COVID Visualizations (COVIS) • SaviR

Data: The Frankenstein Dataset

onetable - stored metadata with iso2 and iso3 codes, country names, WHO/DoS/World Bank regions, and UN population estimates
get_covid_df(sources = c("all", "WHO", "WHO+JHU", "WHO+Primary")) - COVID cases/deaths dataframe from WHO and other sources. calc_add_risk() - Add risk matrix calculations to dataframe (df)
get_vax() - Get vaccination data from Our World In Data (OWID)
get_combined_table(type = c("WHO", "Both", "legacy"), geometry=FALSE) - Combine all of the above into one large df, optionally add geometry

This snippet produces the master df with all the variables required for all of the following graphics.

# Load in data to create visuals
df_who <- get_combined_table("WHO") # China includes Taiwan, Hong Kong, and Macau data

# Data for visuals are typically as of Sunday of the current week
# but this can be determined dynamically using WHO data.
sunday_date <- df_who |>
  distinct(date, dow = weekdays(date)) |>
  arrange(desc(date)) |>
  filter(dow == "Sunday") |>
  slice(1) |>
  pull(date)

Plots

plot_epicurve(df, transparent = T)

When creating epi curves for the WHO regions, the “plot_epicurve” function should take in the df with only “WHO” observations in order to match the same curve on the WHO Coronavirus Dashboard. The individual epi curves for each region can be run simply by filtering for one WHO region in the df. The function will detect that the region column has only one region and will produce the epi curve accordingly.

When creating epi curves for the DoS regions, the plot_epicurve function should take in the df with “Both” observations. Similarly to the WHO epi curves, individual epi curves can also be produce by filtering to one DoS region.

# Epi Curves for WHO -- Global
epi_curve_global <- plot_epicurve(df_who, transparent = T)


epi_curve_global


# Epi Curves for DoS -- Global
state_epi_curve <- plot_epicurve(filter(df_who, date <= sunday_date),
                                 type = "cases", 
                                 by_cat = "State Region")
state_epi_curve

Inset plots

We can also pass inset = TRUE to plot an inset plot for the most recent weeks
- By default, the inset for the case plot is slightly left and cener in the plot window
- Deaths plot inset is slightly up and to the right
Looks at the past 9 weeks of data in the inset, or min(date) + weeks(1), whichever is later

inset_epi_curve <- df_who |>
  filter(date <= sunday_date) |>
  plot_epicurve(type = "cases", by_cat = "State Region", transparent = FALSE, inset = TRUE)

inset_epi_curve

# Regions
for (r in unique(na.omit(df_who$who_region))) {
  epi_curve_regional <- plot_epicurve(filter(df_who, who_region == r), transparent = F)


  print(epi_curve_regional)
}

plot_riskmatrix(df)

Note for labeling purposes, the labels variable should be added to the dataframe specifying the top 10 countries for weekly average incidence, week case percent change, and weekly total cases. Exclusion criteria: Countries with population less than 10 million and countries with weekly cases totals less than 100.

global_risk_matrix <- df_who %>%
  filter(date == sunday_date) %>%
  filter(population >= 10000000) %>% # Exclude population less than 10M
  filter(week_case >= 100) %>%
  mutate(
    rank_inc = dense_rank(desc(week_case_incidence)),
    rank_per = dense_rank(desc(percent_change_case)),
    rank_cas = dense_rank(desc(week_case))
  ) %>%
  mutate(labels = case_when(
    rank_inc %in% 1:10 ~ country,
    rank_per %in% 1:10 ~ country,
    rank_cas %in% 1:10 ~ country
  )) %>%
  plot_riskmatrix()


global_risk_matrix

global_vax_coverage <- df_who %>%
  filter(date <= sunday_date) %>%
  filter(date == max(date)) %>%
  filter(!is.na(state_region)) %>%
  plot_vaxcoverage(by_cat = "State Region")

global_vax_coverage

Maps

By default, get_combined_table() does not return geometry. This is preferable, since the size of the table is quite large and the geometry list column takes up a lot of space in memory. However, if we want to map these data, we can either call get_combined_table("Both", geometry = TRUE), or join with country_coords after filtering, like so:

# Filter to only observations with date as of Sunday
# and join in geometry
df_who_mapping <- df_who %>%
  filter(date == sunday_date) %>%
  left_join(select(country_coords, id, geometry), by = "id")

map_burden(df, region, time_step)

