In R4IDSR/covidmonitor: Merge and Analyse WHO AFRO Covid-19 Monitoring and Evaluation Data
# set options for rmarkdown
knitr::opts_chunk$set(echo = FALSE, # hide all code chunks in output
message = FALSE, # hide all messages in output
warning = FALSE, # hide all warnings in output
collapse = TRUE, # combine all source/output to single block
fig.align = 'center', # centre figures on page
fig.height = 4, # define figure height
dpi = 300, # define figure definitions
cache = F) # run all code chunks (even if repeated)
## Installing required packages for this template
required_packages <- c(
"knitr", # create output docs
"kableExtra", # create nicer tables in html files
## not sure these html packages are required...
"htmltools", # working with html output
"DT", # for creating tables in html files
"webshot", # for putting images in html files
"here", # find your files
"rio", # read in data
"lubridate", # work with dates
"aweek", # define epi weeks (dont need this if using isoweek/lubridate)
"ISOweek", # define epi weeks
"tidyverse", # all tidy functions (dont need whole tidyverse - pull apart)
"janitor", # clean/shape data
"epitrix", # clean/shape data (dont think epitrix needed - use janitor)
"tidyr", # clean/shape data
"matchmaker", # dictionary-based standardization of variables
"epitools", # for two-by-two tables (this package is crap, avoid)
"skimr", # browse data
"ggrepel" # space out overlapping data in ggplot2
)
for (pkg in required_packages) {
# install packages if not already present
if (!pkg %in% rownames(installed.packages())) {
install.packages(pkg)
}
# load packages to this current session
library(pkg, character.only = TRUE)
}
## current week
this_week <- as.Date("2020-09-15")
## week number of current week
No_week <- week(this_week)
## number of expected countries reporting
expected_countries <- 46
## define path to folder with raw data
data_folder <- here::here("data", "raw")
## import linelist of confirmed cases
covid_afro_00 <-
paste0(data_folder, "/ConfirmedCases.csv") %>%
rio::import() %>%
## clean variable names
clean_names() %>%
## only keep certain columns
select(reporting_date,
country,
outcome,
date_of_death,
healthcare_worker,
province)
## import data dictionary
cleaning_rules <- rio::import(here::here("data","cleaned","cleaning_rules_s.xlsx"))
## import data defining transmission pattern
Tab_transm <-
paste0(data_folder, "/Infection_classification_template_final_week34.xlsx") %>%
import(sheet = 1, skip = 5) %>%
clean_names()
## import data on lab testing
covid_testing <- paste0(data_folder, "/lab indicator_summary_3Sept2020.xlsx") %>%
import(sheet = 1, skip = 0) %>%
clean_names()
## import case counts data from South Africa
covid_south_africa <- paste0(data_folder, "/south_africa_cases.xlsx") %>%
import(sheet = 1, skip = 0) %>%
clean_names()
## import case counts data from WHO eastern mediteranean region
covid_emro<- paste0(data_folder, "/emro_cases.xlsx") %>%
import(sheet = 1, skip = 0) %>%
clean_names()
## fix date variables in original dataset
covid_afro_00$date_of_death <- lubridate::as_date(covid_afro_00$date_of_death)
covid_afro_00$reporting_date <- lubridate::as_date(covid_afro_00$reporting_date)
## create clean dataset by recoding variables based on dictionary
covid_afro <- match_df(covid_afro_00, dictionary = cleaning_rules)
## find the date of the initial report (for whole region)
covid_afro$first_report <- min(covid_afro$reporting_date)
# create epi-week variables (return the first date of the week it is in)
covid_afro$epi_week <- floor_date(covid_afro$reporting_date,
"week", week_start = 1)
covid_afro$epi_week2 <- floor_date(covid_afro$date_of_death,
"week", week_start = 1)
## create an epi week variable as calendar week
## so can show prev week easier
covid_afro$calendar_week <- week(covid_afro$reporting_date)
covid_afro$calendar_week2 <- week(covid_afro$date_of_death)
## Cumulative number of cases
## define a dataset to use for case counts (seems unecessary considering so large, just use direct)
cumul_cases <-
covid_afro %>%
