Research question: Has the COVID-19 pandemic and associated vaccination campaigns cannibalized resources and progress on previous set targets for Vaccine preventable disease?
Hypothesis: Advent of COVID-19 pandemic cases led to increased surveillance of COVID-19 and eventually increased immunization while surveilled cases and immunizations for VPDs decreased.
Research question: Has the economic shock of COVID-19 and R&D focus on the COVID-19 vaccine distorted prices to VPD vaccines
Hypothesis: While GDP growth and GNI of countries slowed or shrank in the Pandemic, prices to VPD vaccines increased and became relatively more expensive.
Research question: Is the benefit of added investment into VPD vaccines and immunization coverage greater than/needed against the cost of VPDs
Hypothesis: The cost of VPDs far exceeds the cost and benefit of investing in VPD vaccines and coverage programs
Load libraries for R. Note: additional libraries may be added later for specific visualization techniques, data exploration, and summary statistics.
#| echo: false
#| warning: false
#load libraries for use
library(tidyverse)
library(lubridate)
library(purrr)
library(janitor)
library(here)
library(readxl)
library(ggridges)
library(stringr)
library(naniar)
library(rnaturalearth)
library(countrycode)
library(wbstats)
library("data.table")
setwd("C:/Users/joeau/OneDrive - Johns Hopkins/SAIS/Year 2/Sustainable Finance/final_project/")First need function from earlier in class to help locate countries and use ISO3 codes for WB data and table joins. Created below:
#| echo: false
#| warning: false
#searching gdp data
wb_search("gdp") |>
view()
wb_search("gni") |>
view()
#build functions to help generate locations
country_name_regex_to_iso3c <- function(country_name) {
country_name |>
countrycode(origin = "country.name",
destination = "iso3c",
origin_regex = TRUE)
}
iso3c_to_country_name <- function(country_string) {
country_string %>%
countrycode::countrycode(origin = "iso3c", destination = "country.name")
}
character_num_to_numeric <- function(character_num) {
character_num %>%
# take out the commas
stringr::str_remove_all(pattern = ",") %>%
# take out any blank spaces before or after the number
stringr::str_trim() %>%
# coerce to numeric
as.numeric()
}
iso3c_to_x <- purrr::partial(countrycode, origin = "iso3c")Next will be loading in data needed from API (GDP, GNI, Population, per capita calcs?). Getting World Bank API data for country-level analysis below. (Data Source: WB Stats API)
#| echo: false
#| warning: false
##Get our WB data via the API
wb_population <- wb_data("SP.POP.TOTL") # our population data
wb_gdp_ppp <- wb_data("NY.GDP.MKTP.PP.CD") # ppp real GDP data
wb_gdp_nominal <- wb_data("NY.GDP.MKTP.CD") # real GDP data
wb_gni_ppp <- wb_data("NY.GNP.MKTP.PP.CD") # ppp real GNI data
wb_gni_nominal <- wb_data ("NY.GNP.MKTP.CD")# nominal real GNI data
wb_regions <- wb_countries() #regions data
wb_growth <- wb_data("NY.GDP.MKTP.KD.ZG") #GDP growth rates
wb_health_exp <- wb_data("SH.XPD.CHEX.GD.ZS") #Health expenditure as % GDPCleaning up names of columns for later table joins:
#| echo: false
#| warning: false
##Clean up WB data
wb_gni_nominal <- wb_gni_nominal |>
rename(year = date, country_name = country, gni = NY.GNP.MKTP.CD) |>
select(-iso2c,-unit,-obs_status,-footnote,-last_updated)
wb_gni_ppp <- wb_gni_ppp |>
rename(year = date, country_name = country, gni_ppp = NY.GNP.MKTP.PP.CD) |>
select(-iso2c,-unit,-obs_status,-footnote,-last_updated)
wb_gdp_nominal <- wb_gdp_nominal |>
rename(year = date, country_name = country, gdp = NY.GDP.MKTP.CD) |>
select(-iso2c,-unit,-obs_status,-footnote,-last_updated)
wb_gdp_ppp <- wb_gdp_ppp |>
rename(year = date, country_name = country, gdp_ppp = NY.GDP.MKTP.PP.CD) |>
select(-iso2c,-unit,-obs_status,-footnote,-last_updated)
wb_population <- wb_population |>
rename(year = date, country_name = country, pop_total = SP.POP.TOTL) |>
select(-iso2c,-unit,-obs_status,-footnote,-last_updated)
wb_regions <- wb_regions |>
select(iso3c, country, region, income_level) |>
filter(region != "Aggregates")
wb_growth <- wb_growth |>
rename(gdp_growth = NY.GDP.MKTP.KD.ZG, year = date) |>
select(iso3c, country, year, gdp_growth)
wb_health_exp <- wb_health_exp |>
mutate(gdp_health = SH.XPD.CHEX.GD.ZS, year = date) |>
select(iso3c, country, year, gdp_health)Now let’s join all the WB data together into one table using a series of Left Joins and filters to condense our time frame to >= 1990 :
#Left joins beginning with nominal GDP as reference sheet
#Join 1: GDP
wb_gdp <- wb_gdp_nominal |>
left_join(wb_gdp_ppp, by = c("iso3c", "country_name", "year"))
wb_gdp## # A tibble: 13,454 × 5
## iso3c country_name year gdp gdp_ppp
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 ABW Aruba 1960 NA NA
## 2 ABW Aruba 1961 NA NA
## 3 ABW Aruba 1962 NA NA
## 4 ABW Aruba 1963 NA NA
## 5 ABW Aruba 1964 NA NA
## 6 ABW Aruba 1965 NA NA
## 7 ABW Aruba 1966 NA NA
## 8 ABW Aruba 1967 NA NA
## 9 ABW Aruba 1968 NA NA
## 10 ABW Aruba 1969 NA NA
## # ℹ 13,444 more rows#Join 2: GNI
wb_gni <- wb_gni_nominal |>
left_join(wb_gni_ppp, by = c("iso3c", "country_name", "year"))
wb_gni## # A tibble: 13,454 × 5
## iso3c country_name year gni gni_ppp
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 ABW Aruba 1960 NA NA
## 2 ABW Aruba 1961 NA NA
## 3 ABW Aruba 1962 NA NA
## 4 ABW Aruba 1963 NA NA
## 5 ABW Aruba 1964 NA NA
## 6 ABW Aruba 1965 NA NA
## 7 ABW Aruba 1966 NA NA
## 8 ABW Aruba 1967 NA NA
## 9 ABW Aruba 1968 NA NA
## 10 ABW Aruba 1969 NA NA
## # ℹ 13,444 more rows#Join 3: Combined GDP GNI
wb_econ_data <- wb_gdp |>
left_join(wb_gni, by = c("iso3c", "country_name", "year"))
wb_econ_data## # A tibble: 13,454 × 7
## iso3c country_name year gdp gdp_ppp gni gni_ppp
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ABW Aruba 1960 NA NA NA NA
## 2 ABW Aruba 1961 NA NA NA NA
## 3 ABW Aruba 1962 NA NA NA NA
## 4 ABW Aruba 1963 NA NA NA NA
## 5 ABW Aruba 1964 NA NA NA NA
## 6 ABW Aruba 1965 NA NA NA NA
## 7 ABW Aruba 1966 NA NA NA NA
## 8 ABW Aruba 1967 NA NA NA NA
## 9 ABW Aruba 1968 NA NA NA NA
## 10 ABW Aruba 1969 NA NA NA NA
## # ℹ 13,444 more rows#Filter data set to same time range before join (>= 1990)
wb_econ_data <- wb_econ_data |>
rename(country = country_name) |>
filter(year >= "1990")
wb_econ_data## # A tibble: 6,944 × 7
## iso3c country year gdp gdp_ppp gni gni_ppp
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ABW Aruba 1990 764804469. 1363755809. 757039106. 1348704439.
## 2 ABW Aruba 1991 872067039. 1522141299. 864301676. 1507408172.
## 3 ABW Aruba 1992 958659218. 1648407758. 948994413. 1630343441.
## 4 ABW Aruba 1993 1083240223. 1810796527. 1065363128. 1778312497.
## 5 ABW Aruba 1994 1245810056. 2001192964. 1225810056. 1966271278.
## 6 ABW Aruba 1995 1320670391. 2095198113. 1305474860. 2068993690.
## 7 ABW Aruba 1996 1379888268. 2158860032. 1362067039. 2128552799.
## 8 ABW Aruba 1997 1531843575. 2350843673. 1508659218. 2312168482.
## 9 ABW Aruba 1998 1665363128. 2424658707. 1659776536. 2415817395.
## 10 ABW Aruba 1999 1722905028. 2489269109. 1680223464. 2422237613.
## # ℹ 6,934 more rows#Join 4: Add Regions
wb_reg <- wb_econ_data |>
left_join(wb_regions, by = c("iso3c", "country"))
#Join 5: Add Growth Rates (do later once API catches up to IMF)
#Join 6: Add Health Expenditure
wb_data <- wb_reg |>
left_join(wb_health_exp, by = c("iso3c", "country", "year"))
wb_data## # A tibble: 6,944 × 10
## iso3c country year gdp gdp_ppp gni gni_ppp region income_level
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 ABW Aruba 1990 764804469. 1.36e9 7.57e8 1.35e9 Latin… High income
## 2 ABW Aruba 1991 872067039. 1.52e9 8.64e8 1.51e9 Latin… High income
## 3 ABW Aruba 1992 958659218. 1.65e9 9.49e8 1.63e9 Latin… High income
## 4 ABW Aruba 1993 1083240223. 1.81e9 1.07e9 1.78e9 Latin… High income
## 5 ABW Aruba 1994 1245810056. 2.00e9 1.23e9 1.97e9 Latin… High income
## 6 ABW Aruba 1995 1320670391. 2.10e9 1.31e9 2.07e9 Latin… High income
## 7 ABW Aruba 1996 1379888268. 2.16e9 1.36e9 2.13e9 Latin… High income
## 8 ABW Aruba 1997 1531843575. 2.35e9 1.51e9 2.31e9 Latin… High income
## 9 ABW Aruba 1998 1665363128. 2.42e9 1.66e9 2.42e9 Latin… High income
## 10 ABW Aruba 1999 1722905028. 2.49e9 1.68e9 2.42e9 Latin… High income
## # ℹ 6,934 more rows
## # ℹ 1 more variable: gdp_health <dbl>Look for any unique or missing values, it appears there are 712 missing observations for GDP/GNI measurements for countries. These can be tolerated for analysis as we are looking at global trends and more interested in disease trends.
