COVID-19: a territorial view on demographic context and ...€¦ · 05/09/2020 · Terras de Bouro Carraz. de Ansiães Torre de Moncorvo Castelo de Paiva Lajes das Flores Ribeira

Context and socioeconomic impact indicators for the COVID-19 pandemic in Portugal

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5 June, 2020


COVID-19: a territorial view on demographic context and socioeconomic impact indicators

Despite the progressive spread of the pandemic throughout the national territory, its impact continues to be

characterised by high regional heterogeneity, both from the point of view of the cases registered and from the point of

view of socioeconomic impact. Some of the results presented in this context:

The preliminary total number of deaths between 1 March and 24 May 2020 is 2 374 higher than the number

registered in the same period in 2019 and 1 133 higher than in the same period in 2018. The positive variation

compared to 2019 results mainly from the increase in the number of deaths in persons aged 75 and over (+ 2 262).

In 172 municipalities the number of deaths registered between April 27 and May 24 was higher than the same

reference value (average number of deaths in the same period in 2018 and 2019).

In Portugal, for every 10,000 inhabitants there were 32.6 confirmed cases of COVID-19. The number of confirmed

cases of COVID-19 disease per 10 thousand inhabitants was above the national value in 50 municipalities. The

analysis of the relationship between the number of confirmed cases per 10 thousand inhabitants and population

density highlights a set of 34 municipalities with values above the national average in both indicators.

Comparing the situation on March 25 and June 3, it can be seen that the territorial dispersion of the number of

confirmed cases of infection has increased. However, based on the analysis of the most recent developments, it

was possible to observe an increase in geographical concentration. In fact, the relationship between the number of

confirmed cases per 10 thousand inhabitants and the number of new confirmed cases per 10 thousand inhabitants

(between May 28 and June 3) shows ten municipalities with values above the national average in both indicators,

with six standing out due to their population size: Amadora, Loures, Odivelas, Sintra, Lisboa and Porto [see Figure

11].

Based on the analysis focused on the two metropolitan areas, it was possible to observe that there has been a

progressive slowdown in the number of new cases registered in the Metropolitan Area of Porto and, in turn, a

progressive increase in the number of new cases in the Metropolitan Area of Lisboa, with this region registering

figures above the national average since April 30 [see Figure 12].

In parallel to this press release, INE releases today an application with information that allows a territorial analysis of

the demographic context and the socioeconomic impact of the COVID-19 pandemic in Portugal [see Box 1].

As part of Statistics Portugal's Statslab, this press release also presents data on population mobility at the regional level

provided by Facebook's "Data for Good" initiative [see Box 2].

The first cases diagnosed with COVID-19 in Portugal were reported on March 2nd 2020 and the first death as a result of

COVID-19 was recorded on March 16th 2020. The WHO (World Health Organization) declared the outbreak of COVID-19

as a pandemic on March 11th 2020.


2/19

This press release includes results for the national context on the general deaths (all causes of death) that have

occurred in national territory since March 1, 2020. The incidence of the pandemic in the territory has not been

homogeneous, which justifies the analysis of context indicators, when possible, at NUTS 3 (Metropolitan Areas and

Intermunicipal Communities in Portugal mainland, and Autonomous Regions) and municipality level. In addition,

socioeconomic indicators, on a monthly basis, are presented in this press release to support the analysis of the impact

of the pandemic in the different regions and municipalities.

The results of overall mortality refer to deaths (all causes of death) that occurred in the national territory from March 1st

up to May 24th. Information on deaths is obtained through the Civil Register collected under the Integrated Civil

Registration and Identification System (SIRIC). This information was computed on June 2nd. This time lag prevents the

disclosed information from being subjected to considerable revisions. Even so, the information is preliminary and will be

subject to further updates. Data on resident population are based on the preliminary results of the Annual estimates of

resident population, referenced to December 31, 2019. The number of confirmed cases with COVID-19 is based on the

information released for the entire country and by municipality in the 'Daily COVID-19 Status Report' edited by the

Directorate-General of Health. This press release includes information available up to June 4 (data of the situation up to

June 3). Socioeconomic indicators are based on information from the Institute of Employment and Professional Training

(IEFP) and the Interbank Services Society (SIBS) (see technical note at the end for more information).

Demographic and territorial context indicators

Number of deaths between March 1st and May 24th, 2020 higher than in the same period in 2019 and 2018

The preliminary total number of deaths between March 1st and May 24th, 2020 is 2,374 higher than the number

registered in the same period in 2019 and 1,133 cases higher than number of deaths in 2018. The positive variation in

relation to 2019 is due mainly to the increase in the number of deaths of people aged 75 and over (+ 2,262).

The following figures allow the comparison of the cumulative number of deaths from the beginning of March to May

24th, 2020 with that observed in the same period in 2019 and 2018. For the total number of deaths registered, and for

the age group 75 and over, two lines were added in order to identify the moment values of cumulated deaths

registered in 2020 surpass those registered in 2019 and 2018.

Figure 1 - Cumulative number of deaths in Portugal from March 1st to May 24th (2018-2020)

Source: Statistics Portugal, Deaths; Statistics Portugal, Annual estimates of resident population. Notes: a) 2020 data: preliminary data based on information registered by the Civil Register Offices and sent to Statistics Portugal until June 2nd 2020. b) The total number of deaths may not correspond to the sum of the partial figures due to the existence of records with unknown age.