## only keep rows with a defined country
filter(country!= "")
## get the cumulative number of cases (here too could just do directly)
cumul_cases_ok <- nrow(cumul_cases)
## define a dataset to use for death counts (seems unecessary considering so large, just use direct)
cumul_deaths <-
covid_afro %>%
## only keep rows with defined country and died
filter(country != "" &
outcome == "Dead")
## get the cumulative number of deaths (here too could just do directly)
cumul_deaths_ok <- nrow(cumul_deaths)
## CFR
CFR <- round( (cumul_deaths_ok / cumul_cases_ok) * 100,
digits = 2)
## define a dataset to use for recovery counts (seems unecessary considering so large, just use direct)
cumul_recoveries <-
covid_afro %>%
filter(country != "" &
outcome == "Recovered")
## get the cumulative number of recoveries (here too could just do directly)
cumul_recoveries_ok <- nrow(cumul_recoveries)
perc_recovered <- round(
(cumul_recoveries_ok / cumul_cases_ok) * 100,
digits = 1)
## define a dataset to use for countries reporting (seems unecessary considering so large, just use direct)
tab_afro_cum <- covid_afro %>%
## only keep non-missings that were reported in the last week
filter(country !="NA" & # (not why this here, but maybe data comes out weird)
!is.na(country) &
reporting_date > (this_week - 7) &
reporting_date <= this_week) %>%
count(country) %>%
group_by(country) # not sure group_by necessary, leaving for now (incase used later down)
## get number countries that reported cases this week
nb_countries_this_week <- nrow(tab_afro_cum)
## get the percentage of expected that reported
perc_countries_this_week <- round(
(nb_countries_this_week / expected_countries) * 100,
digits = 1)
## get the cummulative number of cases in EMRO (could just use this directly in text?)
nb_cases_emro <- covid_emro$cumulative_cases
## get the cumulative number of cases in afro and emro combined
nb_cases_afric <- cumul_cases_ok + nb_cases_emro
## get the cumulative number of deaths in EMRO
nb_deaths_emro <- covid_emro$cumul_deaths
## get the cumulative num deaths in afro and emro combined
nb_deaths_afric <- cumul_deaths_ok + nb_deaths_emro
## get the case fatality ratio for whole continent
CFR_Afric <- round(
(nb_deaths_afric/nb_cases_afric) * 100,
digits = 1)
## define a dataset to use for south africa (not neccesary?)
cumul_cases_sa <-
covid_afro %>%
filter(country == "South Africa")
## get the number of cases in south africa
cumul_cases_sa <- nrow(cumul_cases_sa)
## get the percentage that south africa makes of the total
perc_cases_sa <- round( (cumul_cases_sa/ cumul_cases_ok) * 100,
digits = 1)
# Cumul cases past 7 days
cumul_cases_past7 <-
covid_afro %>%
## only keep non-missings that were reported in the last week
filter(country !="NA" & # (not why this here, but maybe data comes out weird)
!is.na(country) &
reporting_date > (this_week - 7) &
reporting_date <= this_week) %>%
count(country) %>%
group_by(country) # not sure this is necessary either...
## get the total number of cases in last seven days
new_cases_last7 <- nrow(cumul_cases_past7)
## define data set to use for getting countries with less than 1000 cases
tab_less1000_1 <- covid_afro %>%
filter(country!="NA") %>%
count(country) %>%
filter(n < 1000)
## get counts
nb_countries_less1000 <- nrow(tab_less1000_1)
## get case counts for prev week
cumul_cases_prev <- covid_afro %>%
filter(calendar_week < No_week) %>%
nrow()
## get death counts for prev week
## (may need to cross check this with missing dates)
## there are missing dates so maybe just do it based on outcome variable and date of report
cumul_deaths_prev <- covid_afro %>%
filter(calendar_week2 < No_week &
!is.na(calendar_week2)) %>%
nrow()
Epidemiological update
-
As of r this_week, on the African continent there werer nb_cases_afric
reported cases and r nb_deaths_afric deaths.
( r CFR_Afric % of case fatality rate).
-
In the WHO African Region there are r cumul_cases_ok cases and
r cumul_deaths_okdeaths (cf. r cumul_cases_prev cases and r cumul_deaths_prev
deaths last week) - about r perc_cases_sa% of cumulative cases in South Africa.