#Take a look at data so far using glimpse() command
glimpse(wb_data)## Rows: 6,944
## Columns: 10
## $ iso3c <chr> "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", "…
## $ country <chr> "Aruba", "Aruba", "Aruba", "Aruba", "Aruba", "Aruba", "Ar…
## $ year <dbl> 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 199…
## $ gdp <dbl> 764804469, 872067039, 958659218, 1083240223, 1245810056, …
## $ gdp_ppp <dbl> 1363755809, 1522141299, 1648407758, 1810796527, 200119296…
## $ gni <dbl> 757039106, 864301676, 948994413, 1065363128, 1225810056, …
## $ gni_ppp <dbl> 1348704439, 1507408172, 1630343441, 1778312497, 196627127…
## $ region <chr> "Latin America & Caribbean", "Latin America & Caribbean",…
## $ income_level <chr> "High income", "High income", "High income", "High income…
## $ gdp_health <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…#Show unique countries & values
#Show unique missing values
unique_missing_obs <- wb_data %>%
filter(!complete.cases(.)) %>%
distinct()
#What's missing from keys
wb_data |>
filter(is.na(iso3c) | is.na(country) | is.na(year) | is.na(region)) |>
select(iso3c, country, year, region) |>
unique()## # A tibble: 0 × 4
## # ℹ 4 variables: iso3c <chr>, country <chr>, year <dbl>, region <chr>#What's missing from indicators
wb_data |>
filter(is.na(gdp) | is.na(gdp_ppp) | is.na(gni) | is.na(gni_ppp)) |>
select(country, gdp, gdp_ppp, gni, gni_ppp) |>
unique()## # A tibble: 712 × 5
## country gdp gdp_ppp gni gni_ppp
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan NA NA NA NA
## 2 Afghanistan 3854235264. 19805683002. NA NA
## 3 Afghanistan 4539500884. 21980384806. NA NA
## 4 Afghanistan 5220823685. 22889600504. NA NA
## 5 Afghanistan 6226199249. 26258419344. NA NA
## 6 Afghanistan 6971379033. 28518821098. NA NA
## 7 Afghanistan 9715761650. 33339216567. NA NA
## 8 Afghanistan 10249767311. 35312296534. NA NA
## 9 Andorra 1029048482. NA NA NA
## 10 Andorra 1106928583. NA NA NA
## # ℹ 702 more rowsLoad in VPD case data (Source: WHO VPD Immunization Data). We will need to clean up the Tetanus observations as there are some for both total tetanus and neonatal tetanus. While this stratification is important it is not relevant to this particular analysis. We can drop the neonatal tetanus observations since they are assumed to be included in the “total tetanus” observations. Then using “mutate” we can rename “Total tetatnus” descriptions to simply “Tetanus”. Finally using the naniar package in R we can summarize our missingness to see it is limited solely to case data for a number of countries. Given the time range and purpose of this assignment we can tolerate these missing values for now, although the fact that they include the entire date range and all countries in the data set some more specific questions and filtering are needed for in-depth analysis and modelling.
#| warning: false
#Load in Incidence Rate
vpd_ir <- read_excel("C:/Users/joeau/OneDrive - Johns Hopkins/SAIS/Year 2/Sustainable Finance/final_project/00_data_raw/VPDs/vpd_incidence_rate_2021.xlsx") |>
rename(group = GROUP, iso3c =CODE, country = NAME, year = YEAR, disease = DISEASE, disease_desc = DISEASE_DESCRIPTION, units= DENOMINATOR, ir = INCIDENCE_RATE) |>
filter(group == "COUNTRIES")
vpd_ir## # A tibble: 76,842 × 8
## group iso3c country year disease disease_desc units ir
## <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl>
## 1 COUNTRIES ABW Aruba 2021 CRS Congenital rubella synd… per … 0
## 2 COUNTRIES ABW Aruba 2021 DIPHTHERIA Diphtheria per … 0
## 3 COUNTRIES ABW Aruba 2021 MEASLES Measles per … 0
## 4 COUNTRIES ABW Aruba 2021 MUMPS Mumps per … 0
## 5 COUNTRIES ABW Aruba 2021 NTETANUS Neonatal tetanus per … 0
## 6 COUNTRIES ABW Aruba 2021 PERTUSSIS Pertussis per … 0
## 7 COUNTRIES ABW Aruba 2021 POLIO Poliomyelitis per … 0
## 8 COUNTRIES ABW Aruba 2021 RUBELLA Rubella per … 0
## 9 COUNTRIES ABW Aruba 2021 TTETANUS Total tetanus per … 0
## 10 COUNTRIES ABW Aruba 2021 YFEVER Yellow fever per … 0
## # ℹ 76,832 more rows#Load in Case Data
vpd_cases <- read_excel("C:/Users/joeau/OneDrive - Johns Hopkins/SAIS/Year 2/Sustainable Finance/final_project/00_data_raw/VPDs/vpd_reported_cases_2021.xlsx") |>
rename(group = GROUP, iso3c =CODE, country = NAME, year = YEAR, disease = DISEASE, disease_desc = DISEASE_DESCRIPTION, cases = CASES) |>
filter(group == "COUNTRIES")
#View unique diseases
vpd_cases |>
select(disease, disease_desc) |>
unique() |>
view()
#View unique diseases
vpd_ir |>
select(disease, disease_desc) |>
unique() |>
view()
#No need to keep neonatal tetanus for this analysis, drop and keep total tetanus
vpd_cases <- vpd_cases |>
filter(!(disease_desc=="Neonatal tetanus"))
vpd_ir <- vpd_ir |>
filter(!(disease_desc=="Neonatal tetanus"))
#Rename "Total tetanus" to just tetanus
vpd_cases <- vpd_cases |>
mutate(disease_desc = ifelse(disease_desc == "Total tetanus", "Tetanus", disease_desc))
vpd_cases |>
select(disease, disease_desc) |>
unique() |>
view()
vpd_ir <- vpd_ir |>
mutate(disease_desc = ifelse(disease_desc == "Total tetanus", "Tetanus", disease_desc))
vpd_ir |>
select(disease, disease_desc) |>
unique() |>
view()
#Check missingness
miss_var_summary(vpd_cases)## # A tibble: 7 × 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 cases 16907 24.8
## 2 group 0 0
## 3 iso3c 0 0
## 4 country 0 0
## 5 year 0 0
## 6 disease 0 0
## 7 disease_desc 0 0vpd_cases |>
filter(is.na(cases)) |>
select(year) |>
unique() |>
view()
vpd_cases |>
filter(is.na(cases)) |>
select(country) |>
unique() |>
view()
#VPD cases data
vpd_cases## # A tibble: 68,221 × 7
## group iso3c country year disease disease_desc cases
## <chr> <chr> <chr> <dbl> <chr> <chr> <dbl>
## 1 COUNTRIES ABW Aruba 2021 RUBELLA Rubella 0
## 2 COUNTRIES ABW Aruba 2021 POLIO Poliomyelitis 0
## 3 COUNTRIES ABW Aruba 2021 TTETANUS Tetanus 0
## 4 COUNTRIES ABW Aruba 2021 CRS Congenital rubella syndrome 0
## 5 COUNTRIES ABW Aruba 2021 MEASLES Measles 0
## 6 COUNTRIES ABW Aruba 2021 PERTUSSIS Pertussis 0
## 7 COUNTRIES ABW Aruba 2021 YFEVER Yellow fever 0
## 8 COUNTRIES ABW Aruba 2021 DIPHTHERIA Diphtheria 0
## 9 COUNTRIES ABW Aruba 2021 MUMPS Mumps 0
## 10 COUNTRIES ABW Aruba 2019 CRS Congenital rubella syndrome 0
## # ℹ 68,211 more rowsJoin country cases and incidence data (Source: WHO VPD Immunization Data). Missingness after the join is relatively the same between cases and ir.