Cumulative number of deaths in Portugal from March 1st to May 24

th, 2018-2020

2018 2019 2020 2018 2019 2020

Total 27,064 25,823 28,197 263.0 251.3 274.0

Males 13,520 12,780 13,969 277.7 263.4 287.8

Females 13,544 13,043 14,228 249.7 240.5 261.6

Under 64 years 3,804 3,745 3,739 47.1 46.6 46.7

65 to 69 years 1,617 1,660 1,652 260.9 268.5 265.3

70 to 74 years 2,224 2,178 2,298 426.5 404.5 418.4

75 to 79 years 3,094 2,874 3,242 728.0 674.4 751.7

80 to 84 years 4,844 4,492 4,898 1,386.6 1,279.1 1,389.8

85 years and over 11,479 10,872 12,360 3,858.0 3,504.0 3,802.0

65 years and over 23,258 22,076 24,450 1,050.8 983.7 1,072.0

75 years and over 19,417 18,238 20,500 1,811.5 1,676.9 1,848.8

b) 2020 data: preliminary data based on information registered by the Civil Register Offices and

sent to Statistics Portugal until June 2nd

2020.

a) The total number of deaths may not correspond to the sum of the partial figures due to the

existence of records with unknown age.

Number of deathsNumber of deaths per 100

thousand inhabitants

Notes:

Source: Statistics Portugal, Deaths; Statistics Portugal, Annual estimates of resident population


3/19

Figure 2 - Cumulative number of deaths, by day of death, March 1st to May 24th (2018-2020)

Source: INE, I.P., Statistics on Deaths (Preliminary (2020) and Final Results (2018 and 2019)).

Figure 3 - Cumulative number of deaths aged 75 and over, by day of death, March 1st to May 24th (2018-2020)


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4/19

In 172 municipalities the number of deaths registered in the last four weeks (between 27 April and 24 May, 2020) was

higher than the corresponding reference value

In 172 out of the 308 Portuguese municipalities the number of deaths registered in the last four weeks (between 13 April

and 10 May, 2020) was higher than the corresponding reference value (average number of deaths in the same period in

2018 and 2019). Of this total, 34 municipalities registered a number of deaths 1.5 times higher than in the same period of

reference. For the remaining 136 municipalities (44% of the total number of municipalities) the number of deaths

registered in the last four weeks was equal or lower than the number observed in the reference period.

Figure 4- Number of deaths in the last four weeks (27 April to 24 May) per deaths in the same period of reference, Portugal, NUTS 3

and municipality


Óbidos

Manteigas

Santana

Melgaço

Vila de Rei

Arronches

Aljezur

Barrancos

Barrancos

Alcochete

Anadia

Odemira

Vila Flor

Trofa

Cartaxo

Mira

Amares

Penamacor

Alandroal

Alenquer

Pinhel

Ponta do Sol

Constância

Avis

Alvito

Avis

Montijo

Alcácer do Sal

Gondomar

Azambuja

Penafiel

Mortágua

- 3 - 2 - 1 0 1 2 3 4 5 6

Beira Baixa

Alentejo Central

Alto Tâmega

Oeste

Beiras e S. Estrela

R.A. Madeira

Alto Minho

Região de Leiria

Médio Tejo

Alto Alentejo

Algarve

Ave

Baixo Alentejo

Portugal

A.M. Lisboa

Viseu Dão Lafões

Região de Aveiro

Alentejo Litoral

T. Trás-os-Montes

R.A. Açores

A.M. Porto

Lezíria do Tejo

Tâmega e Sousa

Região de Coimbra

Douro

Cávado

Lowest municipal valuel NUTS 3 Highest Municipal Valuel

No.

PORTUGAL

Terras de Bouro

Torre de MoncorvoCarraz. de Ansiães

Castelo de Paiva

Lajes das FloresRibeira Grande

Miranda do Douro

Oliveira do Bairro

Sta. Comba Dão V.N.Paiva/Aguiar da Beira

São Brás de Alportel

Mondim de BastoCabec.de Basto

Castanh.de Pêra

Ponte da Barca

Fig.dos Vinhos

Ribeira de Pena V. Pouca de Aguiar

Viana do Alentejo

V.Velha de Ródão0 50 km

Ratio

] 1.5 ; 5.3 ]] 1 ; 1.5 ]] 0.8 ; 1 ][ 0 ; 0.8 ]

Territorial limits

NUTS IIINUTS II

MunicipalitiesFrequencies

676812152 775913042 10474 4777 81515396 15624818 83515396 696713834Municipality


5/19

Box 1 - COVID-19 Dashboard | Context and Impact

Today, INE provides a dashboard with 22 indicators that allow a territorial analysis of the demographic context and of the socioeconomic impact

of the COVID-19 pandemic in Portugal. It includes information for several areas, such as international trade, consumption, housing market,

tourism and labour market. Some of the information presented in the application is of a preliminary nature, with INE anticipating calendars

and/or increasing the geographical breakdown of regular dissemination. The aim is to contribute to a better monitoring of the situation and

impact of the pandemic at the local level.