The “map_burden” function takes in a pre-filtered data.frame and computes and displays average incidence for each country over the time period selected.
The region argument is optional, but it is used to specify a specific DoS/WHO region to zoom to, if desired.
time_step is used to specify the days used to average incidence over. This is always relative to the latest date in the data.frame.
bin_breaks and bin_colors can be used to over-ride sensible defaults for incidence cutpoints and color scheme, if desired.

burden_map_global <- df_who |>
  filter(date <= sunday_date) |>
  map_burden()


burden_map_global


for (r in unique(na.omit(df_who$who_region))) {
  burden_map_regional <- df_who |>
    filter(who_region == r, date <= sunday_date) |>
    map_burden(region = r)


  print(burden_map_regional)
}

and an example of how to produce a 14d map:

burden_map_global_14 <- df_who |>
  filter(date <= sunday_date) |>
  map_burden(time_step = 14)


burden_map_global_14

We can also over-ride the incidence cut-points, like so:

# same map with 0-1, 1-5, 5-10, 10+ breaks
burden_map_global_14_2 <- df_who |>
  filter(date <= sunday_date) |>
  map_burden(
    time_step = 14,
    bin_breaks = c(0, 1, 5, 10, Inf),
    bin_colors = c("0- <1" = "#f1e5a1", "1- <5" = "#e7b351", "5- <10" = "#d26230", "10+" = "#aa001e")
  )

burden_map_global_14_2

map_trend(df, region, time_step = 7)

The “map_trend” function requires a result column where weekly percent change is converted into factors using “cut”.

The default behavior is to visualize a global trend map, but passing a data frame filtered to a specific region and providing the region name as a region argument will change the bounding box accordingly.

A different time_step can be provided. The default assumes you are comparing two 7d periods, but this can be increased or decreased accordingly.

trend_map_global <- df_who |>
  filter(date <= sunday_date) |>
  map_trend()


trend_map_global

for (r in unique(na.omit(df_who$who_region))) {
  trend_map_reg <- df_who |>
  filter(date <= sunday_date) |>
    map_trend(region = r)

  print(trend_map_reg)
}

and an example producing the same with different time_step:

df_who |>
  filter(date <= sunday_date) |>
  map_trend(time_step = 14)

map_vaccinations(df, vac_type = c("People", "Fully"))

global_vax_map <- df_who_mapping %>%
  mutate(result = cut(people_vaccinated_per_hundred, breaks = c(0, 1, 3, 10, 30, Inf))) %>%
  group_by(country) %>%
  filter(!is.na(result)) %>%
  filter(date == max(date)) %>%
  ungroup() %>%
  map_vaccinations(., vac_type = "People")

global_vax_map

Tables

table_10mostcases(df, time_step = 7, region = NULL, data_as_of = NULL)

This function presents a top-10 table of countries based on cases reported in the past time_step.
The default is a 7 day period, but this can be adjusted. Additionally, you can tweak the title and data-as-of footnote using those variables respectively, or they will be inferred internally.

7-day Table

df_who |>
  filter(date <= sunday_date) |>
  table_10mostcases(data_as_of = format(sunday_date, "%B %d, %Y"))

Country/ Area	New Cases Past 7 Days	% Change Past 7 Days¹
10 Countries/ Areas with Most New Cases
Brazil	91,120	577.7
Russia	55,802	−70.2
Greece	8,485	−54.4
United Kingdom	6,854	−27.3
Poland	5,750	−16.5
Italy	5,193	−61.2
New Zealand	3,809	−55.7
France	3,052	−41.0
Lithuania	2,759	−30.2
Norway	2,628	−50.6
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior

14-day Table

df_who |>
  filter(date <= sunday_date) |>
  table_10mostcases(time_step = 14)

Country/ Area	New Cases Past 14 Days	% Change Past 14 Days¹
10 Countries/ Areas with Most New Cases
Russia	243,221	−33.5
Brazil	104,566	-
Greece	27,100	−52.7
Italy	18,593	−86.1
United Kingdom	16,281	−57.5
Poland	12,633	−85.8
New Zealand	12,403	−19.0
Czechia	10,030	−81.9
Malaysia	8,406	−24.2
France	8,225	−12.5
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Percent change in cases of most recent 14 days to 14 days prior