#| echo: false
#| warning: false
#Mutate VPD tables with new unique identifier for joins
vpd_case_key = mutate(vpd_cases,
id = paste(iso3c, year, disease, sep = '_'))
vpd_case_key## # A tibble: 68,221 × 8
## group iso3c country year disease disease_desc cases id
## <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr>
## 1 COUNTRIES ABW Aruba 2021 RUBELLA Rubella 0 ABW_…
## 2 COUNTRIES ABW Aruba 2021 POLIO Poliomyelitis 0 ABW_…
## 3 COUNTRIES ABW Aruba 2021 TTETANUS Tetanus 0 ABW_…
## 4 COUNTRIES ABW Aruba 2021 CRS Congenital rubella synd… 0 ABW_…
## 5 COUNTRIES ABW Aruba 2021 MEASLES Measles 0 ABW_…
## 6 COUNTRIES ABW Aruba 2021 PERTUSSIS Pertussis 0 ABW_…
## 7 COUNTRIES ABW Aruba 2021 YFEVER Yellow fever 0 ABW_…
## 8 COUNTRIES ABW Aruba 2021 DIPHTHERIA Diphtheria 0 ABW_…
## 9 COUNTRIES ABW Aruba 2021 MUMPS Mumps 0 ABW_…
## 10 COUNTRIES ABW Aruba 2019 CRS Congenital rubella synd… 0 ABW_…
## # ℹ 68,211 more rowsvpd_ir_key = mutate(vpd_ir,
id = paste(iso3c, year, disease, sep = '_'))
vpd_ir_key## # A tibble: 68,221 × 9
## group iso3c country year disease disease_desc units ir id
## <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 COUNTRIES ABW Aruba 2021 CRS Congenital rubell… per … 0 ABW_…
## 2 COUNTRIES ABW Aruba 2021 DIPHTHERIA Diphtheria per … 0 ABW_…
## 3 COUNTRIES ABW Aruba 2021 MEASLES Measles per … 0 ABW_…
## 4 COUNTRIES ABW Aruba 2021 MUMPS Mumps per … 0 ABW_…
## 5 COUNTRIES ABW Aruba 2021 PERTUSSIS Pertussis per … 0 ABW_…
## 6 COUNTRIES ABW Aruba 2021 POLIO Poliomyelitis per … 0 ABW_…
## 7 COUNTRIES ABW Aruba 2021 RUBELLA Rubella per … 0 ABW_…
## 8 COUNTRIES ABW Aruba 2021 TTETANUS Tetanus per … 0 ABW_…
## 9 COUNTRIES ABW Aruba 2021 YFEVER Yellow fever per … 0 ABW_…
## 10 COUNTRIES ABW Aruba 2019 CRS Congenital rubell… per … 0 ABW_…
## # ℹ 68,211 more rows#Change order of columns to put unique id in front for left join
vpd_case_key <- vpd_case_key[,c(8,1,2,3,4,5,6,7)]
vpd_ir_key <- vpd_ir_key[,c(9,1,2,3,4,5,6,7,8)]
#Left join ir to cases
vpd <- vpd_case_key |>
left_join(vpd_ir_key, by = c("id", "group", "iso3c", "country", "year", "disease", "disease_desc"))
#Check missingness
miss_var_summary(vpd)## # A tibble: 10 × 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 ir 16923 24.8
## 2 cases 16907 24.8
## 3 id 0 0
## 4 group 0 0
## 5 iso3c 0 0
## 6 country 0 0
## 7 year 0 0
## 8 disease 0 0
## 9 disease_desc 0 0
## 10 units 0 0vpd## # A tibble: 68,221 × 10
## id group iso3c country year disease disease_desc cases units ir
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> <dbl>
## 1 ABW_2021_RU… COUN… ABW Aruba 2021 RUBELLA Rubella 0 per … 0
## 2 ABW_2021_PO… COUN… ABW Aruba 2021 POLIO Poliomyelit… 0 per … 0
## 3 ABW_2021_TT… COUN… ABW Aruba 2021 TTETAN… Tetanus 0 per … 0
## 4 ABW_2021_CRS COUN… ABW Aruba 2021 CRS Congenital … 0 per … 0
## 5 ABW_2021_ME… COUN… ABW Aruba 2021 MEASLES Measles 0 per … 0
## 6 ABW_2021_PE… COUN… ABW Aruba 2021 PERTUS… Pertussis 0 per … 0
## 7 ABW_2021_YF… COUN… ABW Aruba 2021 YFEVER Yellow fever 0 per … 0
## 8 ABW_2021_DI… COUN… ABW Aruba 2021 DIPHTH… Diphtheria 0 per … 0
## 9 ABW_2021_MU… COUN… ABW Aruba 2021 MUMPS Mumps 0 per … 0
## 10 ABW_2019_CRS COUN… ABW Aruba 2019 CRS Congenital … 0 per … 0
## # ℹ 68,211 more rowsNow to add VPD immunization coverage (data source: WHO VPD Vaccination Coverage). There is a high rate of missing data in source data, let’s check where it is. It again spans all countries, years, and diseases so we will have to tailor findings and explanations accordingly. We need to take out target coverage and doses and filter on the official collection method to simplify out vaccination coverage data for the least amount of missingness and higher accuracy.
#| echo: false
#| warning: false
#Load in VPD Immunization Coverage
vpd_vax <- read_excel("C:/Users/joeau/OneDrive - Johns Hopkins/SAIS/Year 2/Sustainable Finance/final_project/00_data_raw/VPDs/vpd_vax_coverage_2021.xlsx") |>
rename(group = GROUP, iso3c =CODE, country = NAME, year = YEAR, antig = ANTIGEN, antig_desc = ANTIGEN_DESCRIPTION, disease = DISEASE, coverage_cat = COVERAGE_CATEGORY, cov_cat_desc = COVERAGE_CATEGORY_DESCRIPTION, targ_number = TARGET_NUMBER, doses = DOSES, coverage = COVERAGE) |>
filter(group == "COUNTRIES")
vpd_vax## # A tibble: 328,351 × 12
## group iso3c country year antig antig_desc disease coverage_cat cov_cat_desc
## <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 COUNT… ABW Aruba 2021 BCG BCG TB ADMIN Administrat…
## 2 COUNT… ABW Aruba 2021 BCG BCG TB OFFICIAL Official co…
## 3 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… ADMIN Administrat…
## 4 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… OFFICIAL Official co…
## 5 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… ADMIN Administrat…
## 6 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… OFFICIAL Official co…
## 7 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… ADMIN Administrat…
## 8 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… OFFICIAL Official co…
## 9 COUNT… ABW Aruba 2021 DTPC… DTP-conta… DIPTHE… ADMIN Administrat…
## 10 COUNT… ABW Aruba 2021 DTPC… DTP-conta… DIPTHE… OFFICIAL Official co…
## # ℹ 328,341 more rows
## # ℹ 3 more variables: targ_number <dbl>, doses <dbl>, coverage <dbl>#Check missingness of original data
miss_var_summary(vpd_vax)## # A tibble: 12 × 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 targ_number 277849 84.6
## 2 doses 277721 84.6
## 3 coverage 140039 42.6
## 4 group 0 0
## 5 iso3c 0 0
## 6 country 0 0
## 7 year 0 0
## 8 antig 0 0
## 9 antig_desc 0 0
## 10 disease 0 0
## 11 coverage_cat 0 0
## 12 cov_cat_desc 0 0vpd_vax |>
filter(is.na(coverage) | is.na(doses) | is.na(targ_number)) |>
select(year) |>
unique() |>
view()
vpd_vax |>
filter(is.na(coverage) | is.na(doses) | is.na(targ_number)) |>
select(country) |>
unique() |>
view()
vpd_vax |>
filter(is.na(coverage) | is.na(doses) | is.na(targ_number)) |>
select(disease) |>
unique() |>
view()
#Mutate VPD vax coverage with new unique identifier for joins
vpd_vax_key = mutate(vpd_vax,
id = paste(iso3c, year, disease, sep = '_'))
vpd_vax_key## # A tibble: 328,351 × 13
## group iso3c country year antig antig_desc disease coverage_cat cov_cat_desc
## <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 COUNT… ABW Aruba 2021 BCG BCG TB ADMIN Administrat…
## 2 COUNT… ABW Aruba 2021 BCG BCG TB OFFICIAL Official co…
## 3 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… ADMIN Administrat…
## 4 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… OFFICIAL Official co…
## 5 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… ADMIN Administrat…
## 6 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… OFFICIAL Official co…
## 7 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… ADMIN Administrat…
## 8 COUNT… ABW Aruba 2021 DIPH… Diphtheri… DIPTHE… OFFICIAL Official co…
## 9 COUNT… ABW Aruba 2021 DTPC… DTP-conta… DIPTHE… ADMIN Administrat…
## 10 COUNT… ABW Aruba 2021 DTPC… DTP-conta… DIPTHE… OFFICIAL Official co…
## # ℹ 328,341 more rows
## # ℹ 4 more variables: targ_number <dbl>, doses <dbl>, coverage <dbl>, id <chr>vpd_vax_key <- vpd_vax_key[,c(13,1,2,3,4,5,6,7,8,9,10,11,12)]
#Look at unique vax & vax/disease match up