Figure 5: COVID-19 Dashboard entry view - 19 | Context and Impact

This dashboard allows to view information by municipality, by selection in the box in the top right bar, and for Portugal, the bottom box. The

first visible slots (municipal indicators and national indicators) provide the demographic and pandemic context of the respective territorial unit.

The following slots, within each of the boxes of municipal and national indicators, provide information in graph format on the evolution of

different socioeconomic indicators, whenever possible since January 2019 [Figure 6].

The technical note presents the link to the metadata of the indicators and the latest reference period available in the application.

Figure 6: View of the evolution graphs for some of the indicators and of the technical note of the COVID-19 dashboard | Context and Impact

https://ine-pt.maps.arcgis.com/apps/opsdashboard/index.html#/7af78fbbbdd9456397317f822dac503d

https://ine-pt.maps.arcgis.com/apps/opsdashboard/index.html


6/19

50 municipalities with confirmed cases of COVID-19 disease per 10 thousand inhabitants above the national value

On May 20, 2020, in Portugal, for every 10 thousand inhabitants there were 32.6 confirmed cases of COVID-19, which

represents an increase of 12% compared to May 20, the reference date analysed in the last press release. Between May

20 and May 6, there was also an increase of 12% in the number of confirmed cases per 10 thousand inhabitants, and

between May 6 and April 22 this increase was 20%. Between April 22 and 7 (reference date of the first press release)

there was a 70% increase in this indicator.

The number of confirmed cases of COVID-19 disease per 10 thousand inhabitants was above the national value in 50

municipalities. In the Norte region, 31 municipalities registered a value above the national average, and a set of

contiguous municipalities in the Metropolitan Area of Porto stood out, with more than 50 confirmed cases per 10 thousand

inhabitants: Valongo, Matosinhos, Maia, Porto, Gondomar, Santo Tirso and Vila Nova de Gaia. Some municipalities in the

Centro (11), Metropolitan Area of Lisboa (the municipalities of Loures, Amadora, Lisboa, Odivelas and Sintra), Alentejo

(the municipalities of Moura and Azambuja) and Região Autónoma dos Açores (the municipality of Nordeste) also scored

values above the national value [Figure ].

Despite this differentiation, the estimated location coefficient1 for March 25th and June 3rd suggests a decrease in territorial

concentration of cases, i.e., a progressive spatial dissemination throughout the country. The location curves graphically

reflect this trend by the approximation to the straight line of equal distribution between the number of confirmed cases

and the resident population in the municipalities [Figure ].

Figure 7 - Number of confirmed cases of COVID-19 disease per 10 thousand inhabitants until June 3 , 2020, by municipality

Figure 8 - Territorial concentration of COVID-19 confirmed cases until March 25 and until June 3 in relation to the

resident population, based on the distribution by municipality Location Curve

Location coefficient

Wednesday – June 3 32.4

Wednesday - March 25 47.7

Source: Directorate-General of Health, Daily COVID-19 Status Report (released on June 4); INE, I.P., Annual estimates of resident population, 31 December 2019 (Preliminary Results). Note: For the calculation of the location coefficients zero cases were considered for the municipalities with no value in the Directorate-General of Health report (null or less than 3 cases).

1 The Location coefficient varies between 0 and 100, with values closer to 100 reflecting greater inequality in the distribution of confirmed cases of COVID-19 against the total resident population.

0 50 km

] 40 ; 123 ]] 32.6 ; 40 ]] 8 ; 32.6 ][ 1.1 ; 8 ]

Territorial limits

NUTS IIINUTS II

No. of confirmed cases per

PT


676812152 775913042 10474 4777 81515396 15624818 83515396 696713834 529614119 80731051832

10 000 inhab.

Null or <3

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7/19

34 municipalities registered both a number of confirmed cases per 10 thousand inhabitants and population density values

above the national reference

The following figure illustrates the relationship between population density and the number of confirmed cases per 10

thousand inhabitants for the country's municipalities. Of the 50 municipalities with a number of confirmed cases per 10

thousand inhabitants above the value for Portugal, 34 also had population density values above the national average.

From this set of 34 municipalities, the municipalities of Valongo (78.2), Matosinhos (73.4), Vale de Cambra (72.5), Maia

(68.3), Porto (65.6), Gondomar (65.5), Santo Tirso (57.9) and Vila Nova de Gaia (52.5), in the Metropolitan Area of Porto,

the municipalities of Lousada (72.9), Felgueiras (72.0) and Paços de Ferreira (54.7) in Tâmega e Sousa, the municipality

of Braga (67.1) in Cávado, the municipality of Vizela (61.7) in the sub-region of Ave, and the municipalities of Loures

(53.6), Amadora (52.1) and Lisboa (50.6) in the Metropolitan Area of Lisboa, stood out with more than 50 confirmed cases

per 10 thousand inhabitants. It should also be noted that 180 of the 308 municipalities in the country had a number of

confirmed cases per 10 thousand inhabitants and population density below the national reference.

Figure 9 - Number of confirmed cases per 10 thousand inhabitants on June 3, 2020 and Population density, by municipality

Source: Directorate-General of Health, Daily COVID-19 Status Report (released on June 4); INE, I.P., Annual estimates of resident population, 31 December 2019 (Preliminary Results).

