Question

How to spread the COVİD 19 virus worldwide in the MATLAB using the interpolation method,please help me???

Can you explain the code...

Answer 1

s = dir("*.csv"); s = s(arrayfun(@(x) ~matches(x.name,"2019_nCoV_data.csv"), s)); filenames = arrayfun(@(x) string(x.name), s) 

filenames = 6×1 string array "COVID19_line_list_data.csv" "COVID19_open_line_list.csv" "covid_19_data.csv" "time_series_covid_19_confirmed.csv" "time_series_covid_19_deaths.csv" "time_series_covid_19_recovered.csv" 

• covid_19_data.csv - this is the main file - daily level data of global cases by province/state, from Jan 22, 2020
• time_series_covid_19_confirmed.csv - time series data of confirmed cases
• time_series_covid_19_deaths.csv - time series data of cumulative number of deaths
• time_series_covid_19_recovered.csv - time series data of cumulative number of recovered cases
• COVID19_line_list_data.csv - individual level information
• COVID19_open_line_list.csv - individual level information

Mapping Confirmed Cases Globally

Let's visualize the number of confirmed cases on a map. We start by loading time_series_covid_19_confirmed.csv which contains latitude and longitude variables we need for mapping. I also decided to keep the variable names as is, rather than letting MATLAB convert them to valid MATLAB identifiers, because some of the column names are dates.

opts = detectImportOptions(filenames(4), "TextType","string"); opts.VariableNamesLine = 1; opts.DataLines = [2,inf]; opts.PreserveVariableNames = true; times_conf = readtable(filenames(4),opts); 

The dataset contains Province/State variable, but we want to aggregate the data at the Country/Region level. Before we do so, we need to clean up the data a bit. Please note that I have use the () notation because the variable names are not valid MATLAB identifiers.

times_conf.("Country/Region")(times_conf.("Country/Region") == "China") = "Mainland China"; times_conf.("Country/Region")(times_conf.("Country/Region") == "Czechia") = "Czech Republic"; times_conf.("Country/Region")(times_conf.("Country/Region") == "Iran (Islamic Republic of)") = "Iran"; times_conf.("Country/Region")(times_conf.("Country/Region") == "Republic of Korea") = "Korea, South"; times_conf.("Country/Region")(times_conf.("Country/Region") == "Republic of Moldova") = "Moldova"; times_conf.("Country/Region")(times_conf.("Country/Region") == "Russian Federation") = "Russia"; times_conf.("Country/Region")(times_conf.("Country/Region") == "Taipei and environs") = "Taiwan"; times_conf.("Country/Region")(times_conf.("Country/Region") == "Taiwan*") = "Taiwan"; times_conf.("Country/Region")(times_conf.("Country/Region") == "United Kingdom") = "UK"; times_conf.("Country/Region")(times_conf.("Country/Region") == "Viet Nam") = "Vietnam"; times_conf.("Country/Region")(times_conf.("Province/State") == "St Martin") = "St Martin"; times_conf.("Country/Region")(times_conf.("Province/State") == "Saint Barthelemy") = "Saint Barthelemy"; 

Now we can use groupsummary to aggregate the data by Country/Region by summing the confirmed cases and averaging the latitudes and longitudes.

vars = times_conf.Properties.VariableNames; times_conf_country = groupsummary(times_conf,"Country/Region",{'sum','mean'},vars(3:end)); 

The output contains unnecessary columns, such as sums of latitudes and longitudes or means of confirmed cases. Let's remove those variables, and also remove 'sum_' or 'mean_' prefixes from the variables we keep.

vars = times_conf_country.Properties.VariableNames; vars = regexprep(vars,"^(sum_)(?=L(a|o))","remove_"); vars = regexprep(vars,"^(mean_)(?=[0-9])","remove_"); vars = erase(vars,{'sum_','mean_'}); times_conf_country.Properties.VariableNames = vars; times_conf_country = removevars(times_conf_country,[{'GroupCount'},vars(contains(vars,"remove_"))]); 

Because Mainland China is so disproportionately large, we want to exclude it from our visualization.

times_conf_exChina = times_conf_country(times_conf_country.("Country/Region") ~= "Mainland China",:); vars = times_conf_exChina.Properties.VariableNames; 

Let's use geobubble to visualize the first and the last dates in the dataset. Since the column names of numerica data are dates, I can simply pick the first date and the last date to show the maps together. Please note that geobubble would show a bubble for zero values, and therefore we need to remove rows with zero values if we don't want to show bubbles for zero cases.

figure t = tiledlayout("flow"); for ii = [4, length(vars)] times_conf_exChina.Category = categorical(repmat("<100",height(times_conf_exChina),1)); times_conf_exChina.Category(table2array(times_conf_exChina(:,ii)) >= 100) = ">=100"; nexttile tbl = times_conf_exChina(:,[1:3, ii, end]); tbl(tbl.(4) == 0,:) = []; gb = geobubble(tbl,"Lat","Long","SizeVariable",vars(ii),"ColorVariable","Category"); gb.BubbleColorList = [1,0,1;1,0,0]; gb.LegendVisible = "off"; gb.Title = "As of " + vars(ii); gb.SizeLimits = [0, max(times_conf_exChina.(vars{length(vars)}))]; gb.MapCenter = [21.6385 36.1666]; gb.ZoomLevel = 0.3606; end title(t,["COVID-19 Confirmed Cases outside Mainland China"; ... "Country/Region with 100+ cases highlighted in red"])