vpd_vax_key |>
select(antig_desc) |>
unique() |>
view()
vpd_vax_key |>
select(disease, antig_desc) |>
unique() |>
view()
#Filter down to official coverage, don't need other estimate methods
vpd_vax_key <- vpd_vax_key |>
filter((coverage_cat=="OFFICIAL")) |>
select(-c(targ_number,doses))
#Join Vax coverage with
vpd_combo <- vpd |>
left_join(vpd_vax_key, by = c("id", "group", "iso3c", "country", "year", "disease"))
vpd_combo## # A tibble: 78,597 × 15
## id group iso3c country year disease disease_desc cases units ir antig
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 ABW_2… COUN… ABW Aruba 2021 RUBELLA Rubella 0 per … 0 RCV1
## 2 ABW_2… COUN… ABW Aruba 2021 POLIO Poliomyelit… 0 per … 0 IPV1
## 3 ABW_2… COUN… ABW Aruba 2021 POLIO Poliomyelit… 0 per … 0 IPV1…
## 4 ABW_2… COUN… ABW Aruba 2021 POLIO Poliomyelit… 0 per … 0 IPV2
## 5 ABW_2… COUN… ABW Aruba 2021 POLIO Poliomyelit… 0 per … 0 IPV2…
## 6 ABW_2… COUN… ABW Aruba 2021 POLIO Poliomyelit… 0 per … 0 POL3
## 7 ABW_2… COUN… ABW Aruba 2021 TTETAN… Tetanus 0 per … 0 TT2P…
## 8 ABW_2… COUN… ABW Aruba 2021 TTETAN… Tetanus 0 per … 0 TTCV4
## 9 ABW_2… COUN… ABW Aruba 2021 TTETAN… Tetanus 0 per … 0 TTCV5
## 10 ABW_2… COUN… ABW Aruba 2021 TTETAN… Tetanus 0 per … 0 TTCV6
## # ℹ 78,587 more rows
## # ℹ 4 more variables: antig_desc <chr>, coverage_cat <chr>, cov_cat_desc <chr>,
## # coverage <dbl>#check missingness
miss_var_summary(vpd_combo)## # A tibble: 15 × 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 coverage 51979 66.1
## 2 antig 35599 45.3
## 3 antig_desc 35599 45.3
## 4 coverage_cat 35599 45.3
## 5 cov_cat_desc 35599 45.3
## 6 ir 17840 22.7
## 7 cases 17823 22.7
## 8 id 0 0
## 9 group 0 0
## 10 iso3c 0 0
## 11 country 0 0
## 12 year 0 0
## 13 disease 0 0
## 14 disease_desc 0 0
## 15 units 0 0Load in VPD immunization coverage. (Source: Our World in Data COVID-19). Cases and vaccinations are again missing a significant amount of values in the source data. To join this data to our VPD data we need to convert doses of vaccines given to coverage, this is a rough estimate for two reasons:
COVID-19 doses are not always = to vaccination (depending on person, vaccine type, and timing of pandemic regulations)
This is a rough estimate (similar to administrative method) unlike the official method used for VPDs
#| echo: false
#| warning: false
#Load in Case & vax Coverage Data
covid <- fread("C:/Users/joeau/OneDrive - Johns Hopkins/SAIS/Year 2/Sustainable Finance/final_project/00_data_raw/COVID/owid-covid-data.csv",
select = c("iso_code","continent","location","date","new_cases","new_deaths","people_fully_vaccinated")) |>
filter(!grepl("OWID", iso_code))
covid## iso_code continent location date new_cases new_deaths
## 1: AFG Asia Afghanistan 1/3/2020 0 0
## 2: AFG Asia Afghanistan 1/4/2020 0 0
## 3: AFG Asia Afghanistan 1/5/2020 0 0
## 4: AFG Asia Afghanistan 1/6/2020 0 0
## 5: AFG Asia Afghanistan 1/7/2020 0 0
## ---
## 280146: ZWE Africa Zimbabwe 4/1/2023 7 0
## 280147: ZWE Africa Zimbabwe 4/2/2023 0 0
## 280148: ZWE Africa Zimbabwe 4/3/2023 6 0
## 280149: ZWE Africa Zimbabwe 4/4/2023 0 0
## 280150: ZWE Africa Zimbabwe 4/5/2023 0 0
## people_fully_vaccinated
## 1: <NA>
## 2: <NA>
## 3: <NA>
## 4: <NA>
## 5: <NA>
## ---
## 280146: <NA>
## 280147: <NA>
## 280148: <NA>
## 280149: <NA>
## 280150: <NA>#missingness
miss_var_summary(covid)## # A tibble: 7 × 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 people_fully_vaccinated 225509 80.5
## 2 new_cases 3311 1.18
## 3 new_deaths 3207 1.14
## 4 iso_code 0 0
## 5 continent 0 0
## 6 location 0 0
## 7 date 0 0#Need to aggregate cases and vax by Country by Month, Year
covid_date <- covid |>
rename(iso3c = iso_code, country = location) |>
separate_wider_delim(date, "/", names = c("month","day","year")) |>
transform(year = as.numeric(year)) |>
filter(month == 12,
day == 31)
covid_date## iso3c continent country month day year
## 1 AFG Asia Afghanistan 12 31 2020
## 2 AFG Asia Afghanistan 12 31 2021
## 3 AFG Asia Afghanistan 12 31 2022
## 4 ALB Europe Albania 12 31 2020
## 5 ALB Europe Albania 12 31 2021
## 6 ALB Europe Albania 12 31 2022
## 7 DZA Africa Algeria 12 31 2020
## 8 DZA Africa Algeria 12 31 2021
## 9 DZA Africa Algeria 12 31 2022
## 10 ASM Oceania American Samoa 12 31 2020
## 11 ASM Oceania American Samoa 12 31 2021
## 12 ASM Oceania American Samoa 12 31 2022
## 13 AND Europe Andorra 12 31 2020
## 14 AND Europe Andorra 12 31 2021
## 15 AND Europe Andorra 12 31 2022
## 16 AGO Africa Angola 12 31 2020
## 17 AGO Africa Angola 12 31 2021
## 18 AGO Africa Angola 12 31 2022
## 19 AIA North America Anguilla 12 31 2020
## 20 AIA North America Anguilla 12 31 2021
## 21 AIA North America Anguilla 12 31 2022
## 22 ATG North America Antigua and Barbuda 12 31 2020
## 23 ATG North America Antigua and Barbuda 12 31 2021
## 24 ATG North America Antigua and Barbuda 12 31 2022
## 25 ARG South America Argentina 12 31 2020
## 26 ARG South America Argentina 12 31 2021
## 27 ARG South America Argentina 12 31 2022
## 28 ARM Asia Armenia 12 31 2020
## 29 ARM Asia Armenia 12 31 2021
## 30 ARM Asia Armenia 12 31 2022
## 31 ABW North America Aruba 12 31 2020
## 32 ABW North America Aruba 12 31 2021
## 33 ABW North America Aruba 12 31 2022
## 34 AUS Oceania Australia 12 31 2020
## 35 AUS Oceania Australia 12 31 2021
## 36 AUS Oceania Australia 12 31 2022
## 37 AUT Europe Austria 12 31 2020
## 38 AUT Europe Austria 12 31 2021
## 39 AUT Europe Austria 12 31 2022
## 40 AZE Asia Azerbaijan 12 31 2020
## 41 AZE Asia Azerbaijan 12 31 2021
## 42 AZE Asia Azerbaijan 12 31 2022
## 43 BHS North America Bahamas 12 31 2020
## 44 BHS North America Bahamas 12 31 2021
## 45 BHS North America Bahamas 12 31 2022
## 46 BHR Asia Bahrain 12 31 2020
## 47 BHR Asia Bahrain 12 31 2021
## 48 BHR Asia Bahrain 12 31 2022
## 49 BGD Asia Bangladesh 12 31 2020
## 50 BGD Asia Bangladesh 12 31 2021
## 51 BGD Asia Bangladesh 12 31 2022
## 52 BRB North America Barbados 12 31 2020
## 53 BRB North America Barbados 12 31 2021
## 54 BRB North America Barbados 12 31 2022
## 55 BLR Europe Belarus 12 31 2020
## 56 BLR Europe Belarus 12 31 2021
## 57 BLR Europe Belarus 12 31 2022
## 58 BEL Europe Belgium 12 31 2020
## 59 BEL Europe Belgium 12 31 2021
## 60 BEL Europe Belgium 12 31 2022
## 61 BLZ North America Belize 12 31 2020
## 62 BLZ North America Belize 12 31 2021
## 63 BLZ North America Belize 12 31 2022
## 64 BEN Africa Benin 12 31 2020
## 65 BEN Africa Benin 12 31 2021
## 66 BEN Africa Benin 12 31 2022
## 67 BMU North America Bermuda 12 31 2020
## 68 BMU North America Bermuda 12 31 2021
## 69 BMU North America Bermuda 12 31 2022
## 70 BTN Asia Bhutan 12 31 2020
## 71 BTN Asia Bhutan 12 31 2021
## 72 BTN Asia Bhutan 12 31 2022
## 73 BOL South