By region

for (r in unique(na.omit(df_who$who_region))) {
  tab_out <- df_who |>
    filter(date <= sunday_date, who_region == r) |>
    table_10mostcases(region = r, data_as_of = format(Sys.Date(), "%B %d, %Y"))
  
  print(htmltools::tagList(tab_out))
}

Country/ Area	New Cases Past 7 Days	% Change Past 7 Days¹
10 (AMRO) Countries/ Areas with Most New Cases
Brazil	91,120	577.7
Ecuador	1,143	465.8
Colombia	554	185.6
Argentina	492	−64.9
Guatemala	267	413.5
Costa Rica	246	215.4
Uruguay	155	−69.5
Panama	99	80.0
Chile	98	−97.7
Nicaragua	64	1,180.0
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior

Country/ Area	New Cases Past 7 Days	% Change Past 7 Days¹
10 (EMRO) Countries/ Areas with Most New Cases
Afghanistan	0	-
United Arab Emirates	0	-
Bahrain	0	-
Djibouti	0	-
Egypt	0	-
Iran	0	-
Iraq	0	-
Jordan	0	-
Kuwait	0	-
Lebanon	0	-
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior

Country/ Area	New Cases Past 7 Days	% Change Past 7 Days¹
10 (AFRO) Countries/ Areas with Most New Cases
Mauritius	1,293	−4.7
South Africa	52	23.8
Madagascar	51	131.8
Ethiopia	22	−35.3
Niger	19	1,800.0
Algeria	18	−50.0
Mozambique	14	−69.6
Senegal	14	180.0
Gabon	12	1,100.0
Cote d’Ivoire	8	100.0
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior

Country/ Area	New Cases Past 7 Days	% Change Past 7 Days¹
10 (EURO) Countries/ Areas with Most New Cases
Russia	55,802	−70.2
Greece	8,485	−54.4
United Kingdom	6,854	−27.3
Poland	5,750	−16.5
Italy	5,193	−61.2
France	3,052	−41.0
Lithuania	2,759	−30.2
Norway	2,628	−50.6
Czechia	2,259	−70.9
Sweden	2,200	−52.6
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior

Country/ Area	New Cases Past 7 Days	% Change Past 7 Days¹
10 (WPRO) Countries/ Areas with Most New Cases
New Zealand	3,809	−55.7
Brunei Darussalam	135	−85.0
China	89	−79.7
American Samoa	0	-
Australia	0	-
Cook Islands	0	-
Fiji	0	-
Micronesia	0	-
Guam	0	-
Japan	0	-
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior

Country/ Area	New Cases Past 7 Days	% Change Past 7 Days¹
10 (SEARO) Countries/ Areas with Most New Cases
Thailand	1,884	−41.1
Indonesia	77	−74.2
Bangladesh	42	−26.3
India	34	−79.4
Bhutan	0	-
Sri Lanka	0	-
Maldives	0	-
Burma	0	-
Nepal	0	-
Korea (North)	0	-
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior

table_10incidence(df, time_step = 7, region = NULL, data_as_of = NULL)

Similar to the above, but computes top-10 countries baseed on incidence over past time_step.

7-day table

df_who |>
  filter(date <= sunday_date) |>
  table_10incidence(data_as_of = format(sunday_date, "%B %d, %Y"))

Country/ Area	Incidence Per 100,000¹	% Change Past 7 Days²
10 Countries/ Areas with Highest Incidence
Lithuania	14.3	−30.2
Mauritius	14.2	−4.7
Cyprus	12.9	−35.0
Greece	11.7	−54.4
New Zealand	10.5	−55.7
Luxembourg	10.5	−15.4
Croatia	7.7	−31.5
Norway	6.9	−50.6
Brazil	6.0	577.7
Estonia	5.6	−51.4
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Average daily incidence per 100,000 in past 7 days
² Percent change in cases of most recent 7 days to 7 days prior

14-day table

df_who |>
  filter(date <= sunday_date) |>
  table_10incidence(time_step = 14)

Country/ Area	Incidence Per 100,000¹	% Change Past 14 Days²
10 Countries/ Areas with Highest Incidence
Greece	18.6	−52.7
Lithuania	17.4	−78.9
New Zealand	17.1	−19.0
Brunei Darussalam	16.4	−33.7
Cyprus	16.3	−52.8
Mauritius	14.6	159.3
Russia	12.0	−33.5
Luxembourg	11.4	−60.4
Norway	10.5	−42.2
Croatia	9.5	−80.3
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Average daily incidence per 100,000 in past 14 days
² Percent change in cases of most recent 14 days to 14 days prior

