint-Denis (+68.16%),
Paris (+29.79%), Hauts-de-Seine (+6.12%) and Val-de-Marne (+11.83%), with
a delta far above for Seine-Saint-Denis (see Overcrowded housing in Figure 3).

4 Conclusion and opening to future work

Seine-Saint-Denis differs from other departments according to a number of
important variables. These variables, on the main field of financial economic
poverty, on the one hand, and structural factors linked to housing, on the other
hand, may explain the excess mortality during social distancing and lockdown
put in place by French Government. Six of these seven variables are also sig-
nificant in Val-d'Oise, another department which, like Seine-Saint-Denis, has a
significant excess mortality with a lower proportion of people over the age of 75.
Our study therefore provides political leaders with a number of inputs allow-
ing them to better implement effective measures in the event of a second wave
of COVID-19 or new pandemics due to viruses inside the coronavirus family.
Agglomerations with higher precariousness indicators (unemployment benefit
income, poverty rate, social minima in income, little or no graduate in the
workforce) and less suitable housing (unworthy housing, household size, over-
crowded housing) are more at risk, including when their population is younger.
It is therefore a requirement to set up new health policies facilitating an accurate
monitoring of the inhabitants and their environment in these departments or
agglomerations, with the main objective of breaking human-to-human transmis-
sion chains more quickly and efficiently. It would be interesting in an additional
work to see if, in other countries and other continents, the results obtained at

<footnote n="5">⁵Refer to https://www.legifrance.gouv.fr/.</footnote>
the occasion of the analysis of structural factors go in the same direction as in Seine-Saint-Denis in France.

## 5 References

Abrams E. M. and Szefler S. J. (2020), “Managing Asthma during COVID-19: An Example for Other Chronic Conditions in Children and Adolescents”, The Journal of Pediatrics. Available at: https://doi.org/10.1016/j.jpeds.2020.04.049.

Altmann D. M., Douek D. C. and Boyton R. J. (2020), “What policy makers need to know about COVID-19 protective immunity”, The Lancet. Available at: https://doi.org/10.1016/S0140-6736(20)30985-5.

Bacon S., Bates C., et al. (2020), “OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients”, Working Paper, University of Oxford. Available at: https://doi.org/10.1101/2020.05.06.20092999.

Butler D. (2013), “Progress stalled on coronavirus: lack of in-depth studies hampers efforts to identify source”, Nature 7467 (501): 294-295. Available at: https://doi.org/10.1038/501294a.

Cohen J. (2020), “Vaccine designers take first shots at COVID-19”, Science 368 (6486): 14-6. Available at: https://doi.org/10.1126/science.368.6486.14.

Desjardins M. R., Hohl A. and Delmelle E. M. (2020), “Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters”, Applied Geography 118: 102202. Available at: https://doi.org/10.1016/j.apgeog.2020.102202.

Di Lorenzo G. and Di Trolio R. (2020), “Coronavirus Disease (COVID-19) in Italy: Analysis of Risk Factors and Proposed Remedial Measures”, Frontiers in medicine 7:140. Available at: https://doi.org/10.3389/fmed.2020.00140.

Gilbert M., Pullano G., et al. (2020), “Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study”, The Lancet 395(10227): 871-877. Available at: https://doi.org/10.1016/S0140-6736(20)30411-6.

Hopman J., Allegranzi B. and Mehtar S. (2020), “Managing COVID-19 in Low- and Middle-Income Countries”, JAMA 323(16): 1549-1550. Available at: https://doi.org/10.1001/jama.2020.4169.

Klonoff D. C. and Umpierrez G. E. (2020), “COVID-19 in patients with diabetes: risk factors that increase morbidity”, Metabolism: clinical and experimental. Available at: https://doi.org/10.1016/j.metabol.2020.154224.

Li X., Xu S., et al. (2020), “Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan”, The Journal of Allergy and Clinical Immunology. Available at: https://doi.org/10.1016/j.jaci.2020.04.006.
Liu J., Zhou J., et al. (2020), "Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China", Science of the Total Environment 726: 138513.