America Bolivia 12 31 2020
## 74 BOL South America Bolivia 12 31 2021
## 75 BOL South America Bolivia 12 31 2022
## 76 BES North America Bonaire Sint Eustatius and Saba 12 31 2020
## 77 BES North America Bonaire Sint Eustatius and Saba 12 31 2021
## 78 BES North America Bonaire Sint Eustatius and Saba 12 31 2022
## 79 BIH Europe Bosnia and Herzegovina 12 31 2020
## 80 BIH Europe Bosnia and Herzegovina 12 31 2021
## 81 BIH Europe Bosnia and Herzegovina 12 31 2022
## 82 BWA Africa Botswana 12 31 2020
## 83 BWA Africa Botswana 12 31 2021
## 84 BWA Africa Botswana 12 31 2022
## 85 BRA South America Brazil 12 31 2020
## 86 BRA South America Brazil 12 31 2021
## 87 BRA South America Brazil 12 31 2022
## 88 VGB North America British Virgin Islands 12 31 2020
## 89 VGB North America British Virgin Islands 12 31 2021
## 90 VGB North America British Virgin Islands 12 31 2022
## 91 BRN Asia Brunei 12 31 2020
## 92 BRN Asia Brunei 12 31 2021
## 93 BRN Asia Brunei 12 31 2022
## 94 BGR Europe Bulgaria 12 31 2020
## 95 BGR Europe Bulgaria 12 31 2021
## 96 BGR Europe Bulgaria 12 31 2022
## 97 BFA Africa Burkina Faso 12 31 2020
## 98 BFA Africa Burkina Faso 12 31 2021
## 99 BFA Africa Burkina Faso 12 31 2022
## 100 BDI Africa Burundi 12 31 2020
## 101 BDI Africa Burundi 12 31 2021
## 102 BDI Africa Burundi 12 31 2022
## 103 KHM Asia Cambodia 12 31 2020
## 104 KHM Asia Cambodia 12 31 2021
## 105 KHM Asia Cambodia 12 31 2022
## 106 CMR Africa Cameroon 12 31 2020
## 107 CMR Africa Cameroon 12 31 2021
## 108 CMR Africa Cameroon 12 31 2022
## 109 CAN North America Canada 12 31 2020
## 110 CAN North America Canada 12 31 2021
## 111 CAN North America Canada 12 31 2022
## 112 CPV Africa Cape Verde 12 31 2020
## 113 CPV Africa Cape Verde 12 31 2021
## 114 CPV Africa Cape Verde 12 31 2022
## 115 CYM North America Cayman Islands 12 31 2020
## 116 CYM North America Cayman Islands 12 31 2021
## 117 CYM North America Cayman Islands 12 31 2022
## 118 CAF Africa Central African Republic 12 31 2020
## 119 CAF Africa Central African Republic 12 31 2021
## 120 CAF Africa Central African Republic 12 31 2022
## 121 TCD Africa Chad 12 31 2020
## 122 TCD Africa Chad 12 31 2021
## 123 TCD Africa Chad 12 31 2022
## 124 CHL South America Chile 12 31 2020
## 125 CHL South America Chile 12 31 2021
## 126 CHL South America Chile 12 31 2022
## 127 CHN Asia China 12 31 2020
## 128 CHN Asia China 12 31 2021
## 129 CHN Asia China 12 31 2022
## 130 COL South America Colombia 12 31 2020
## 131 COL South America Colombia 12 31 2021
## 132 COL South America Colombia 12 31 2022
## 133 COM Africa Comoros 12 31 2020
## 134 COM Africa Comoros 12 31 2021
## 135 COM Africa Comoros 12 31 2022
## 136 COG Africa Congo 12 31 2020
## 137 COG Africa Congo 12 31 2021
## 138 COG Africa Congo 12 31 2022
## 139 COK Oceania Cook Islands 12 31 2020
## 140 COK Oceania Cook Islands 12 31 2021
## 141 COK Oceania Cook Islands 12 31 2022
## 142 CRI North America Costa Rica 12 31 2020
## 143 CRI North America Costa Rica 12 31 2021
## 144 CRI North America Costa Rica 12 31 2022
## 145 CIV Africa Cote d'Ivoire 12 31 2020
## 146 CIV Africa Cote d'Ivoire 12 31 2021
## 147 CIV Africa Cote d'Ivoire 12 31 2022
## 148 HRV Europe Croatia 12 31 2020
## 149 HRV Europe Croatia 12 31 2021
## 150 HRV Europe Croatia 12 31 2022
## 151 CUB North America Cuba 12 31 2020
## 152 CUB North America Cuba 12 31 2021
## 153 CUB North America Cuba 12 31 2022
## 154 CUW North America Curacao 12 31 2020
## 155 CUW North America Curacao 12 31 2021
## 156 CUW North America Curacao 12 31 2022
## 157 CYP Europe Cyprus 12 31 2020
## 158 CYP Europe Cyprus 12 31 2021
## 159 CYP Europe Cyprus 12 31 2022
## 160 CZE Europe Czechia 12 31 2020
## 161 CZE Europe Czechia 12 31 2021
## 162 CZE Europe Czechia 12 31 2022
## 163 COD Africa Democratic Republic of Congo 12 31 2020
## 164 COD Africa Democratic Republic of Congo 12 31 2021
## 165 COD Africa Democratic Republic of Congo 12 31 2022
## 166 DNK Europe Denmark 12 31 2020
## 167 DNK Europe Denmark 12 31 2021
## 168 DNK Europe Denmark 12 31 2022
## 169 DJI Africa Djibouti 12 31 2020
## 170 DJI Africa Djibouti 12 31 2021
## 171 DJI Africa Djibouti 12 31 2022
## 172 DMA North America Dominica 12 31 2020
## 173 DMA North America Dominica 12 31 2021
## 174 DMA North America Dominica 12 31 2022
## 175 DOM North America Dominican Republic 12 31 2020
## 176 DOM North America Dominican Republic 12 31 2021
## 177 DOM North America Dominican Republic 12 31 2022
## 178 ECU South America Ecuador 12 31 2020
## 179 ECU South America Ecuador 12 31 2021
## 180 ECU South America Ecuador 12 31 2022
## 181 EGY Africa Egypt 12 31 2020
## 182 EGY Africa Egypt 12 31 2021
## 183 EGY Africa Egypt 12 31 2022
## 184 SLV North America El Salvador 12 31 2020
## 185 SLV North America El Salvador 12 31 2021
## 186 SLV North America El Salvador 12 31 2022
## 187 GNQ Africa Equatorial Guinea 12 31 2020
## 188 GNQ Africa Equatorial Guinea 12 31 2021
## 189 GNQ Africa Equatorial Guinea 12 31 2022
## 190 ERI Africa Eritrea 12 31 2020
## 191 ERI Africa Eritrea 12 31 2021
## 192 ERI Africa Eritrea 12 31 2022
## 193 EST Europe Estonia 12 31 2020
## 194 EST Europe Estonia 12 31 2021
## 195 EST Europe Estonia 12 31 2022
## 196 SWZ Africa Eswatini 12 31 2020
## 197 SWZ Africa Eswatini 12 31 2021
## 198 SWZ Africa Eswatini 12 31 2022
## 199 ETH Africa Ethiopia 12 31 2020
## 200 ETH Africa Ethiopia 12 31 2021
## 201 ETH Africa Ethiopia 12 31 2022
## 202 FRO Europe Faeroe Islands 12 31 2020
## 203 FRO Europe Faeroe Islands 12 31 2021
## 204 FRO Europe Faeroe Islands 12 31 2022
## 205 FLK South America Falkland Islands 12 31 2020
## 206 FLK South America Falkland Islands 12 31 2021
## 207 FLK South America Falkland Islands 12 31 2022
## 208 FJI Oceania Fiji 12 31 2020
## 209 FJI Oceania Fiji 12 31 2021
## 210 FJI Oceania Fiji 12 31 2022
## 211 FIN Europe Finland 12 31 2020
## 212 FIN Europe Finland 12 31 2021
## 213 FIN Europe Finland 12 31 2022
## 214 FRA Europe France 12 31 2020
## 215 FRA Europe France 12 31 2021
## 216 FRA Europe France 12 31 2022
## 217 GUF South America French Guiana 12 31 2020
## 218 GUF South America French Guiana 12 31 2021
## 219 GUF South America French Guiana 12 31 2022
## 220 PYF Oceania French Polynesia 12 31 2020
## 221 PYF Oceania French Polynesia 12 31 2021
## 222 PYF Oceania French Polynesia 12 31 2022
## 223 GAB Africa Gabon 12 31 2020
## 224 GAB Africa Gabon 12 31 2021
## 225 GAB Africa Gabon 12 31 2022
## 226 GMB Africa Gambia 12 31 2020
## 227 GMB Africa Gambia 12 31 2021
## 228 GMB Africa Gambia 12 31 2022
## 229 GEO Asia Georgia 12 31 2020
## 230 GEO Asia Georgia 12 31 2021
## 231 GEO Asia Georgia 12 31 2022
## 232 DEU Europe Germany 12 31 2020
## 233 DEU Europe Germany 12 31 2021
## 234 DEU Europe Germany 12 31 2022
## 235 GHA Africa Ghana 12 31 2020
## 236 GHA Africa Ghana 12 31 2021
## 237 GHA Africa Ghana 12 31 2022
## 238 GIB Europe Gibraltar 12 31 2020
## 239 GIB Europe Gibraltar 12 31 2021