By Region

for (r in unique(na.omit(df_who$who_region))) {
  tab_out <- df_who %>%
    filter(date <= sunday_date, who_region == r) |>
    table_10incidence(region = r, data_as_of = format(Sys.Date(), "%B %d, %Y"))

  print(htmltools::tagList(tab_out))
}

Country/ Area	Incidence Per 100,000¹	% Change Past 7 Days²
10 (AMRO) Countries/ Areas with Highest Incidence
Brazil	6.0	577.7
Turks and Caicos Islands	4.1	85.7
Ecuador	0.9	465.8
Costa Rica	0.7	215.4
Uruguay	0.6	−69.5
British Virgin Islands	0.5	-
Panama	0.3	80.0
Guatemala	0.2	413.5
Argentina	0.2	−64.9
Colombia	0.2	185.6
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Average daily incidence per 100,000 in past 7 days
² Percent change in cases of most recent 7 days to 7 days prior

Country/ Area	Incidence Per 100,000¹	% Change Past 7 Days²
10 (EMRO) Countries/ Areas with Highest Incidence
Afghanistan	0.0	-
United Arab Emirates	0.0	-
Bahrain	0.0	-
Djibouti	0.0	-
Egypt	0.0	-
Iran	0.0	-
Iraq	0.0	-
Jordan	0.0	-
Kuwait	0.0	-
Lebanon	0.0	-
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Average daily incidence per 100,000 in past 7 days
² Percent change in cases of most recent 7 days to 7 days prior

Country/ Area	Incidence Per 100,000¹	% Change Past 7 Days²
10 (AFRO) Countries/ Areas with Highest Incidence
Mauritius	14.2	−4.7
Gabon	0.1	1,100.0
Madagascar	0.0	131.8
Mauritania	0.0	500.0
South Africa	0.0	23.8
Senegal	0.0	180.0
South Sudan	0.0	33.3
Niger	0.0	1,800.0
Zimbabwe	0.0	33.3
Mozambique	0.0	−69.6
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Average daily incidence per 100,000 in past 7 days
² Percent change in cases of most recent 7 days to 7 days prior

Country/ Area	Incidence Per 100,000¹	% Change Past 7 Days²
10 (EURO) Countries/ Areas with Highest Incidence
Lithuania	14.3	−30.2
Cyprus	12.9	−35.0
Greece	11.7	−54.4
Luxembourg	10.5	−15.4
Croatia	7.7	−31.5
Norway	6.9	−50.6
Estonia	5.6	−51.4
Russia	5.5	−70.2
Slovenia	4.2	−56.5
Czechia	3.1	−70.9
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Average daily incidence per 100,000 in past 7 days
² Percent change in cases of most recent 7 days to 7 days prior

Country/ Area	Incidence Per 100,000¹	% Change Past 7 Days²
10 (WPRO) Countries/ Areas with Highest Incidence
New Zealand	10.5	−55.7
Brunei Darussalam	4.3	−85.0
China	0.0	−79.7
American Samoa	0.0	-
Australia	0.0	-
Cook Islands	0.0	-
Fiji	0.0	-
Micronesia	0.0	-
Guam	0.0	-
Japan	0.0	-
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Average daily incidence per 100,000 in past 7 days
² Percent change in cases of most recent 7 days to 7 days prior

Country/ Area	Incidence Per 100,000¹	% Change Past 7 Days²
10 (SEARO) Countries/ Areas with Highest Incidence
Thailand	0.4	−41.1
Indonesia	0.0	−74.2
Bangladesh	0.0	−26.3
India	0.0	−79.4
Bhutan	0.0	-
Sri Lanka	0.0	-
Maldives	0.0	-
Burma	0.0	-
Nepal	0.0	-
Korea (North)	0.0	-
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of March 11, 2025
¹ Average daily incidence per 100,000 in past 7 days
² Percent change in cases of most recent 7 days to 7 days prior

table_10percentchange(df, time_step = 7, second_time_step = 28, region = NULL, data_as_of = NULL)

Works similarly to the above, but selects top 10 countries based on percent change over time_step.
There’s an additional argument second_time_step that specifies a second (normally longer) period to compute a percent change over.
The default visualization is using weekly change and 4 week change.

This table is usually filtered to only countries over a certain population size, but this should be handled
externally.