Gondomar

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A.M. Lisboa A.M. Porto Other NUTS 3

PT = 32,6Sintra

Loures

Municipalities with + than 10 thousand inhabitants


8/19

The calculation of the location coefficient considering the new confirmed cases (last 7 days) for April 1 and 29 and June 3

suggests an increase in the territorial concentration of the new confirmed cases of COVID-19. The location curves

graphically reflect this trend by the progressively moving away from the straight line of equal distribution between the

number of new confirmed cases and the resident population in the municipalities [Figure 10].

Figure 10 – Territorial concentration of COVID-19 new confirmed cases (last 7 days) for April 1, April 29 and June 3 in relation to

the resident population, based on the distribution by municipality

Location curve


Wednesday – June 3 54.1

Wednesday – April 29 43.2

Wednesday – April 1 37.2

Source: Directorate-General of Health, Daily COVID-19 Status Report (released on June 4); INE, I.P., Annual estimates of resident population, 31 December 2019

(Preliminary Results). Note: For the calculation of the location coefficients zero cases were considered for the municipalities with no value in the Directorate-General of Health report (null or less than 3 cases).

The following figure illustrates the relationship between the total number of confirmed cases per 10,000 inhabitants by

June 3 and the number of new cases registered per 10,000 inhabitants on June 3 (last 7 days). Of the 50 municipalities

with a number of confirmed cases per 10,000 inhabitants above the figure for Portugal, 10 also scored a number of new

confirmed cases per 10,000 inhabitants above the national average. Of these 10 municipalities, half were located in the

Metropolitan Area of Lisboa - Amadora (11.1 new cases per 10 thousand inhabitants), Loures (10.0), Odivelas (7.4), Sintra

(5.8) and Lisboa (4.9) – and the remaining corresponded to the municipality of Nordeste in Região Autónoma dos Açores,

the municipalities of Vieira do Minho (5.9) and Vizela (4.6) in the sub-region of Ave, the municipality of Azambuja (5.3) in

Lezíria do Tejo, and the municipality of Porto (2.3) in the Metropolitan Area of Porto.

0

20

40

60

80

100

0 20 40 60 80 100

Ne

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ca

se

s (%

cu

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)

Resident population (% cumulative)

Equal distribution

Wednesday - June 3

Wednesday - April 1

Wednesday - April 29


9/19

Figure 11 – Number of confirmed cases per 10 thousand inhabitants on June 3, 2020 and Number of new confirmed cases per 10

thousand inhabitants on June 3 2020 (last 7 days), by municipality

Source: Directorate-General of Health, Daily COVID-19 Status Report (released on June 4); INE, I.P., Annual estimates of resident population, 31 December 2019 (Preliminary Results).

Given the high population density that characterizes the two metropolitan areas, an analysis focusing on the dynamics of

new confirmed cases of COVID-19 in these territories is particularly relevant. The following figure shows the number of

new cases registered in the last seven days per 10 thousand inhabitants for the total of the country and for the

metropolitan areas of Porto and Lisboa for the period from April 1st to June 3rd. In this context, it should be highlighted the

progressive slowdown in the number of new cases registered in the Metropolitan Area of Porto and, in turn, the

progressive increase in the number of new cases in the Metropolitan Area of Lisboa, with this region registering figures

above the national average since April 30.

Gondomar

MaiaMatosinhos

Porto

V.N. Gaia

Cascais

Lisboa

Oeiras

Amadora

Odivelas

Almada Seixal

Santo Tirso

S.J. Madeira

Valongo

BragaVizela

Ovar

Moita

V.N.Fôz Coa

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New confirmed cases per 10 000 inhabitants (No.)

OutrasRegiões OutrasRegiões_100mil AMP AML AMP_100mil reta

Municípios com + 100 mil hab.Municipalities located in:

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on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6


0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6


0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6


0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

Loures

PT = 1,9

V.F. Xira

Azambuja


0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6


0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6Sintra

Loures

V.F. Xira

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20





PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

so

s c

on

firm

ad

os

po

r 1

0 0

00

ha

bit

an

tes

(N

.º)

a 3

de

ab

ril

20

20




0

5

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15

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45

0 100 200 300 400 500 600 700

Ca

so

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(N

.º)

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PT = 32,6

0

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(N

.º)

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15

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0 100 200 300 400 500 600 700

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(N

.º)

a 3

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20

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PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

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(N

.º)

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0

5

10

15

20

25

30

35

40

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0 100 200 300 400 500 600 700

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(N

.º)

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0

5

10

15

20

25

30

35

40

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0 100 200 300 400 500 600 700

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(N

.º)

a 3

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20

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PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

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(N

.º)

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10

15

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30

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45

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(N

.º)

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0

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10

15

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25

30

35

40

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0 100 200 300 400 500 600 700

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(N

.º)

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PT = 32,6

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

Ca

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(N

.º)

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10

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0 100 200 300 400 500 600 700

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(N

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20




A.M. Lisboa A.M. Porto Other NUTS 3

PT = 32,6

Loures

PT = 1,9

V.F. Xira

Nordeste

Azambuja

Coimbra

S.M. Feira

MafraSetúbal

Montijo

Sesimbra

Municipalities with + 100 thousand inhabitants.