## 240 GIB Europe Gibraltar 12 31 2022
## 241 GRC Europe Greece 12 31 2020
## 242 GRC Europe Greece 12 31 2021
## 243 GRC Europe Greece 12 31 2022
## 244 GRL North America Greenland 12 31 2020
## 245 GRL North America Greenland 12 31 2021
## 246 GRL North America Greenland 12 31 2022
## 247 GRD North America Grenada 12 31 2020
## 248 GRD North America Grenada 12 31 2021
## 249 GRD North America Grenada 12 31 2022
## 250 GLP North America Guadeloupe 12 31 2020
## 251 GLP North America Guadeloupe 12 31 2021
## 252 GLP North America Guadeloupe 12 31 2022
## 253 GUM Oceania Guam 12 31 2020
## 254 GUM Oceania Guam 12 31 2021
## 255 GUM Oceania Guam 12 31 2022
## 256 GTM North America Guatemala 12 31 2020
## 257 GTM North America Guatemala 12 31 2021
## 258 GTM North America Guatemala 12 31 2022
## 259 GGY Europe Guernsey 12 31 2020
## 260 GGY Europe Guernsey 12 31 2021
## 261 GGY Europe Guernsey 12 31 2022
## 262 GIN Africa Guinea 12 31 2020
## 263 GIN Africa Guinea 12 31 2021
## 264 GIN Africa Guinea 12 31 2022
## 265 GNB Africa Guinea-Bissau 12 31 2020
## 266 GNB Africa Guinea-Bissau 12 31 2021
## 267 GNB Africa Guinea-Bissau 12 31 2022
## 268 GUY South America Guyana 12 31 2020
## 269 GUY South America Guyana 12 31 2021
## 270 GUY South America Guyana 12 31 2022
## 271 HTI North America Haiti 12 31 2020
## 272 HTI North America Haiti 12 31 2021
## 273 HTI North America Haiti 12 31 2022
## 274 HND North America Honduras 12 31 2020
## 275 HND North America Honduras 12 31 2021
## 276 HND North America Honduras 12 31 2022
## 277 HKG Asia Hong Kong 12 31 2020
## 278 HKG Asia Hong Kong 12 31 2021
## 279 HKG Asia Hong Kong 12 31 2022
## 280 HUN Europe Hungary 12 31 2020
## 281 HUN Europe Hungary 12 31 2021
## 282 HUN Europe Hungary 12 31 2022
## 283 ISL Europe Iceland 12 31 2020
## 284 ISL Europe Iceland 12 31 2021
## 285 ISL Europe Iceland 12 31 2022
## 286 IND Asia India 12 31 2020
## 287 IND Asia India 12 31 2021
## 288 IND Asia India 12 31 2022
## 289 IDN Asia Indonesia 12 31 2020
## 290 IDN Asia Indonesia 12 31 2021
## 291 IDN Asia Indonesia 12 31 2022
## 292 IRN Asia Iran 12 31 2020
## 293 IRN Asia Iran 12 31 2021
## 294 IRN Asia Iran 12 31 2022
## 295 IRQ Asia Iraq 12 31 2020
## 296 IRQ Asia Iraq 12 31 2021
## 297 IRQ Asia Iraq 12 31 2022
## 298 IRL Europe Ireland 12 31 2020
## 299 IRL Europe Ireland 12 31 2021
## 300 IRL Europe Ireland 12 31 2022
## 301 IMN Europe Isle of Man 12 31 2020
## 302 IMN Europe Isle of Man 12 31 2021
## 303 IMN Europe Isle of Man 12 31 2022
## 304 ISR Asia Israel 12 31 2020
## 305 ISR Asia Israel 12 31 2021
## 306 ISR Asia Israel 12 31 2022
## 307 ITA Europe Italy 12 31 2020
## 308 ITA Europe Italy 12 31 2021
## 309 ITA Europe Italy 12 31 2022
## 310 JAM North America Jamaica 12 31 2020
## 311 JAM North America Jamaica 12 31 2021
## 312 JAM North America Jamaica 12 31 2022
## 313 JPN Asia Japan 12 31 2020
## 314 JPN Asia Japan 12 31 2021
## 315 JPN Asia Japan 12 31 2022
## 316 JEY Europe Jersey 12 31 2020
## 317 JEY Europe Jersey 12 31 2021
## 318 JEY Europe Jersey 12 31 2022
## 319 JOR Asia Jordan 12 31 2020
## 320 JOR Asia Jordan 12 31 2021
## 321 JOR Asia Jordan 12 31 2022
## 322 KAZ Asia Kazakhstan 12 31 2020
## 323 KAZ Asia Kazakhstan 12 31 2021
## 324 KAZ Asia Kazakhstan 12 31 2022
## 325 KEN Africa Kenya 12 31 2020
## 326 KEN Africa Kenya 12 31 2021
## 327 KEN Africa Kenya 12 31 2022
## 328 KIR Oceania Kiribati 12 31 2020
## 329 KIR Oceania Kiribati 12 31 2021
## 330 KIR Oceania Kiribati 12 31 2022
## 331 KWT Asia Kuwait 12 31 2020
## 332 KWT Asia Kuwait 12 31 2021
## 333 KWT Asia Kuwait 12 31 2022
## 334 KGZ Asia Kyrgyzstan 12 31 2020
## 335 KGZ Asia Kyrgyzstan 12 31 2021
## 336 KGZ Asia Kyrgyzstan 12 31 2022
## 337 LAO Asia Laos 12 31 2020
## 338 LAO Asia Laos 12 31 2021
## 339 LAO Asia Laos 12 31 2022
## 340 LVA Europe Latvia 12 31 2020
## 341 LVA Europe Latvia 12 31 2021
## 342 LVA Europe Latvia 12 31 2022
## 343 LBN Asia Lebanon 12 31 2020
## 344 LBN Asia Lebanon 12 31 2021
## 345 LBN Asia Lebanon 12 31 2022
## 346 LSO Africa Lesotho 12 31 2020
## 347 LSO Africa Lesotho 12 31 2021
## 348 LSO Africa Lesotho 12 31 2022
## 349 LBR Africa Liberia 12 31 2020
## 350 LBR Africa Liberia 12 31 2021
## 351 LBR Africa Liberia 12 31 2022
## 352 LBY Africa Libya 12 31 2020
## 353 LBY Africa Libya 12 31 2021
## 354 LBY Africa Libya 12 31 2022
## 355 LIE Europe Liechtenstein 12 31 2020
## 356 LIE Europe Liechtenstein 12 31 2021
## 357 LIE Europe Liechtenstein 12 31 2022
## 358 LTU Europe Lithuania 12 31 2020
## 359 LTU Europe Lithuania 12 31 2021
## 360 LTU Europe Lithuania 12 31 2022
## 361 LUX Europe Luxembourg 12 31 2020
## 362 LUX Europe Luxembourg 12 31 2021
## 363 LUX Europe Luxembourg 12 31 2022
## 364 MAC Asia Macao 12 31 2021
## 365 MAC Asia Macao 12 31 2022
## 366 MDG Africa Madagascar 12 31 2020
## 367 MDG Africa Madagascar 12 31 2021
## 368 MDG Africa Madagascar 12 31 2022
## 369 MWI Africa Malawi 12 31 2020
## 370 MWI Africa Malawi 12 31 2021
## 371 MWI Africa Malawi 12 31 2022
## 372 MYS Asia Malaysia 12 31 2020
## 373 MYS Asia Malaysia 12 31 2021
## 374 MYS Asia Malaysia 12 31 2022
## 375 MDV Asia Maldives 12 31 2020
## 376 MDV Asia Maldives 12 31 2021
## 377 MDV Asia Maldives 12 31 2022
## 378 MLI Africa Mali 12 31 2020
## 379 MLI Africa Mali 12 31 2021
## 380 MLI Africa Mali 12 31 2022
## 381 MLT Europe Malta 12 31 2020
## 382 MLT Europe Malta 12 31 2021
## 383 MLT Europe Malta 12 31 2022
## 384 MHL Oceania Marshall Islands 12 31 2020
## 385 MHL Oceania Marshall Islands 12 31 2021
## 386 MHL Oceania Marshall Islands 12 31 2022
## 387 MTQ North America Martinique 12 31 2020
## 388 MTQ North America Martinique 12 31 2021
## 389 MTQ North America Martinique 12 31 2022
## 390 MRT Africa Mauritania 12 31 2020
## 391 MRT Africa Mauritania 12 31 2021
## 392 MRT Africa Mauritania 12 31 2022
## 393 MUS Africa Mauritius 12 31 2020
## 394 MUS Africa Mauritius 12 31 2021
## 395 MUS Africa Mauritius 12 31 2022
## 396 MYT Africa Mayotte 12 31 2020
## 397 MYT Africa Mayotte 12 31 2021
## 398 MYT Africa Mayotte 12 31 2022
## 399 MEX North America Mexico 12 31 2020
## 400 MEX North America Mexico 12 31 2021
## 401 MEX North America Mexico 12 31 2022
## 402 FSM Oceania Micronesia (country) 12 31 2020
## 403 FSM Oceania Micronesia (country) 12 31 2021
## 404 FSM Oceania Micronesia (country) 12 31 2022
## 405 MDA Europe Moldova 12 31 2020
## 406 MDA Europe Moldova 12 31 2021
## 407 MDA Europe Moldova 12 31 2022
## 408 MCO Europe Monaco 12 31 2020
## 409 MCO Europe Monaco 12 31 2021
## 410 MCO Europe Monaco 12 31 2022
## 411 MNG Asia Mongolia 12 31 2020
## 412 MNG Asia Mongolia 12 31 2021
## 413 MNG Asia Mongolia 12 31 2022
## 414 MNE Europe Montenegro 12 31 2020
## 415 MNE Europe Montenegro 12 31 2021
## 416 MNE Europe Montenegro 12 31 2022
## 417 MSR North America Montserrat 12 31 2020
## 418 MSR North America Montserrat 12 31 2021
## 419 MSR North America Montserrat 12 31 2022
## 420 MAR Africa Morocco 