Global table (7d and 28d pct change)

df_who %>%
  filter(date <= sunday_date, population >= 10000000) |>
  table_10percentchange(data_as_of = format(sunday_date, "%B %d, %Y"))

Country/ Area	% Change Past 7 Days¹	% Change Past 28 Days²
10 Countries/ Areas with Highest Percent Change In Past 7 Days
Niger	1,800.0	900.0
Venezuela	933.3	-
Nigeria	600.0	700.0
Brazil	577.7	-
Ecuador	465.8	−61.9
Guatemala	413.5	553.8
Colombia	185.6	−85.5
Senegal	180.0	−70.9
Madagascar	131.8	27.9
Cote d'Ivoire	100.0	−33.3
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior
² Percent change in cases of most recent 28 days to 28 days prior

Global table (14d and 28d pct change)

df_who %>%
  filter(date <= sunday_date, population >= 10000000) |>
  table_10percentchange(time_step = 14)

Country/ Area	% Change Past 14 Days¹	% Change Past 28 Days²
10 Countries/ Areas with Highest Percent Change In Past 14 Days
Mozambique	2,900.0	−21.5
Guatemala	1,419.0	553.8
Madagascar	421.4	27.9
Angola	400.0	−96.8
Kenya	375.0	64.3
Congo DR	100.0	−97.4
Niger	100.0	900.0
South Africa	64.9	9.4
Algeria	58.8	−30.7
Zimbabwe	55.6	−76.8
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Percent change in cases of most recent 14 days to 14 days prior
² Percent change in cases of most recent 28 days to 28 days prior

Regional Tables

for (r in unique(na.omit(df_who$who_region))) {
  tab_out <- df_who %>%
    filter(date <= sunday_date, population >= 100000, who_region == r) |> # Exclude population less than 100,000
    table_10percentchange(region = r, data_as_of = format(sunday_date, "%B %d, %Y")) 
    
  print(htmltools::tagList(tab_out))
}

Country/ Area	% Change Past 7 Days¹	% Change Past 28 Days²
10 (AMRO) Countries/ Areas with Highest Percent Change In Past 7 Days
Nicaragua	1,180.0	19.0
Venezuela	933.3	-
Brazil	577.7	-
Ecuador	465.8	−61.9
Guatemala	413.5	553.8
El Salvador	250.0	−24.4
Costa Rica	215.4	−74.5
Barbados	200.0	−69.4
Colombia	185.6	−85.5
Panama	80.0	−67.8
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior
² Percent change in cases of most recent 28 days to 28 days prior

Country/ Area	% Change Past 7 Days¹	% Change Past 28 Days²
10 (EMRO) Countries/ Areas with Highest Percent Change In Past 7 Days
Afghanistan	-	-
United Arab Emirates	-	-
Bahrain	-	-
Djibouti	-	-
Egypt	-	-
Iran	-	-
Iraq	-	-
Jordan	-	-
Kuwait	-	-
Lebanon	-	-
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior
² Percent change in cases of most recent 28 days to 28 days prior

Country/ Area	% Change Past 7 Days¹	% Change Past 28 Days²
10 (AFRO) Countries/ Areas with Highest Percent Change In Past 7 Days
Niger	1,800.0	900.0
Gabon	1,100.0	-
Nigeria	600.0	700.0
Mauritania	500.0	−71.4
Senegal	180.0	−70.9
Madagascar	131.8	27.9
Cote d’Ivoire	100.0	−33.3
South Sudan	33.3	1,433.3
Zimbabwe	33.3	−76.8
South Africa	23.8	9.4
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior
² Percent change in cases of most recent 28 days to 28 days prior

Country/ Area	% Change Past 7 Days¹	% Change Past 28 Days²
10 (EURO) Countries/ Areas with Highest Percent Change In Past 7 Days
North Macedonia	37.5	68.4
Ukraine	26.2	137.7
Azerbaijan	0.0	−51.1
Luxembourg	−15.4	24.0
Poland	−16.5	392.4
Ireland	−18.2	−42.9
United Kingdom	−27.3	−35.0
Romania	−27.6	−16.3
Lithuania	−30.2	332.8
Croatia	−31.5	339.5
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior
² Percent change in cases of most recent 28 days to 28 days prior