Martinez-Alvarez M., Jarde A., et al. (2020), “COVID-19 pandemic in west Africa”, The Lancet Global Health 8(5): 631–632.

McKee M. and Stuckler D. (2020), “If the world fails to protect the economy, COVID-19 will damage health not just now but also in the future”, Nature medicine 26: 640-642. Available at: https://doi.org/10.1038/s41591-020-0863-y.

Mihm S. (2020), “COVID-19: Possible Impact of the Genetic Background in IFNL Genes on Disease Outcomes”, Journal of Innate Immunity. Available at: https://doi.org/10.1159/000508076.

NHS England (2020), “COVID-19 Daily Deaths”. Available at: https://www.england.nhs.uk/statistics/statistical-work-areas/covid-19-daily-deaths/.

Nuwagira E. and Muzoora C. (2020), “Is Sub-Saharan Africa prepared for COVID-19?”, Tropical Medicine and Health 48(18). Available at: https://doi.org/10.1186/s41182-020-00206-x.

Santé Publique France (2020), “COVID-19 : point épidémiologique du 24 mars 2020”. Available at: https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/documents/bulletin-national/covid-19-point-epidemiologique-du-24-mars-2020.

Sargin G. and Yavasoglu S. (2020), “Is Coronavirus Disease 2019 (COVID-19) seen less in countries more exposed to Malaria?”, Medical Hypotheses 140. Available at: https://doi.org/10.1016/j.mehy.2020.109756.

Shoenfeld Y. (2020), “Corona (COVID-19) time musings: Our involvement in COVID-19 pathogenesis, diagnosis, treatment and vaccine planning”, Autoimmunity Reviews 19: 102561. Available at: https://doi.org/10.1016/j.autrev.2020.102538.

Thanh Le T., Andreadakis Z., et al. (2020), “The COVID-19 vaccine development landscape”, Nature reviews Drug discovery 19(5): 305–6. Available at: https://doi.org/10.1038/d41573-020-00073-5.

Webb Hooper M., Nápoles A. M. and Pérez-Stable E. J. (2020), “COVID-19 and Racial/Ethnic Disparities”, JAMA. Available at: https://doi.org/10.1001/jama.2020.8598.

WHO (2020a), “WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020”. Available at: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19—11-march-2020.

WHO (2020b), “Clinical management of severe acute respiratory infection when COVID-19 is suspected”. Available at:
https://www.who.int/publications-detail/clinical-management-of-severe-acute-respiratory-infection-when-novel-coronavirus-(ncov)-infection-is-suspected.

Yue X. G., Shao X. F., et al. (2020), “Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy”, Journal of Risk and Financial Management 13(4) 66. Available at: https://doi.org/10.3390/jrfm13040066.
<table><caption>Table 3: Values of the delta percentage of our panel of data for each departments
- Part I</caption><thead><tr><th>Departments</th><th>Excess mortality</th><th>0 to 19y</th><th>20 to 39y</th><th>40 to 59y</th><th>60 to 74y</th><th>75y and more</th></tr></thead><tbody><tr><td>Paris</td><td>73,90%</td><td>-37,41%</td><td>18,28%</td><td>-4,97%</td><td>7,17%</td><td>14,38%</td></tr><tr><td>Seine-et-Marne</td><td>71,70%</td><td>5,55%</td><td>-8,82%</td><td>2,29%</td><td>3,93%</td><td>-7,41%</td></tr><tr><td>Yvelines</td><td>66,60%</td><td>2,34%</td><td>-15,93%</td><td>3,63%</td><td>6,78%</td><td>14,38%</td></tr><tr><td>Essonne</td><td>88,20%</td><td>4,76%</td><td>-6,47%</td><td>0,98%</td><td>-0,54%</td><td>2,45%</td></tr><tr><td>Hauts-de-Seine</td><td>127,80%</td><td>-6,54%</td><td>3,56%</td><td>1,31%</td><td>-3,09%</td><td>8,48%</td></tr><tr><td>Seine-Saint-Denis</td><td>128,10%</td><td>10,44%</td><td>4,51%</td><td>-2,28%</td><td>-15,50%</td><td>-38,51%</td></tr><tr><td>Val-de-Marne</td><td>96,50%</td><td>-1,70%</td><td>1,44%</td><td>-0,13%</td><td>-0,93%</td><td>2,71%</td></tr><tr><td>Val-d'Oise</td><td>88,60%</td><td>9,18%</td><td>-4,52%</td><td>-1,36%</td><td>-1,22%</td><td>-15,54%</td></tr></tbody></table>