10/19

Figure 12 – New confirmed cases in the last seven days per 10 thousand inhabitants, by day, Portugal, metropolitan areas of Lisboa

(AML) and Porto (AMP)

Source: Directorate-General of Health, Daily COVID-19 Status Report (released on June 4); INE, I.P., Annual estimates of resident population, 31 December 2019

(Preliminary Results). Note: The dates marked on the graph axis correspond to the first days of the month and Sundays.

2

4

6

8

10

12

5 A

pr

12

Ap

r

19

Ap

r

26

Ap

r

3 M

ay

10

Ma

y

17

Ma

y

24

Ma

y

31

Ma

y

AML

AMP

Portugal

3 April:Start 2nd State of Emergency

18 April: Start 3rd State of Emergency

3 May:Start State of Calamity

4 May: Start 1st Phase of Deconfinement

9-13 April Limited

movement between

municipalities(Easter)

1-3 May Limited

movement between

municipalities(1st May)

18 May: Start 2nd Phase of Deconfinement

No. of new confirmed cases per 10,000 inhabitants

1 A

pr

1 M

ay

1 J

un


11/19

Socioeconomic impact indicators

Metropolitan Area of Lisboa and Algarve with decreases of more than 40% in the value of purchases and in withdrawals

per inhabitant in April 2020, compared to the same period in the previous year

In April 2020, the value of national withdrawals at ATMs per inhabitant was 134 Euros at national level, representing a

decrease of -34% compared to the previous month and -39% compared to the same period in the previous year. At

regional level, the autonomous regions of Madeira (117 Euros per inhabitant) and of Açores (120), the Metropolitan Area

of Lisboa (127) and Centro (128), scored, in April 2020, a value of withdrawals per inhabitant below the national average.

In April 2020, there was a decrease in the value of national withdrawals at ATMs per inhabitant in the seven NUTS 2

regions, compared with the same month of the previous year, with Metropolitan Area of Lisboa (-45%), Região Autónoma

da Madeira and the Algarve (-41% in both), standing out, with decreases of more than 40% [Figure 13].

In Portugal, the value of national purchases through automatic payment terminals per inhabitant was 196 in April 2020. At

NUTS 2 region level, this value was lower than the national reference, in Região Autónoma da Madeira (160 Euros per

inhabitant), in Norte (169), Alentejo (191) and Centro (194). In relation to the same period of the previous year, in April

2020, there was a decrease of 36% in the value of purchases per inhabitant in Portugal. This decrease was common to

the seven NUTS 2 regions of the country, with the Metropolitan Area of Lisboa (-43%) and Algarve (-40%), standing out,

with decreases greater than the national reference [Figure 14].

Figure 13 - National withdrawals at ATMs per inhabitant, monthly, Portugal and NUTS 2

Figure 14 - National purchases through automatic payment terminals per inhabitant, monthly, Portugal and NUTS 2

Source: Interbank Services Society (SIBS).

In April 2020, in 94% of the Portuguese municipalities (289 out of a total of 308), the value of national purchases through

automatic payment terminals was lower than the value for the corresponding month of the previous year. Of these, 52

municipalities, mostly located in the Metropolitan Area of Lisboa (8 out of a total of 18) and Porto (5 out of 17) and

Algarve (6 out of 16), stand out as having a lower ratio than the one registered for the country [Figure 15].

PORTUGALNorte

CentroA.M. Lisboa

AlentejoAlgarve

R.A.MadeiraR.A. Açores

50

100

150

200

250

300

350

400

jan

-19

feb

-19

ma

r-1

9

ap

r-1

9

ma

y-1

9

jun

-19

jul-

19

au

g-1

9

se

p-1

9

oct-

19

no

v-1

9

de

c-1

9

jan

-20

feb

-20

ma

r-2

0

ap

r-2

0

€ /inhab.

∕∕

0

PORTUGAL

Norte

Centro

A.M. Lisboa

Alentejo

R.A. Açores

R.A. Madeira

75

150

225

300

375

450

525

600

675

750

jan

-19

feb

-19

ma

r-1

9

ap

r-1

9

ma

y-1

9

jun

-19

jul-

19

au

g-1

9

se

p-1

9

oct-

19

no

v-1

9

de

c-1

9

jan

-20

feb

-20

ma

r-2

0

ap

r-2

0

€/inhab.

0

∕∕


12/19

Figure 15 – Value of national purchases through automatic payment terminals in April 2020 compared to the same period of the

previous year, Portugal, NUTS 3 and municipality

Source: Interbank Services Society (SIBS).

74 municipalities with a flow of unemployed registered in employment centres in April 2020 more than two times higher

than the same month of the previous year

In April 2020, in mainland Portugal, 0.4 new job placements were made with candidates presented by employment

centres per thousand inhabitants of working age (15-64 years), this value was less than half of the value registered in the

previous month (0.9). At regional level, the Metropolitan Area of Lisboa (0.11), Algarve (0.14) and Norte (0.35) presented

a lower number of job placements per thousand inhabitants of working age than the reference for mainland Portugal. In

April 2020, there was a decrease in the value of this indicator compared to the same period of the previous year in the five

NUTS 2 regions of mainland Portugal, with the region of Algarve standing out with a variation of -96% [Figure 16].