Country/ Area	% Change Past 7 Days¹	% Change Past 28 Days²
10 (WPRO) Countries/ Areas with Highest Percent Change In Past 7 Days
New Zealand	−55.7	−83.7
China	−79.7	−86.8
Brunei Darussalam	−85.0	−54.5
Australia	-	-
Fiji	-	-
Micronesia	-	-
Guam	-	-
Japan	-	-
Cambodia	-	−97.5
Kiribati	-	-
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior
² Percent change in cases of most recent 28 days to 28 days prior

Country/ Area	% Change Past 7 Days¹	% Change Past 28 Days²
10 (SEARO) Countries/ Areas with Highest Percent Change In Past 7 Days
Bangladesh	−26.3	−94.7
Thailand	−41.1	−69.3
Indonesia	−74.2	−60.4
India	−79.4	−85.0
Bhutan	-	-
Sri Lanka	-	−74.1
Maldives	-	-
Burma	-	−72.1
Nepal	-	-
Korea (North)	-	-
Data Source: WHO Coronavirus Disease (COVID-19) Dashboard
Data as of February 23, 2025
¹ Percent change in cases of most recent 7 days to 7 days prior
² Percent change in cases of most recent 28 days to 28 days prior

table_10vaccinations(df, type = c("Global", "Region"), run_date)

The “table_10vaccinations” function takes looks for a country, value1 (people vaccinated per hundred), and a value2 (daily vaccinations per hundred) column. Note as vaccination reporting has gaps, the df must be sliced by country and the most recent date with people_vaccinated_per_hundred value (if there is one).

df_who %>%
  filter(date <= sunday_date) %>%
  filter(population > 1000000) %>%
  group_by(country) %>%
  filter(!is.na(people_vaccinated_per_hundred)) %>%
  filter(date == max(date)) %>%
  ungroup() %>%
  select(country = who_country, value1 = people_vaccinated_per_hundred, value2 = daily_vaccinations_per_hundred) %>%
  arrange(desc(value1)) %>%
  head(10) %>%
  table_10vaccinations(., run_date = format(sunday_date, "%B %d, %Y"))

Country/ Area	People Vaccinated per 100 People³	Daily Vaccines Administered per 100 People⁴
Top 10 Countries/ Areas with Highest Vaccination per 100 People^1,2
United Arab Emirates	105.8	NA
Qatar	105.8	NA
Cuba	96.3	NA
Portugal	95.3	NA
Chile	92.3	NA
Nicaragua	92.2	NA
Viet Nam	91.9	NA
China	91.9	NA
Singapore	91.5	NA
Cambodia	91.3	NA
Data as of February 23, 2025
¹ Countries with population size less than or equal to 1 million were excluded
² People vaccinated per 100 people represents total population (all ages)
³ Number of people out of 100 who received at least one vaccine dose; does not represent percent of population that completed primary vaccination series
⁴ Vaccine doses administered per day (7 day rolling average); does not represent number of people vaccinated

for (r in unique(na.omit(df_who$who_region))) {
  tab_out <- df_who %>%
    filter(date <= sunday_date) %>%
    filter(population > 100000) %>% # Exclude population less than 100,000
    filter(who_region == r) %>%
    group_by(country) %>%
    filter(!is.na(people_vaccinated_per_hundred)) %>%
    arrange(date) %>%
    top_n(1, date) %>%
    distinct(id, .keep_all = T) %>%
    select(country = who_country, value1 = people_vaccinated_per_hundred, value2 = daily_vaccinations_per_hundred) %>%
    arrange(desc(value1)) %>%
    head(10) %>%
    table_10vaccinations(., type = r, run_date = format(sunday_date, "%B %d, %Y"))

  print(htmltools::tagList(tab_out))
}

Country/ Area	People Vaccinated per 100 People³	Daily Vaccines Administered per 100 People⁴
10 (AMRO) Countries/ Areas with Highest Vaccination per 100 People^1,2
Cuba	96.3	NA
Chile	92.3	NA
Nicaragua	92.2	NA
Argentina	91.2	NA
Canada	90.3	NA
Peru	89.8	NA
Costa Rica	89.6	NA
Brazil	88.1	NA
Uruguay	88.0	NA
Ecuador	85.2	NA
Data as of February 23, 2025
¹ Countries with population size less than or equal to 100,000 were excluded
² People vaccinated per 100 people represents total population (all ages)
³ Number of people out of 100 who received at least one vaccine dose; does not represent percent of population that completed primary vaccination series
⁴ Vaccine doses administered per day (7 day rolling average); does not represent number of people vaccinated