# 6 Appendix
<table><caption>Table 4: Values of the delta percentage of our panel of data for each departments
- Part II</caption><thead><tr><th></th><th>2019-Q4 quarterly unemployment rate</th><th>Population density</th><th>Average household size</th><th>Median of standard of living</th><th>Share of taxable households</th><th>Share of unemployment benefits in disposable income</th></tr></thead><tbody><tr><td>Paris</td><td>-14,04%</td><td>270,31%</td><td>-20,00%</td><td>15,85%</td><td>8,98%</td><td>-5,14%</td></tr><tr><td>Seine-et-Marne</td><td>-7,11%</td><td>-95,79%</td><td>5,26%</td><td>-2,43%</td><td>-1,78%</td><td>-5,14%</td></tr><tr><td>Yvelines</td><td>-14,04%</td><td>-88,83%</td><td>1,05%</td><td>11,60%</td><td>10,40%</td><td>-14,62%</td></tr><tr><td>Essonne</td><td>-9,88%</td><td>-87,29%</td><td>5,26%</td><td>-0,06%</td><td>2,81%</td><td>-8,30%</td></tr><tr><td>Hauts-de-Seine</td><td>-12,65%</td><td>62,69%</td><td>-7,37%</td><td>14,83%</td><td>10,72%</td><td>-11,46%</td></tr><tr><td>Seine-Saint-Denis</td><td>44,19%</td><td>21,21%</td><td>9,47%</td><td>-26,55%</td><td>-24,40%</td><td>39,13%</td></tr><tr><td>Val-de-Marne</td><td>-0,17%</td><td>0,22%</td><td>-3,16%</td><td>-5,11%</td><td>-1,46%</td><td>-1,98%</td></tr><tr><td>Val-d'Oise</td><td>13,69%</td><td>-82,52%</td><td>9,47%</td><td>-8,13%</td><td>-5,26%</td><td>7,51%</td></tr></tbody></table>
<table><caption>Table 5: Values of the delta percentage of our panel of data for each departments
- Part III</caption><thead><tr><th></th><th>Share of social minima in disposable income</th><th>Poverty rate</th><th>Share of those with little or no education in the out-of-school population aged 15 and over</th><th>Share of higher education graduates in the out-of-school population of 15 years or more</th><th>Share of apartments in total housing</th><th>Share of houses in total housing</th></tr></thead><tbody><tr><td>Paris</td><td>-27,27%</td><td>1,69%</td><td>-30,97%</td><td>52,54%</td><td>44,56%</td><td>-97,14%</td></tr><tr><td>Seine-et-Marne</td><td>-7,44%</td><td>-25,34%</td><td>5,85%</td><td>-25,87%</td><td>-39,52%</td><td>84,59%</td></tr><tr><td>Yvelines</td><td>-47,11%</td><td>-37,57%</td><td>-15,24%</td><td>10,18%</td><td>-16,22%</td><td>35,35%</td></tr><tr><td>Essonne</td><td>-14,05%</td><td>-16,98%</td><td>-0,29%</td><td>-10,75%</td><td>-22,49%</td><td>49,01%</td></tr><tr><td>Hauts-de-Seine</td><td>-33,88%</td><td>-21,48%</td><td>-21,76%</td><td>31,61%</td><td>29,62%</td><td>-63,15%</td></tr><tr><td>Seine-Saint-Denis</td><td>118,18%</td><td>84,07%</td><td>46,12%</td><td>-33,69%</td><td>10,51%</td><td>-22,48%</td></tr><tr><td>Val-de-Marne</td><td>5,79%</td><td>7,48%</td><td>4,70%</td><td>-2,43%</td><td>13,50%</td><td>-29,47%</td></tr><tr><td>Val-d'Oise</td><td>5,79%</td><td>8,13%</td><td>11,60%</td><td>-21,59%</td><td>-19,96%</td><td>43,29%</td></tr></tbody></table>