In April 2020, there were 10.2 new unemployed registered in employment centres per thousand inhabitants between 15

and 64 years old, in mainland Portugal, corresponding to an increase of +19% compared to the previous month. At

regional level, the Algarve (20.8) stood out with the highest number of new unemployed per thousand inhabitants among

the five regions of the mainland Portugal and with a value above the national reference, the Metropolitan Area of Lisboa

(10.8) also stood out. In April 2020, there was an increase in the number of unemployed per thousand inhabitants of

working age for mainland Portugal and for the respective five NUTS 2 regions, compared to the same month in the

previous year, with the greatest variations being registered in the regions of Algarve - where the value almost quadrupled

– and, the Metropolitan Area of Lisboa where the value more than doubled [Figure 17].

Alcochete

Castro Marim

Porto

Porto MonizCoimbra

Porto MonizBraga

V.V. de Ródão

T. de Moncorvo

Batalha

Aveiro

Viseu

Madalena

Manteigas

Vieira do Minho

Mourão

Grândola

Alvito

S.M. Agraço

Vila de Rei

Santarém

Bragança

Chaves

Viana do Castelo

Penafiel

Alter do Chão

V.F. de Xira

Olhão

Trofa

Calheta

P. da Serra

Porto Santo

Vila Verde

Proença-a-Nova

Mesão Frio

C. de Pêra

Estarreja

V.N. de Paiva

Calheta

Belmonte

C. de Basto

V. do Alentejo

Sines

Cuba

A. Dos Vinhos

Alpiarça

Vinhais

Montalegre

V.N. de Cerveira

Cinfães

Campo Maior

0,0 0,5 1,0 1,5

A.M. Lisboa

Algarve

A.M. Porto

Portugal

Região de Coimbra

R.A. Madeira

Cávado

Beira Baixa

Douro

Região de Leiria

Região de Aveiro

Viseu Dão Lafões

R.A. Açores

Beiras e S.Estrela

Ave

Alentejo Central

Alentejo Litoral

Baixo Alentejo

Oeste

Médio Tejo

Lezíria do Tejo

Terras de TM

Alto Tâmega

Alto Minho

Tâmega e Sousa

Alto Alentejo

Lowest municipal value NUTS 3 Highest municipal value

No.

PORTUGAL

Ferreira do Zêzere

0 50 km

No.

> 1] 0.8 ; 1 ]] 0.64 ; 0.8 ]] 0 ; 0.64 ]

Territorial limits

NUTS IIINUTS II

PT


676812152 775913042 10474 4777 81515396 15624818 83515396 696713834 529614119Municipality


13/19

Figure 16 - Job placements per thousand inhabitants between 15 and 64 years old, monthly, mainland Portugal and NUTS 2

Figure 17 – Unemployed registered at IEFP employment centres throughout the month per thousand inhabitants between 15 and

64 years old, monthly, mainland Portugal and NUTS 2

Source: Institute of Employment and Professional Training (IEFP).

In 204 of the 278 municipalities in mainland Portugal, the number of unemployed registered in employment centres during

the month of April 2020 was higher than the corresponding flow in the same period of the previous year. Of these, 74

municipalities, mostly located in the Algarve (14 out of a total of 16), Alto Minho (9 out of 10) and Metropolitan Area of

Lisboa (9 out of 18) regions, stand out for presenting, in April 2020, a flow of unemployed more than two times higher

than the one registered in the same month of the previous year [Figure 18].

Figure 18 – Number of unemployed registered in IEFP employment centres over April 2020 compared to the corresponding flow in the

same period of the previous year, mainland Portugal, NUTS 3 and municipality

Source: Institute of Employment and Professional Training (IEFP).

CONTINENTENorte

Centro

A.M. Lisboa

Alentejo

5.4

Algarve

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

4,5

jan

-19

feb-

19

mar

-19

apr-

19

may

-19

jun

-19

jul-1

9

aug-

19

sep-

19

oct-1

9

nov

-19

dec-

19

jan

-20

feb-

20

mar

-20

apr-

20

No. /1 000 inhab.

⁄⁄

5,5

CONTINENTENorte

Centro

A.M. Lisboa

Alentejo

33

Algarve

0

5

10

15

20

25

30

jan

-19

feb-

19

mar

-19

apr-

19

may

-19

jun

-19

jul-1

9

aug-

19

sep-

19

oct-1

9

nov

-19

dec-

19

jan

-20

feb-

20

mar

-20

apr-

20

No./1 000 inhab.

⁄⁄

35

Vimioso

Boticas

V.N. de Foz Côa Penedono

F. de Algodres

V.N.de Paiva

Borba

Sertã

V.V.de Ródão

Alvito

Góis

Ílhavo

Fronteira

Póvoa de Varzim

Cab. de Basto

Almeirim

Peniche

Alcácer do Sal

Amares

V.V.de Ródão

P. Grande

Cascais

Cel. de Basto

Melgaço

Monchique

Mogadouro

Ribeira de Pena

C. de Ansiães

Manteigas

Carregal do Sal

Mora

Ourém

Castelo Branco

Beja

Castelo de Vide

O. de Azeméis

Vizela

Benavente

Alenquer

Odemira

Esposende

AlcoutimBatalha

Sesimbra

P. de Coura

Alcoutim

-2 0 2 4 6 8

Terras de TM

Alto Tâmega

Douro

Beiras e S. Estrela

Viseu Dão Lafões

Alentejo Central

Médio Tejo

Beira Baixa

Baixo Alentejo

Região de Coimbra

Região de Aveiro

Alto Alentejo

A.M. Porto

Ave

Lezíria do Tejo

Oeste

Alentejo Litoral

Cávado

Continente

Região de Leiria

A.M. Lisboa

Tâmega e Sousa

Alto Minho

Algarve

Lowest municipal value NUTS 3 Highest municipal value

24 No.

CONTINENTE

//

23

23

Marco de Canaveses

Albergaria-a-Velha

Condeixa-a-Nova

0 50 km

> 2] 1.78 ; 2 ]] 1 ; 1.78 ]] 0 ; 1 ]

No.


