Updated
Data DGS:
Source: Direção-geral de Saúde through DSSG (https://github.com/dssg-pt/covid19pt-data/blob/master/data.csv)
App News
30-04-2020: App launched
06-05-2020: Mobility data available
08-05-2020: Lockdown period on plots
21-05-2020: App refresh button
02-06-2020: Add maps
10-06-2020 Mobility/growth correlation

Overview of the Situation in Portugal


Confirmed Cases

Alentejo

Algarve

Azores

Centro

LVT

Madeira

Norte


Plot options

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Marked regional asymmetry

Confirmed cases
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Total number of individuals with confirmed positive COVID-19 diagnosis.

Population infected

Percentage
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Total number of individuals with confirmed positive COVID-19 diagnosis / population. This is not the current number of infections, but the overall total.

Confirmed cases


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Confirmed - Daily total number of individuals with confirmed positive COVID-19 diagnosis.

Growth factor by region


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The growth factor = New cases today/new cases previous day. A growth factor of 1 might indicate the inflecton point on the logistic curve.

Overview of the Situation in Portugal


Total Confirmed Cases

Total Deaths

Mortality Rate

Deaths National Pop.

Current growth factor


The AIRCentre is developing this live platform as a response to the evolution of COVID19.

Using Portugal as a case study, we explore data to inform.

Components and goals
Basic indicators
  • To inform the general public regarding the current situation in Portugal.
  • Simulator
  • To assist decision-makers through the ability to simulate scenarios and estimate their consequences.
  • Analysis
  • To assist scientists with a platform for divulging the exploration of data trends which may increase our understanding of the disease and its transmission.

  • Plot options

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    Daily new cases

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    Daily new deaths

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    Daily hospitalized and in intensive care unit


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    Cumulative deaths and recovered


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    Active cases, diagnosed, deaths and recovered


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    Active - Daily total number of individuals who are COVID-19 contagious.

    Confirmed - Daily total number of individuals with confirmed positive COVID-19 diagnosis.

    Obitos (deaths) - Daily total number of individuals who pass away due to COVID-19.

    Recuperados (recovered) - Daily total number of individuals who recover from COVID-19.

    Growth Factor


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    The growth factor = New cases today/new cases previous day

    Mortality


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    Percentage of COVID-19-infected individuals who pass away.

    Fluctuation of mortality rate (%/day)


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    Daily percentage variation of COVID-19-infected individuals who pass away.

    Contamination


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    Percentage of the national population COVID-19-infected.

    Contamination Rate (%/day)


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    Daily percentage variation of the national pop. contaminated by COVID-19.

    Data source: Direção-geral de Saúde through DSSG (https://github.com/dssg-pt/covid19pt-data/blob/master/data.csv)

    Overview of the Situation in Portugal


    Scientists have hypothesized that air temperature may play a role in the spread of COVID-19, for example:

  • Wang et al
  • Sajadi et al
  • Wang et al
  • Ma et al
  • We are exploring that association in Portugal.


    Plot options
    (Region tab only)

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    Data source (meteorology): Instituto Português do Mar e da Atmosfera

    Maximum and minimum air temperature

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    Growth factor by region

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    Data source (meteorology): Instituto Português do Mar e da Atmosfera

    Frequency distribution of variables

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    Note the different type of distribution of growth rate (growth factor) in comparison to temperature data. Due to the logarithmic nature of the distribution of growth factor, this is log converted before correlated with temperature.


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    Data source (meteorology): Instituto Português do Mar e da Atmosfera

    Association between temperature and growth factor

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    As data is updted on a daily basis, the correlogram above will automatically illustrate the correlations between the log of growth factor and temperature, at the regional level.