-
r new_cases_last7cases recorded over the past 7 days - top 10 account for
over 83% of these cases ( cf. 33,500 and 88% last week) -Table I)
tab_top10_14days <- covid_afro %>%
filter(country!="NA") %>%
filter(reporting_date>="2020-09-09") %>%
select(country) %>%
count(country) %>%
arrange(desc(n)) %>%
mutate(total_cases=sum(n)) %>%
mutate(nb_cases=n) %>%
mutate(perc_cases=round((nb_cases/total_cases)*100,1)) %>%
filter(nb_cases>=536) %>%
janitor::adorn_totals("row",fill="-",na.rm=TRUE) %>%
select(country, nb_cases, perc_cases) %>%
kable(caption="Tableau I: Countries that reported the highest number of cases during the last 14 days") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11)
tab_top10_14days
- Still
r nb_countries_less1000 countries with fewer than 1000 reported cases (Table II)
tab_less1000 <- covid_afro %>%
filter(country!="NA") %>%
select(country) %>%
count(country) %>%
arrange(desc(n)) %>%
mutate(nb_cases=n) %>%
filter(nb_cases<1000) %>%
select(country, nb_cases) %>%
kable(caption="Tableau II: Countries twith less than 1,000 cumulative cases") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11)
tab_less1000
- South Africa has
r cumul_cases_sa cases.
covid_south_africa$date <- as.character(covid_south_africa$date)
covid_south_africa$date <- recode(covid_south_africa$date, "2020-09-08"="Sept02_Sep08", "2020-09-01"="Aug26_Sept01")
tab_south_Africa <- covid_south_africa %>%
select(date, province, cumulative_cases) %>%
group_by(date, province) %>%
spread(date,cumulative_cases, fill=0) %>%
janitor::adorn_totals("row",fill="-",na.rm=TRUE) %>%
mutate(perc_increase_in_cumulative=round(((Sept02_Sep08/Aug26_Sept01)-1)*100,1)) %>%
kable(caption="Tableau III: Cumulative cases by province in South Africa") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11)
tab_south_Africa
change in number of new cases during the last 7 days
r perc_countries_this_week% of countries (r nb_countries_this_week out of 46) reported new cases during the last 14 days.
The number of cases reported over the past 14 days increased by over 20% compared to the previous 14 days in 24 countries: SEychelles (3 new cases during the last 14 days Vs 0 the previous 14 days), Burkina Faso, Eritrea, Lesotho, Comoros, Sao Tome and Principe, Central Africa Republic, Uganda, Mozambique, South Sudan, Mali, Sierra Leone, Chad, Cape Verde, Botswana, Togo, Zimbabwe, Angola, Mauritania, Burundi, Madagascar, Guinea, Cote d'ivoire, and Cameroon.
covid_afro$period <- ifelse(covid_afro$reporting_date>="2020-09-02", "Sept02_Sept15",
ifelse(covid_afro$reporting_date<"2020-09-02"&covid_afro$reporting_date>="2020-08-23", "Aug23_Sept01","Before_Aug23"))
table(covid_afro$period)
tab_increase <- covid_afro %>%
filter(country!="") %>%
select(country,period) %>%
count(country,period) %>%
spread(period, n, fill=0) %>%
mutate(perc_increase=round(((Sept02_Sept15-Aug23_Sept01)/Aug23_Sept01)*100,1)) %>%
arrange(desc(perc_increase)) %>%
filter(perc_increase>=20) %>%
select(country, Sept02_Sept15,Aug23_Sept01,perc_increase ) %>%
kable(caption="Tableau IV: Countries the number of new cases increased by 20% over the past 14 days") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11)
nrow(tab_increase)
tab_increase
- The number of cases reported over the past 14 days remained stabe (increased of decreased by less than 20% compared to the previous 14 days) in 17 countries, including South Africa and Algeria where the number of cases during the last 7 days increased respectively by 9.7% and 3.6%.