CT

87666857

Territorial limits

NUTS IIINUTS II

Municipality

77788835 741052574


14/19

Box 2 - Population mobility indicators at regional level: an analysis based on information from

Facebook's "Data for Good" Initiative

In this box, taking advantage of Facebook's "Data for Good" Initiative, population mobility indicators at NUTS 3 level in the national territory are

released.

The data represented in the figure below correspond to the proportion of population “staying put” between March 1st and June 2nd, namely

minimum, median and maximum values obtained from the 25 NUTS 3 sub-regions of the country. For a better contextualization of the

information, the figure includes the main key moments associated with the COVID-19 pandemic in Portugal.

Figure 19: Proportion of the population “staying put” between March 1st and June 2nd – minimum, median and maximum values of NUTS 3

Source: Facebook's “Data for Good” Initiative. Data provided by Carnegie Mellon University. Note: The dates marked on the graph axis correspond to the first days of the month and Sundays.

.

The following figures show this indicator at NUTS 3 level for the days corresponding to Sundays [Figure 20] and Mondays [Figure 21], since the

beginning of March. It can be seen that the days corresponding to Sundays indicate, overall, less mobility of the population than the days

corresponding to Mondays. In particular, there is a reduction in mobility levels with the beginning of the State of Emergency on March 19 (maps

of March 22 and 23). On the contrary, a progressive increase in mobility has been registered with the transition from the State of Emergency to

the State of Calamity on May 3, followed by the first phase of implementation of the deconfinement measures (maps on May 3 and 4), and the

beginning of the second phase of deconfinement on May 18 (maps on May 18, 24, 25 and 31 and June 1).

0

20

40

60

80

8 M

ar

15

Ma

r

22

Ma

r

29

Ma

r

5 A

pr

12

Ap

r

19

Ap

r

26

Ap

r

3 M

ay

10

Ma

y

17

Ma

y

24

Ma

y

31

Ma

y

Maximum

Median

Minimum

%2 March: First cases

19 March: Start 1st State of Emergency

3 April:Start 2nd State of Emergency

18 April: Start 3rd State of Emergency

3 May:Start State of Calamity

4 May: Start 1st Phase of Deconfinement

9-13 April Limited

movement between

municipalities(Easter)

1-3 May Limited

movement between

municipalities(1st May)

1 M

ar

1 A

br

1 M

aio

1 J

un

11 March: WHO declares pandemic

15 March: Schools close

18 May: Start 2nd Phase of Deconfinement

16 March: First death

https://dataforgood.fb.com/


15/19

Figure 20: Proportion of the population “staying put” on Sundays between March 1st and May 31st by NUTS 3

1 March 8 March 15 March 22 March

29 March 5 April 12 April 19 April

26 April 3 May 10 May 17 May

24 May 31 May

Source: Facebook's “Data for Good” Initiative. Data provided by Carnegie Mellon University.

0 50 km 0 50 km 0 50 km 0 50 km

60.7%

9.7%

0 50 km 0 50 km 0 50 km 0 50 km

60.7%

9.7%

0 50 km 0 50 km 0 50 km 0 50 km

60.7%

9.7%

0 50 km 0 50 km

60.7%

9.7%


16/19

Figure 21: Proportion of the population “staying put” on Mondays between March 2nd and June 1st by NUTS 3

2 March 9 March 16 March 23 March

30 March 6 April 13 April 20 April

27 April 4 May 11 May 18 May

25 May 1 June

Source: Facebook's “Data for Good” Initiative. Data provided by Carnegie Mellon University. .

0 50 km 0 50 km 0 50 km 0 50 km

60.7%

9.7%

0 50 km 0 50 km 0 50 km 0 50 km

60.7%

9.7%

0 50 km 0 50 km 0 50 km 0 50 km

60.7%

9.7%

0 50 km 0 50 km

60.7%

9.7%


17/19

Technical Note: The mobility data from Facebook's "Data for Good" Initiative correspond to location updates collected from mobile devices of Facebook

application users that have the "location history" option turned on. Only location accuracy (GPS) data of less than 200 meters is considered and

if a user has multiple locations resulting from more than one associated mobile device, Facebook only considers the data with the highest

location accuracy. Obtaining results for the NUTS 3 level implies a minimum of 300 unique users per sub-region.

The proportion of the population “staying put” is measured by the number of Facebook users associated with a single 600mx600m reference

grid during 8am and 8pm on day x, requiring at least three occurrences during that time period. The reference grid, as a "residence" proxy, is

measured daily based on the largest number of locations observed between 8pm and midnight on day x-1 and between 0 am and 8 am on day

x, requiring at least three occurrences during that time period.