<table><caption>Table 6: Values of the delta percentage of our panel of data for each departments
- Part IV</caption><thead><tr><th></th><th>Share of workers in the number of jobs</th><th>average hourly net wages</th><th>Share of public administration, education, health and social work</th><th>General practitioner 2018</th><th>Nurse</th><th>Pharmacy</th></tr></thead><tbody><tr><td>Paris</td><td>-37,74%</td><td>28,90%</td><td>-9,94%</td><td>116,64%</td><td>126,70%</td><td>114,95%</td></tr><tr><td>Seine-et-Marne</td><td>39,25%</td><td>-14,55%</td><td>6,47%</td><td>-15,67%</td><td>-1,90%</td><td>-23,91%</td></tr><tr><td>Yvelines</td><td>-4,27%</td><td>14,69%</td><td>-0,09%</td><td>-9,61%</td><td>-24,89%</td><td>-15,39%</td></tr><tr><td>Essonne</td><td>11,80%</td><td>-8,35%</td><td>8,30%</td><td>-26,61%</td><td>-12,76%</td><td>-27,05%</td></tr><tr><td>Hauts-de-Seine</td><td>-47,11%</td><td>24,93%</td><td>-38,38%</td><td>1,67%</td><td>-24,71%</td><td>4,98%</td></tr><tr><td>Seine-Saint-Denis</td><td>12,47%</td><td>-25,12%</td><td>4,65%</td><td>-16,09%</td><td>-17,10%</td><td>-14,28%</td></tr><tr><td>Val-de-Marne</td><td>1,76%</td><td>-6,78%</td><td>12,31%</td><td>-21,39%</td><td>-28,24%</td><td>-12,43%</td></tr><tr><td>Val-d'Oise</td><td>23,85%</td><td>-13,71%</td><td>16,68%</td><td>-28,96%</td><td>-17,10%</td><td>-26,87%</td></tr></tbody></table>
<table><caption>Table 7: Values of the delta percentage of our panel of data for each departments
- Part V</caption><thead><tr><th></th><th>Elderly accommodation</th><th>Emergency service</th><th>Nb. of overcrowded main residences</th><th>Which population living in apartment</th><th>People living in apartments in a household of at least 4 people</th><th>Share of housing in the potentially unworthy private housing stock (PPPI)</th></tr></thead><tbody><tr><td>Paris</td><td>52,58%</td><td>34,74%</td><td>29,80%</td><td>53,73%</td><td>60,00%</td><td>77,47%</td></tr><tr><td>Seine-et-Marne</td><td>2,41%</td><td>-7,37%</td><td>-38,78%</td><td>-44,59%</td><td>-25,33%</td><td>-37,20%</td></tr><tr><td>Yvelines</td><td>-6,53%</td><td>1,05%</td><td>-40,41%</td><td>-20,36%</td><td>-49,71%</td><td>-64,51%</td></tr><tr><td>Essonne</td><td>4,47%</td><td>1,05%</td><td>-28,98%</td><td>-25,11%</td><td>-29,90%</td><td>-72,70%</td></tr><tr><td>Hauts-de-Seine</td><td>10,65%</td><td>9,47%</td><td>6,12%</td><td>32,52%</td><td>0,57%</td><td>-4,44%</td></tr><tr><td>Seine-Saint-Denis</td><td>-27,15%</td><td>1,05%</td><td>68,16%</td><td>11,78%</td><td>53,90%</td><td>104,78%</td></tr><tr><td>Val-de-Marne</td><td>-14,78%</td><td>-15,79%</td><td>11,84%</td><td>14,47%</td><td>8,19%</td><td>-1,71%</td></tr><tr><td>Val-d'Oise</td><td>-21,65%</td><td>-24,21%</td><td>-7,76%</td><td>-22,42%</td><td>-17,71%</td><td>-1,71%</td></tr></tbody></table>