tab_increase1 <- covid_afro %>%
filter(country!="") %>%
select(country,period) %>%
count(country,period) %>%
spread(period, n, fill=0) %>%
mutate(perc_increase=round(((Sept02_Sept15-Aug23_Sept01)/Aug23_Sept01)*100,1)) %>%
arrange(desc(perc_increase)) %>%
filter(perc_increase>-20) %>%
filter(perc_increase<20) %>%
select(country, Sept02_Sept15,Aug23_Sept01,perc_increase ) %>%
kable(caption="Tableau V: Countries the number of new cases increased or decreased by less than 20% over the past 14 days") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11)
nrow(tab_increase1)
tab_increase1
- The number of cases reported over the past 14 days decreased by over 20% (compared to the previous 14 days) in 5 countries: Gambia (-34.3%), Malawi (-35.6%), Congo (-60.7%), Rwanda (-61.5%) and Guinea-Bissau (-68.8%).
tab_increase2 <- covid_afro %>%
filter(country!="") %>%
select(country,period) %>%
count(country,period) %>%
spread(period, n, fill=0) %>%
mutate(perc_increase=round(((Sept02_Sept15-Aug23_Sept01)/Aug23_Sept01)*100,1)) %>%
arrange(desc(perc_increase)) %>%
filter(perc_increase<=-20) %>%
select(country, Sept02_Sept15,Aug23_Sept01,perc_increase )%>%
kable(caption="Tableau VI: Countries the number of new cases decreased by 20% over the past 14 days") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11)
tab_increase2
HCW infections
- The region continues to record infections in Health workers. As of
r this_week, 41 540 infections in health workers have been recorded in 42 countries of the region, reppresenting 3.8% of all cases (Table V). The % of cases among health workers is above 5% in 12 countries: Liberia, Niger, Guinea-Bissau, Eq Guinea,Mauritius,Sierra Leone,Burundi, Chad, Burkina Faso, Benin, Mozambique and Eswatini.
- Comoros, Eritrea, Mali, Rwanda and Seychelles are the only five countries that have not yet recorded infections in health workers.
tab_hcw <- covid_afro %>%
filter(country!="") %>%
subset(healthcare_worker=="Yes"|healthcare_worker=="yes") %>%
select(country) %>%
count(country) %>%
group_by(country) %>%
summarise(nb_hcw=sum(n)) %>%
select(country, nb_hcw)
tab_afro_cases <- covid_afro %>%
filter(country!="NA") %>%
select(country) %>%
count(country) %>%
mutate(total_cases=n) %>%
select(country, total_cases)
tab_hcw_cases <- tab_afro_cases %>%
left_join(tab_hcw, by="country") %>%
mutate_all(funs(replace_na(., 0))) %>%
janitor::adorn_totals("row",fill="-",na.rm=TRUE) %>%
mutate(perc_cases_hcw=round((nb_hcw/total_cases)*100,1)) %>%
select(country, total_cases, nb_hcw, perc_cases_hcw) %>%
arrange(desc(perc_cases_hcw)) %>%
set_names("Country","Cumulative cases","Nb HWs","%") %>%
kable(caption="Tableau VII: Distribution of COVID-19 cases among health workers by country in the Africa region") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11)
tab_hcw_cases
Testing update
- As of 03 September 2020, a total of 8,831,870 tests have been performed in the region (85.8 tests per 10,000 people and 12% of positivity rate). Nine countries have performed over 200 tests per 10,000 (Cape Verde, South Africa, Gabon, Mauritius, Botswana, Equatorial Guinea, Seychelles, Rwanda, and Namibia). Twenty-one countries have a positivity rate between 3% and 12% , but only four have over 200 cumulative tests per 10,000 people (Seychelles, Equatorial Guinea and Cape verde).
names(covid_testing)
tab_tests <- covid_testing %>%
filter(cumulative_pcr_sept03!="") %>%
select(country, cumulative_pcr_sept03,test_capita_sept03,
positivity_sept03 ) %>%
arrange(desc(test_capita_sept03)) %>%
kable(caption="Tableau VIII: Cumulative test per capita and positivity rate by country in the Africa nregion") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11)
tab_tests
- Among 23 countries where the number of new cases decreased by at least 20% over the past 14 days, 18 recorded less than 5% of positivity rate during the last 14 days. In these 18 countries, the pandemic is about to be controlled, assuming that surveillance for suspected cases is comprehensive.