12 31 2020
## 421 MAR Africa Morocco 12 31 2021
## 422 MAR Africa Morocco 12 31 2022
## 423 MOZ Africa Mozambique 12 31 2020
## 424 MOZ Africa Mozambique 12 31 2021
## 425 MOZ Africa Mozambique 12 31 2022
## 426 MMR Asia Myanmar 12 31 2020
## 427 MMR Asia Myanmar 12 31 2021
## 428 MMR Asia Myanmar 12 31 2022
## 429 NAM Africa Namibia 12 31 2020
## 430 NAM Africa Namibia 12 31 2021
## 431 NAM Africa Namibia 12 31 2022
## 432 NRU Oceania Nauru 12 31 2020
## 433 NRU Oceania Nauru 12 31 2021
## 434 NRU Oceania Nauru 12 31 2022
## 435 NPL Asia Nepal 12 31 2020
## 436 NPL Asia Nepal 12 31 2021
## 437 NPL Asia Nepal 12 31 2022
## 438 NLD Europe Netherlands 12 31 2020
## 439 NLD Europe Netherlands 12 31 2021
## 440 NLD Europe Netherlands 12 31 2022
## 441 NCL Oceania New Caledonia 12 31 2020
## 442 NCL Oceania New Caledonia 12 31 2021
## 443 NCL Oceania New Caledonia 12 31 2022
## 444 NZL Oceania New Zealand 12 31 2020
## 445 NZL Oceania New Zealand 12 31 2021
## 446 NZL Oceania New Zealand 12 31 2022
## 447 NIC North America Nicaragua 12 31 2020
## 448 NIC North America Nicaragua 12 31 2021
## 449 NIC North America Nicaragua 12 31 2022
## 450 NER Africa Niger 12 31 2020
## 451 NER Africa Niger 12 31 2021
## 452 NER Africa Niger 12 31 2022
## 453 NGA Africa Nigeria 12 31 2020
## 454 NGA Africa Nigeria 12 31 2021
## 455 NGA Africa Nigeria 12 31 2022
## 456 NIU Oceania Niue 12 31 2020
## 457 NIU Oceania Niue 12 31 2021
## 458 NIU Oceania Niue 12 31 2022
## 459 PRK Asia North Korea 12 31 2020
## 460 PRK Asia North Korea 12 31 2021
## 461 PRK Asia North Korea 12 31 2022
## 462 MKD Europe North Macedonia 12 31 2020
## 463 MKD Europe North Macedonia 12 31 2021
## 464 MKD Europe North Macedonia 12 31 2022
## 465 MNP Oceania Northern Mariana Islands 12 31 2020
## 466 MNP Oceania Northern Mariana Islands 12 31 2021
## 467 MNP Oceania Northern Mariana Islands 12 31 2022
## 468 NOR Europe Norway 12 31 2020
## 469 NOR Europe Norway 12 31 2021
## 470 NOR Europe Norway 12 31 2022
## 471 OMN Asia Oman 12 31 2020
## 472 OMN Asia Oman 12 31 2021
## 473 OMN Asia Oman 12 31 2022
## 474 PAK Asia Pakistan 12 31 2020
## 475 PAK Asia Pakistan 12 31 2021
## 476 PAK Asia Pakistan 12 31 2022
## 477 PLW Oceania Palau 12 31 2020
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## 481 PSE Asia Palestine 12 31 2021
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## 484 PAN North America Panama 12 31 2021
## 485 PAN North America Panama 12 31 2022
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## 707 41 1 <NA>covid_stats <- covid_date |>
group_by(iso3c, continent, country, year) |>
summarize(cases = sum(new_cases),
deaths = sum(new_deaths),
vaxed = sum(people_fully_vaccinated))
covid_stats <- covid_stats |>
mutate(vaxed = as.double(vaxed))
covid_stats## # A tibble: 707 × 7
## # Groups: iso3c, continent, country [236]
## iso3c continent country year cases deaths vaxed
## <chr> <chr> <chr> <dbl> <int> <int> <dbl>
## 1 ABW North America Aruba 2020 50 0 NA
## 2 ABW North America Aruba 2021 671 0 NA
## 3 ABW North America Aruba 2022 0 0 NA
## 4 AFG Asia Afghanistan 2020 0 0 NA
## 5 AFG Asia Afghanistan 2021 20 0 NA
## 6 AFG Asia Afghanistan 2022 43 2 NA
## 7 AGO Africa Angola 2020 62 2 NA
## 8 AGO Africa Angola 2021 0 0 NA
## 9 AGO Africa Angola 2022 0 0 NA
## 10 AIA North America Anguilla 2020 0 0 NA
## # ℹ 697 more rowscovid_cov <- covid_stats |>
mutate(
disease = 'COVID19',
disease_desc = 'Coronavirus 19'
)
covid_clean <- covid_cov |>
left_join(wb_population, by = c("iso3c", country = "country_name", "year")) |>
mutate(coverage = vaxed/pop_total)
covid_clean## # A tibble: 707 × 11
## # Groups: iso3c, continent, country [236]
## iso3c continent country year cases deaths vaxed disease disease_desc
## <chr> <chr> <chr> <dbl> <int> <int> <dbl> <chr> <chr>
## 1 ABW North America Aruba 2020 50 0 NA COVID19 Coronavirus…
## 2 ABW North America Aruba 2021 671 0 NA COVID19 Coronavirus…
## 3 ABW North America Aruba 2022 0 0 NA COVID19 Coronavirus…
## 4 AFG Asia Afghanistan 2020 0 0 NA COVID19 Coronavirus…
## 5 AFG Asia Afghanistan 2021 20 0 NA COVID19 Coronavirus…
## 6 AFG Asia Afghanistan 2022 43 2 NA COVID19 Coronavirus…
## 7 AGO Africa Angola 2020 62 2 NA COVID19 Coronavirus…
## 8 AGO Africa Angola 2021 0 0 NA COVID19 Coronavirus…
## 9 AGO Africa Angola 2022 0 0 NA COVID19 Coronavirus…
## 10 AIA North America Anguilla 2020 0 0 NA COVID19 Coronavirus…
## # ℹ 697 more rows
## # ℹ 2 more variables: pop_total <dbl>, coverage <dbl>#Create ID and join to data set
covid_key = mutate(covid_clean,
id = paste(iso3c, year, disease, sep = '_'))
covid_key <- covid_key |>
mutate(antig = "SARSCOV2",
antig_desc = "SARS Coronavirus 2 vaccine")
covid_key## # A tibble: 707 × 14
## # Groups: iso3c, continent, country [236]
## iso3c continent country year cases deaths vaxed disease disease_desc
## <chr> <chr> <chr> <dbl> <int> <int> <dbl> <chr> <chr>
## 1 ABW North America Aruba 2020 50 0 NA COVID19 Coronavirus…
## 2 ABW North America Aruba 2021 671 0 NA COVID19 Coronavirus…
## 3 ABW North America Aruba 2022 0 0 NA COVID19 Coronavirus…
## 4 AFG Asia Afghanistan 2020 0 0 NA COVID19 Coronavirus…
## 5 AFG Asia Afghanistan 2021 20 0 NA COVID19 Coronavirus…
## 6 AFG Asia Afghanistan 2022 43 2 NA COVID19 Coronavirus…
## 7 AGO Africa Angola 2020 62 2 NA COVID19 Coronavirus…
## 8 AGO Africa Angola 2021 0 0 NA COVID19 Coronavirus…
## 9 AGO Africa Angola 2022 0 0 NA COVID19 Coronavirus…
## 10 AIA North America Anguilla 2020 0 0 NA COVID19 Coronavirus…
## # ℹ 697 more rows
## # ℹ 5 more variables: pop_total <dbl>, coverage <dbl>, id <chr>, antig <chr>,
## # antig_desc <chr>covid_key <- covid_key[,c(12,1,2,3,4,5,6,11,8,9,13,14)]Join COVID table onto VPD data using full join (no existing COVID data to join onto via Left join)
#| echo: false
#| warning: false
#Full join of both data sets using ID field
diseases <- vpd_combo |>
full_join(covid_key, by = c("id", "iso3c", "country", "year", "cases","disease", "disease_desc", "coverage", "antig", "antig_desc"))
diseases_update <- diseases |>
mutate(vpd = case_when(disease != "COVID19" ~ "VPD",
disease == "COVID19" ~ "COVID"))
#Drop id & group for future joins and analysis
diseases_total <- diseases_update[,-c(1,2,13,14,16,17)]
diseases_total |>
view()
#Look at missingness
miss_var_summary(diseases_total)## # A tibble: 12 × 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 coverage 52604 66.3
## 2 antig 35599 44.9
## 3 antig_desc 35599 44.9
## 4 ir 18547 23.4
## 5 cases 17831 22.5
## 6 units 707 0.892
## 7 iso3c 0 0
## 8 country 0 0
## 9 year 0 0
## 10 disease 0 0
## 11 disease_desc 0 0
## 12 vpd 0 0#Reorder columns
diseases_total <- diseases_total |>
relocate(vpd, .after = year) |>
relocate(antig, .after = disease_desc) |>
relocate(antig_desc, .after = antig) |>
rename(vax = antig,
vax_desc = antig_desc) |>
view()Shrink data set down to 1990-2023 for easier manipulations but long enough for meaningful findings
#| echo: false
#| warning: false