Country/ Area	People Vaccinated per 100 People³	Daily Vaccines Administered per 100 People⁴
10 (EMRO) Countries/ Areas with Highest Vaccination per 100 People^1,2
United Arab Emirates	105.8	NA
Qatar	105.8	NA
Bahrain	84.3	NA
Kuwait	81.0	NA
Iran	73.6	NA
Saudi Arabia	71.3	NA
Oman	71.1	NA
Pakistan	70.2	NA
Morocco	66.8	NA
Tunisia	58.4	NA
Data as of February 23, 2025
¹ Countries with population size less than or equal to 100,000 were excluded
² People vaccinated per 100 people represents total population (all ages)
³ Number of people out of 100 who received at least one vaccine dose; does not represent percent of population that completed primary vaccination series
⁴ Vaccine doses administered per day (7 day rolling average); does not represent number of people vaccinated

Country/ Area	People Vaccinated per 100 People³	Daily Vaccines Administered per 100 People⁴
10 (AFRO) Countries/ Areas with Highest Vaccination per 100 People^1,2
Mauritius	86.5	NA
Seychelles	82.6	NA
Rwanda	79.0	NA
Botswana	74.2	NA
Liberia	73.6	NA
Mozambique	69.4	NA
Sierra Leone	66.0	NA
Guinea	62.9	NA
Sao Tome and Principe	61.7	NA
Cabo Verde	60.1	NA
Data as of February 23, 2025
¹ Countries with population size less than or equal to 100,000 were excluded
² People vaccinated per 100 people represents total population (all ages)
³ Number of people out of 100 who received at least one vaccine dose; does not represent percent of population that completed primary vaccination series
⁴ Vaccine doses administered per day (7 day rolling average); does not represent number of people vaccinated

Country/ Area	People Vaccinated per 100 People³	Daily Vaccines Administered per 100 People⁴
10 (EURO) Countries/ Areas with Highest Vaccination per 100 People^1,2
Portugal	95.3	NA
Malta	89.8	NA
Spain	86.9	NA
Italy	86.3	NA
Iceland	83.0	NA
Ireland	81.9	NA
Finland	81.7	NA
Denmark	80.7	NA
France	80.6	NA
Norway	80.0	NA
Data as of February 23, 2025
¹ Countries with population size less than or equal to 100,000 were excluded
² People vaccinated per 100 people represents total population (all ages)
³ Number of people out of 100 who received at least one vaccine dose; does not represent percent of population that completed primary vaccination series
⁴ Vaccine doses administered per day (7 day rolling average); does not represent number of people vaccinated

Country/ Area	People Vaccinated per 100 People³	Daily Vaccines Administered per 100 People⁴
10 (WPRO) Countries/ Areas with Highest Vaccination per 100 People^1,2
Brunei Darussalam	100.5	NA
Viet Nam	91.9	NA
China	91.9	NA
Singapore	91.5	NA
Cambodia	91.3	NA
Korea (South)	86.4	NA
Samoa	86.1	NA
Australia	84.9	NA
Japan	84.5	NA
New Zealand	83.0	NA
Data as of February 23, 2025
¹ Countries with population size less than or equal to 100,000 were excluded
² People vaccinated per 100 people represents total population (all ages)
³ Number of people out of 100 who received at least one vaccine dose; does not represent percent of population that completed primary vaccination series
⁴ Vaccine doses administered per day (7 day rolling average); does not represent number of people vaccinated

Country/ Area	People Vaccinated per 100 People³	Daily Vaccines Administered per 100 People⁴
10 (SEARO) Countries/ Areas with Highest Vaccination per 100 People^1,2
Nepal	91.3	NA
Bhutan	89.3	NA
Bangladesh	88.5	NA
Thailand	79.5	NA
Sri Lanka	78.5	NA
Maldives	76.2	NA
Burma	74.5	NA
Indonesia	74.0	NA
India	72.5	NA
Timor-Leste	66.1	NA
Data as of February 23, 2025
¹ Countries with population size less than or equal to 100,000 were excluded
² People vaccinated per 100 people represents total population (all ages)
³ Number of people out of 100 who received at least one vaccine dose; does not represent percent of population that completed primary vaccination series
⁴ Vaccine doses administered per day (7 day rolling average); does not represent number of people vaccinated

table_10vaccinations(df, vac_type = c("Partial", "Fully"), type = "Global", run_date = "Enter a date")

df_who %>%
  filter(date <= sunday_date) %>%
  filter(population > 1000000) %>%
  group_by(country) %>%
  filter(!is.na(people_fully_vaccinated_per_hundred)) %>%
  filter(date == max(date)) %>%
  ungroup() %>%
  select(country = who_country, value1 = people_fully_vaccinated_per_hundred, value2 = daily_vaccinations_per_hundred) %>%
  arrange(desc(value1)) %>%
  head(10) %>%
  table_10vaccinations(., run_date = format(sunday_date, "%B %d, %Y"))