    Reading the Correlogram
  • Circles represent the association between the variables names at the end of each line.
  • Positive correlations are displayed in blue and negative correlations in red color.
  • Color intensity and the size of the circle are proportional to the correlation coefficients.
  • If it occurs, a correlation between growth factor and temperature shall show in the first row.
  • Work in Progress
  • As more data becomes available, we will also include the corresponding significance tests.
  • We are continually developing the analysis in response to the data.
  • We welcome feedback.
  • Data source: Direção-geral de Saúde through DSSG (https://github.com/dssg-pt/covid19pt-data/blob/master/data.csv)

    Overview of the Situation in Portugal


    Scientists have hypothesized that mobility may play a role in the spread of COVID-19.

    Lockdown and other mobility restriction policies were based on that hypothesis.

    We are exploring that association in Portugal.


    Over the factual data, sourced from Google Inc., we interpret the behavioural trends of the Portuguese population. We also mark official resolutions. Several trends are easy to visually identify on the data:

    March 8th
  • Surge in grocery and pharmacy shopping;
  • March 12th
  • The Portuguese started self-isolating at home;
  • March 18th
  • The Portuguese authorities declared the State of Emergency, with mandatory lockdown for most citizens/businesses;
  • May 2nd
  • The Portuguese authorities ended the State of Emergency. Businesses start reopening;
  • The data seems to suggest that the effect of the mandatory lockdown to have been minimal as most people had already self-isolated at home.



    Plot options
    National tab

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    Correlation tab

    Data source: Google (https://www.google.com/covid19/mobility/index.html)

    Correlation between mobility and growth factor in Centro Region

    Adjust offset days on the sidebar to see how that affects the correlations

    Sample size
    n =
    Correlation method

    Spearman

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    Reading the Correlogram with Significance Test
  • Each ellipse represents the cloud of points for the association between the variables names at the end of each row/column.
  • Positive correlations are displayed in blue and negative correlations in red.
  • Color intensity is proportional to the correlation coefficients.
  • X identifies correlations with no statistical significance at the 0.01 level (p-value > 0.01).

  • Correlation Strength (r squared)

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    Press Play

    Or manually adjust offset days with the slider to see how that affects the correlations

    Offset days - Naturally, the consequesnces of contamination growth due to variations in mobility are reflected not the same day, but some days later, when the new cases are identified. How many days ahead shall we look for correlation? This is the value 'offset days'.

    The slider on the left allows you to explore which number of offset days provides the highest and more meaningful correlations.

    Note that mobility from the residence is inversely correlated to the growth of contamination, while mobility from public places is directly related to the propagation rate.

    As data is updted on a daily basis, the correlogram above will automatically update.

    Work in Progress
  • We are continually developing the analysis in response to the data.
  • We welcome feedback.
  • Notes

    Unfortunately there is not enough mobility data for equivalent analysis in other regions.

    Source/more information: https://www.google.com/covid19/mobility/data_documentation.html?hl=en

    Data source: Google (https://www.google.com/covid19/mobility/index.html)

    Variation of mobility

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    About this data

    Changes for each day are compared to a baseline value for that day of the week:

  • The baseline is the median value, for the corresponding day of the week, during the 5-week period Jan 3–Feb 6, 2020.
  • The datasets show trends over several months with the most recent data representing approximately 2-3 days ago—this is how long it takes to produce the datasets.
  • Place Categories
    Grocery & pharmacy

    'Mobility trends for places like grocery markets, food warehouses, farmers markets, specialty food shops, drug stores, and pharmacies.'

    Parks

    'Mobility trends for places like local parks, national parks, public beaches, marinas, dog parks, plazas, and public gardens.'

    Transit stations

    'Mobility trends for places like public transport hubs such as subway, bus, and train stations.'

    Retail & recreation

    'Mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.'

    Residential

    'Mobility trends for places of residence.'

    Workplaces

    'Mobility trends for places of work.'

    Source/more information: https://www.google.com/covid19/mobility/data_documentation.html?hl=en

    Data source: COVID-19 Data Hub (https://covid19datahub.io)

    Overview of Portugal's situation in the world context


    COVID-19 Mortality by country


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    COVID-19 Mortality: Deaths / confirmed cases

    Only countries with reported deaths and confirmed cases are shown