The information associated with the 600mx600m grids is allocated to the respective NUTS 3 sub-region. Since a grid can intercept more than

one sub-region, 9 sample points are generated in each grid, assigning 1/9 of the grid population to each point in the sample.

Facebook's "Data for Good" initiative aims to provide data for research on humanitarian issues and has allowed results to be published in

scientific articles particularly in the United States. Obviously, Statistics Portugal's use of this data source in the Statslab domain is not motivated

by any publicity motive, but by the public interest of the information. Statistics Portugal thanks researcher Miguel Godinho Matos1 for his support

in the analytical preparation of this information

___________

1 Associate Professor at Católica Lisbon School of Business & Economics and visiting research scholar at the Carnegie Mellon University.


18/19

Technical note

Data sources

Data on Deaths correspond to general deaths (all causes of death) occurring in the national territory since March 1st, 2020 and until the Tuesday of

the week prior to publication. The information is preliminary and is obtained from statistical operations of direct and exhaustive collection on deaths

occurring in Portuguese territory using facts that are subject to compulsory civil registration (death) in the Sistema Integrado do Registo e

Identificação Civil (SIRIC). In addition to administrative information obtained from Civil Register Offices, Statistics Portugal collects an additional set

of variables identified as statistically relevant to the National Statistical System (NSS) and the European Statistical System (EES). Data are recorded

and sent electronically, in compliance with the requirements set out by Statistics Portugal and laid down in liaison with the Instituto de Registos e

Notariado (IRN) and the Instituto de Gestão Financeira e Equipamentos da Justiça (IGFEJ).

Data on the number of confirmed cases are based on those published daily in the Directorate-General of Health COVID-19 Status Report for

the entire country and by municipality. The confirmed cases are referenced to the municipality of occurrence and correspond to the total of clinical

notifications in the SINAVE (National System of Epidemiological Surveillance) system. When the confirmed cases by municipality are fewer than 3, for

confidentiality reasons, data are not disclosed by the Directorate-General of Health. For the reference dates considered in this press release – June 3

– data by municipality corresponded, respectively, to 91% of confirmed cases in the national territory. This proportion reflects data confidentiality by

municipality, but also limitations in the process of spatial referencing of information.

The information on the labour market is based on the publication Unemployment Registered by Municipality - Monthly Statistics of the

Institute of Employment and Professional Training (IEFP). Monthly Registered Unemployment data refers to the number of registers during the month

for individuals aged 16 or over (subject to the reservations provided by law), registered in the Employment Centres to obtain a job as an employee,

who do not have a job and are immediately available for work. The monthly data of Placements refer to Job Vacancies (available jobs reported by

employers to the Job Centres) satisfied with candidates submitted by the Employment Centres.

Data on withdrawals at ATMs and purchases through Automatic Payment Terminals (TPA) are based on information recorded by Interbank Services

Society (SIBS) and comprise movements made on cards issued by national institutions. Data by municipality is based on the location of the ATM and

of the TPA.

The resident population data referenced to December 31, 2019 correspond to preliminary estimates, not yet disseminated.

http://smi.ine.pt/DocumentacaoMetodologica/Detalhes/1084

https://covid19.min-saude.pt/relatorio-de-situacao/

https://www.iefp.pt/estatisticas


19/19

Disseminated Indicators

Number of total deaths, by sex or age group

Number of deaths in the last 4 weeks per deaths in the same reference period

Number of confirmed cases of COVID-19 disease per 10 thousand inhabitants

Population density

Number of new confirmed cases of COVID-19 disease in the last 7 days per 10 thousand inhabitants

Proportion of resident population with 75 or more years old

National withdrawals at ATMs per inhabitant

National purchases through automatic payment terminals per inhabitant

Value of national purchases through automatic payment terminals in March 2020 compared to the same period of the previous year

Job placements per thousand inhabitants between 15 and 64 years old

Unemployed registered at IEFP employment centres throughout the month per thousand inhabitants between 15 and 64 years old

Number of unemployed registered in IEFP employment centres over March 2020 compared to the corresponding flow in the same period of the previous year


The location coefficient (LC) is obtained using the following formula:

1002

1

1

n

j

jjyxLC where:

jx

corresponds to the ratio of the number of confirmed cases of COVID-19 in each municipality j to the number of confirmed cases of COVID-19 for

the total country;

jy

corresponds to the ratio between the resident population in each municipality j and the total resident population in the country.

The Location coefficient varies between 0 and 100, with values closer to 100 reflecting greater inequality in the distribution of confirmed cases of

COVID-19 against the total resident population and, in this sense, indicates situations of greater territorial concentration.

The location curve (or Lorenz concentration curve) corresponds to a graphical representation that relates the cumulative distribution of two variables.

This representation also includes the straight line of equal distribution, and the greater the distance from it, the greater is the concentration of the

variable represented in the ordinate axis (in this analysis, the confirmed cases of COVID-19, by period of reference) versus the variable represented in

the abscissa axis (in this analysis, the total resident population).

Documents

COVID-19: a territorial view on demographic context and ...€¦ · 05/09/2020 · Terras de Bouro Carraz. de Ansiães Torre de Moncorvo Castelo de Paiva Lajes das Flores Ribeira