Country/ Area	People Vaccinated per 100 People³	Daily Vaccines Administered per 100 People⁴
Top 10 Countries/ Areas with Highest Vaccination per 100 People^1,2
Qatar	105.8	NA
United Arab Emirates	103.7	NA
Singapore	90.8	NA
Chile	90.3	NA
Cuba	89.7	NA
China	89.5	NA
Nicaragua	88.0	NA
Cambodia	87.6	NA
Viet Nam	87.5	NA
Portugal	86.8	NA
Data as of February 23, 2025
¹ Countries with population size less than or equal to 1 million were excluded
² People vaccinated per 100 people represents total population (all ages)
³ Number of people out of 100 who received at least one vaccine dose; does not represent percent of population that completed primary vaccination series
⁴ Vaccine doses administered per day (7 day rolling average); does not represent number of people vaccinated

table_countriesofconcern(df, df_vaccinations_manufacturers, country_list)

c_list <- c("United Kingdom","Denmark","Portugal","South Africa","Kenya","Zambia","United States of America")
c_list_iso <- parse_country(c_list, to = "iso3c")

df_who_latest <- df_who %>%
  group_by(id) %>%
  filter(date == max(date)) %>%
  ungroup()

vax_man <- get_vax_manufacturers()

table_countriesofconcern(df_who_latest, vax_man, c_list_iso)

Country	Denmark	United Kingdom	Kenya	Portugal	United States of America	South Africa	Zambia
Date	2025-02-23	2025-02-23	2025-02-23	2025-02-23	2025-02-23	2025-02-23	2025-02-23
New Cases 7 Day Average (7 Day Average Case Incidence per 100,000)	149.1 (2.54)	979.1 (1.45)	0.9 (0)	72.7 (0.71)	0 (0)	7.4 (0.01)	0 (0)
7 Day Cases	1,044	6,854	6	509	0	52	0
Previous 7 Day Cases	1,827	9,427	13	808	0	42	0
% Change in Cases from Previous 7 Days	-42.9%	-27.3%	-53.8%	-37%		23.8%
New Deaths 7 Day Average (7 Day Average Death Incidence per 100,000)	0 (0)	0 (0)	0 (0)	5.1 (0.05)	800.3 (0.24)	0 (0)	0 (0)
7 Day Deaths	0	0	0	36	5,602	0	0
Previous 7 Day Deaths	2	0	0	21	4,164	0	0
% Change in Deaths from Previous 7 Days	-100%			71%	35%
Most Recent Date for Vaccinations	2023-09-29	2022-09-11	2023-04-02	2023-09-29	2023-05-09	2023-09-24	2023-06-25
People Vaccinated Per 100 People	80.69	79.71	26.83	95.34	81.39	40.42	58.51
People who completed primary vaccination series per 100 People	80.11	75.19	20.53	86.75	69.47	35.13	46.03
Total Vaccinations Per 100 People	254.24	224.04	43.96	275.03	203.81	69.79	67.21
Vaccines in Use	Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech	Moderna, Oxford/AstraZeneca, Pfizer/BioNTech	Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing	Covaxin, Johnson&Johnson, Moderna, Novavax, Oxford/AstraZeneca, Pfizer/BioNTech, Sanofi/GSK, Sinopharm/Beijing, Sinovac	Johnson&Johnson, Moderna, Novavax, Pfizer/BioNTech	Johnson&Johnson, Pfizer/BioNTech	Johnson&Johnson, Oxford/AstraZeneca, Sinopharm/Beijing
% Delta since January 1, 2022	Fill manually	Fill manually	Fill manually	Fill manually	Fill manually	Fill manually	Fill manually
% Omicron since January 1, 2022	Fill manually	Fill manually	Fill manually	Fill manually	Fill manually	Fill manually	Fill manually