Wednesday, April 1, 2020

Mapping COVID-19: Follow-up

As a follow up to our previous blog entry describing how to import Covid-19 data into EViews and produce some maps/graphs of the data, this post will produce a couple more graphs similar to ones we've seen become popular across social media in recent days.

Table of Contents

  1. Deaths Since First Death
  2. One Week Difference

Deaths Since First Death

The first is a graph showing the 3 day moving average of the number of deaths per day since the first death was recorded in a country, for countries with a current number of deaths greater than 160:


Figure 1: 3-Day moving average

The graph shows that for most countries the growth rate of deaths (approximated by using log-scaling) is increasing, but at a slower rate. The code to produce this graph, including importing the death data from Johns Hopkins is:

    'import the death data from Johns Hopkins
    %url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
    
    'load up the url as a new page
    pageload(page=temp) {%url}
    
    'stack the page into a 2d panel
    pagestack(page=stack) _? @ *? *  
    
    'do some renaming and make the date series
    rename country_region country 
    rename province_state province
    rename _ deaths
    series date = @dateval(var01, "MM_DD_YYYY")
    
    'structure the page 
    pagestruct province country @date(date)
    
    'delete the original page
    pagedelete temp
    
    'create the panel page
    pagecreate(id, page=panel) country @date @srcpage stack
    
    'copy the deaths series to the panel page
    copy(c=sum) stack\deaths * @src @date country @dest @date country
    pagedelete stack
    
    'contract the page to only include countries with greater than 160 deaths
    pagecontract if @maxsby(deaths,country)>160
    
    'create a series containing the number of days since the first death was recorded in each country.  This series is equal to 0 if the number of deaths on a date is equal to the minimum number of deaths for that country (nearly always 0, but for China, the data starts after the first recorded death), and then counts up by one for dates after the minimum.
    series days = @recode(deaths=@minsby(deaths,country), 0, days(-1)+1)
    
    'contract the page so that days before the second recorded death in each country are removed
    pagecontract if days>0
    
    'restructure the page to be based on this day count rather than actual dates
    pagestruct(freq=u) @date(days) country
    
    'set sample to be first 45 days
    smpl 1 45
    
    'make a graph of the 3 day moving average of deaths
    freeze(d_graph) @movav(log(deaths),3).line(m, panel=c)
    d_graph.addtext(t, just(c)) Deaths Since First Death\n(3 day moving average, log scale)
    d_graph.addtext(br) Days
    d_graph.addtext(l) log(deaths)
    d_graph.legend columns(5)
    d_graph.legend position(-0.6,3.72)
    show d_graph
    

One Week Difference

The second graph is an interesting approach plotting the one-week difference in the number of new confirmed cases of COVID-19 against the total number of confirmed cases for each country, with both shown using log-scales. We have only included countries with more than 140 deaths, and have highlighted just three countries – China, South Korea and the US.


Figure 2: One week difference

The code to generate this graph is:

    'names of the three topics/files
    %topics = "confirmed deaths recovered"

    'loop through the topics
    for %topic {%topics}
        
        'build the url by taking the base url and then adding the topic in the middle
        %url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_" + %topic + "_global.csv"
    
        'load up the url as a new page
        pageload(page=temp) {%url}
        
        'stack the page into a 2d panel
        pagestack(page=stack_{%topic}) _? @ *? *
        
        'do some renaming and make the date series
        rename country_region country 
        rename province_state province
        rename _ {%topic}
        series date = @dateval(var01, "MM_DD_YYYY")
        
        'structure the page
        pagestruct province country @date(date)
        
        'delete the original page
        pagedelete temp
    next
    
    'create the panel page
    pagecreate(id, page=panel) country @date @srcpage stack_{%topic}
    
    'loop through the topics copying each from the 2D panel
    for %topic {%topics}
        copy(c=sum) stack_{%topic}\{%topic} * @src @date country @dest @date country
        pagedelete stack_{%topic}
    next
    
    'contract the page to only include countries with more than 140 deaths
    pagecontract if @maxsby(deaths, country)>140
    
    'make a group, called DATA, containing confirmed cases and the one week difference in confirmed cases
    group data confirmed confirmed-confirmed(-7)
    
    'set the sample to remove periods with fewer than 50 cases
    smpl if confirmed > 50
    
    'produce a panel plot of confirmed against 7 day difference in confirmed
    freeze(c_graph) data.xyline(panel=c)
    
    ' Add titles
    c_graph.addtext(t) "COVID-19: New vs. Total Cases\n(Countries with >140 deaths)"
    c_graph.addtext(bc, just(c)) "Total Confirmed Cases\n(log scale)"
    c_graph.addtext(l, just(c))"New Confirmed Cases (in the past week)\n(log scale)"
    c_graph.setelem(1) legend("")
    
    ' Adjust axis to use logs
    c_graph.axis(b) log
    c_graph.axis(l) log
    
    ' Adjust lines  -   remove lines after this if you want to show all countries
    c_graph.legend -display
    for !i = 1 to @rows(@uniquevals(country))
        c_graph.setelem(!i) linewidth(.75) linecolor(@rgb(192,192,192))
    next

    c_graph.setelem(8) linecolor(@rgb(128,64,0))
    c_graph.setelem(3) linecolor(@rgb(0,64,128))
    c_graph.setelem(15) linecolor(@rgb(0,128,0))
    
    'add some text
    c_graph.addtext(3.29, 1.92, font(Calibri,10))  "S. Korea"
    c_graph.addtext(4.87, 2.35, font(Calibri,10))  "China"
    c_graph.addtext(5.31, 0.23, font(Calibri,10))  "United States"
    
    show c_graph
    

3 comments:

  1. Hello EViews Team.
    Great Work. Felicitations.
    Very Thanks.

    ReplyDelete
  2. HELLO EViews Team. Please, how could l download the population by country data from the site of Johns Hopkins Whiting School of Engineering Center for Systems Science and Engineering? For example in an CSV file?
    Cordially

    ReplyDelete
  3. HELLO EViews Team. Please, on EViews how can we do a forecast by distribution?

    ReplyDelete