#Filter Data from after 1990
diseases_short <- diseases_total |>
filter(year >= 1990 )
diseases_short## # A tibble: 66,512 × 12
## iso3c country year vpd disease disease_desc vax vax_desc cases units
## <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 ABW Aruba 2021 VPD RUBELLA Rubella RCV1 Rubella… 0 per …
## 2 ABW Aruba 2021 VPD POLIO Poliomyelitis IPV1 Inactiv… 0 per …
## 3 ABW Aruba 2021 VPD POLIO Poliomyelitis IPV1_F… Fractio… 0 per …
## 4 ABW Aruba 2021 VPD POLIO Poliomyelitis IPV2 Inactiv… 0 per …
## 5 ABW Aruba 2021 VPD POLIO Poliomyelitis IPV2_F… Fractio… 0 per …
## 6 ABW Aruba 2021 VPD POLIO Poliomyelitis POL3 Polio, … 0 per …
## 7 ABW Aruba 2021 VPD TTETANUS Tetanus TT2PLUS Tetanus… 0 per …
## 8 ABW Aruba 2021 VPD TTETANUS Tetanus TTCV4 Tetanus… 0 per …
## 9 ABW Aruba 2021 VPD TTETANUS Tetanus TTCV5 Tetanus… 0 per …
## 10 ABW Aruba 2021 VPD TTETANUS Tetanus TTCV6 Tetanus… 0 per …
## # ℹ 66,502 more rows
## # ℹ 2 more variables: ir <dbl>, coverage <dbl>Join onto WB country data
#| echo: false
#| warning: false
#Join to VPD data for full disease comparison data set
diseases_clean <- diseases_short |>
left_join(wb_data, by = c("iso3c", "country", "year"))
#Remove unneeded columns
diseases_clean <- diseases_clean[,-c(16)]
diseases_clean |>
select(iso3c, country, year) |>
filter(is.na(country)) |>
unique() |>
view()
diseases_clean |>
filter(grepl("antilles", country, ignore.case = TRUE))## # A tibble: 74 × 18
## iso3c country year vpd disease disease_desc vax vax_desc cases units
## <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 ANT Netherland… 2000 VPD DIPHTH… Diphtheria <NA> <NA> NA per …
## 2 ANT Netherland… 2000 VPD MUMPS Mumps <NA> <NA> NA per …
## 3 ANT Netherland… 2000 VPD MEASLES Measles MCV1 Measles… NA per …
## 4 ANT Netherland… 2000 VPD MEASLES Measles MCV2 Measles… NA per …
## 5 ANT Netherland… 2000 VPD YFEVER Yellow fever YFV Yellow … NA per …
## 6 ANT Netherland… 2000 VPD PERTUS… Pertussis <NA> <NA> NA per …
## 7 ANT Netherland… 2000 VPD POLIO Poliomyelit… POL3 Polio, … 0 per …
## 8 ANT Netherland… 2000 VPD CRS Congenital … <NA> <NA> NA per …
## 9 ANT Netherland… 2000 VPD RUBELLA Rubella <NA> <NA> NA per …
## 10 ANT Netherland… 2000 VPD TTETAN… Tetanus TT2P… Tetanus… NA per …
## # ℹ 64 more rows
## # ℹ 8 more variables: ir <dbl>, coverage <dbl>, gdp <dbl>, gdp_ppp <dbl>,
## # gni <dbl>, region <chr>, income_level <chr>, gdp_health <dbl>diseases_clean |>
filter(grepl("kosovo", country, ignore.case = TRUE))## # A tibble: 27 × 18
## iso3c country year vpd disease disease_desc vax vax_desc cases units
## <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 XKX Kosovo (in… 2021 VPD JAPENC Japanese en… JAPE… Japanes… NA per …
## 2 XKX Kosovo (in… 2021 VPD YFEVER Yellow fever YFV Yellow … NA per …
## 3 XKX Kosovo (in… 2021 VPD TTETAN… Tetanus TT2P… Tetanus… NA per …
## 4 XKX Kosovo (in… 2021 VPD TTETAN… Tetanus TTCV4 Tetanus… NA per …
## 5 XKX Kosovo (in… 2021 VPD TTETAN… Tetanus TTCV5 Tetanus… NA per …
## 6 XKX Kosovo (in… 2021 VPD TTETAN… Tetanus TTCV6 Tetanus… NA per …
## 7 XKX Kosovo (in… 2021 VPD MUMPS Mumps <NA> <NA> NA per …
## 8 XKX Kosovo (in… 2021 VPD PERTUS… Pertussis PERC… Pertuss… NA per …
## 9 XKX Kosovo (in… 2021 VPD PERTUS… Pertussis PERC… Pertuss… NA per …
## 10 XKX Kosovo (in… 2021 VPD CRS Congenital … <NA> <NA> NA per …
## # ℹ 17 more rows
## # ℹ 8 more variables: ir <dbl>, coverage <dbl>, gdp <dbl>, gdp_ppp <dbl>,
## # gni <dbl>, region <chr>, income_level <chr>, gdp_health <dbl>#clean up names & add flag
diseases_clean <- diseases_clean |>
mutate(flag = iso3c_to_x(iso3c, destination = "unicode.symbol"))
diseases_clean |>
select(iso3c, country, year) |>
filter(iso3c == "ANT" | iso3c == "XKX") |>
unique() |>
view()Add IMF Growth Data
#IMF Growth Data Set Import
imf_growth <- read_excel(
"C:/Users/joeau/OneDrive - Johns Hopkins/SAIS/Year 2/Sustainable Finance/final_project/00_data_raw/ECON/imf_export.xls")
imf_growth <- imf_growth |>
slice(2:n()) |>
pivot_longer(!'Real GDP growth (Annual percent change)', names_to = "year", values_to = "growth_rate") |>
mutate(growth_rate = ifelse(growth_rate == "no data", "", growth_rate),
growth_rate = format(growth_rate, nsmall = 2),
growth_rate = as.numeric(growth_rate),
year = as.numeric(year)) |>
rename(country = 'Real GDP growth (Annual percent change)') |>
view()
#Filter down to after 1990
imf_growth <- imf_growth |>
filter(year >= 1990)
#Check countries
imf_growth |>
distinct(country) |>
view()
#Clean names
imf <- imf_growth |>
mutate(iso3c = country_name_regex_to_iso3c(country),
country = iso3c_to_country_name(iso3c))
imf <- imf[,c(4,1,2,3)]
#Join onto main data set
diseases_final <- diseases_clean |>
left_join(imf, by = c("iso3c", "country", "year"))Now that data is assembled, write to a .csv file for storage and use later in analysis and exploration
path_out = 'C:\\Users\\joeau\\OneDrive - Johns Hopkins\\SAIS\\Year 2\\Sustainable Finance\\final_project\\03_data_processed\\'
fileName = paste(path_out, 'vpd_covid_project.csv',sep = '')
write.csv(diseases_final,fileName)For part of this analysis, would like to analyze VPD vaccination as part of health expenditure to try to illustrate an effect of COVID-19 on VPD immunization coverage. Data seems hard to find and might be better to wait for data exploration of particular pathogens to narrow down data to download?
Ideas:
By specific antigen
Over year period
Not market price, contracted price
Further, for vaccine prices does it make sense to show prices over time if they have been negotiated through multi-laterals like UNICEF? These are likely subsidized costs compared to market price. Better to narrow down to most burdensome VPDs and then show prices so data is available?
Interested in presenting something similar to this:
Another issue identified is a mismatch in vaccination coverage (2 parts)
Vaccination data - lists additional vaccines for pathogens not in case data set. Current plan is to only analyze pathogens in case data set and exclude others in vaccination coverage.
There are some combination vaccines that are skewing data - DPT is a Diptheria/Pertussis combination vaccine and join is only occurring on Diptheria. Is there a known work around or way to approximate match so it double counts only in this instance for both pathogens?
Another two issues are standardization of units
Incident rates are measures as either per 1,000,000 <15 population, per 1,000 live births, or per 1,000,000 total population
Vaccination coverage is measured both at the administrative and official levels
Econ data in total $USD (maybe present in $M USD?)
Finally, the message I am trying to end this project with is not only the disruption of COVID-19 and the burden of VPDs/Vaccine coverages, but to also show the value of investing and funding these vaccines for impact investors:
Need to somehow show value for money
Potential for growth
Returns on human live and capital investment (R&D might be hard to show)