tab_increase3 <- covid_afro %>%
filter(country!="") %>%
select(country,period) %>%
count(country,period) %>%
spread(period, n, fill=0) %>%
mutate(perc_increase=round(((Aug26_Sept08-Aug12_Aug25)/Aug12_Aug25)*100,1)) %>%
arrange(desc(perc_increase)) %>%
filter(perc_increase<=-20)
tab_tests2 <- covid_testing %>%
filter(tests_previous14!="") %>%
filter(positivity_rate_last14>0) %>%
select(country, positivity_rate_last14 )
tab_positivity_countries_declining <- tab_increase3 %>%
left_join(tab_tests2, by="country") %>%
mutate_all(funs(replace_na(., 0))) %>%
arrange(desc(positivity_rate_last14)) %>%
select(country,perc_increase, positivity_rate_last14 ) %>%
kable(caption="Tableau IX: positivity rate in countries where is declining trend") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11)
tab_positivity_countries_declining
R4IDSR/covidmonitor documentation built on March 29, 2021, 12:05 p.m.
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# set options for rmarkdown knitr::opts_chunk$set(echo = FALSE, # hide all code chunks in output message = FALSE, # hide all messages in output warning = FALSE, # hide all warnings in output collapse = TRUE, # combine all source/output to single block fig.align = 'center', # centre figures on page fig.height = 4, # define figure height dpi = 300, # define figure definitions cache = F) # run all code chunks (even if repeated) ## Installing required packages for this template required_packages <- c( "knitr", # create output docs "kableExtra", # create nicer tables in html files ## not sure these html packages are required... "htmltools", # working with html output "DT", # for creating tables in html files "webshot", # for putting images in html files "here", # find your files "rio", # read in data "lubridate", # work with dates "aweek", # define epi weeks (dont need this if using isoweek/lubridate) "ISOweek", # define epi weeks "tidyverse", # all tidy functions (dont need whole tidyverse - pull apart) "janitor", # clean/shape data "epitrix", # clean/shape data (dont think epitrix needed - use janitor) "tidyr", # clean/shape data "matchmaker", # dictionary-based standardization of variables "epitools", # for two-by-two tables (this package is crap, avoid) "skimr", # browse data "ggrepel" # space out overlapping data in ggplot2 ) for (pkg in required_packages) { # install packages if not already present if (!pkg %in% rownames(installed.packages())) { install.packages(pkg) } # load packages to this current session library(pkg, character.only = TRUE) }
## current week this_week <- as.Date("2020-09-15") ## week number of current week No_week <- week(this_week) ## number of expected countries reporting expected_countries <- 46
## define path to folder with raw data data_folder <- here::here("data", "raw") ## import linelist of confirmed cases covid_afro_00 <- paste0(data_folder, "/ConfirmedCases.csv") %>% rio::import() %>% ## clean variable names clean_names() %>% ## only keep certain columns select(reporting_date, country, outcome, date_of_death, healthcare_worker, province) ## import data dictionary cleaning_rules <- rio::import(here::here("data","cleaned","cleaning_rules_s.xlsx")) ## import data defining transmission pattern Tab_transm <- paste0(data_folder, "/Infection_classification_template_final_week34.xlsx") %>% import(sheet = 1, skip = 5) %>% clean_names() ## import data on lab testing covid_testing <- paste0(data_folder, "/lab indicator_summary_3Sept2020.xlsx") %>% import(sheet = 1, skip = 0) %>% clean_names() ## import case counts data from South Africa covid_south_africa <- paste0(data_folder, "/south_africa_cases.xlsx") %>% import(sheet = 1, skip = 0) %>% clean_names() ## import case counts data from WHO eastern mediteranean region covid_emro<- paste0(data_folder, "/emro_cases.xlsx") %>% import(sheet = 1, skip = 0) %>% clean_names()
## fix date variables in original dataset covid_afro_00$date_of_death <- lubridate::as_date(covid_afro_00$date_of_death) covid_afro_00$reporting_date <- lubridate::as_date(covid_afro_00$reporting_date) ## create clean dataset by recoding variables based on dictionary covid_afro <- match_df(covid_afro_00, dictionary = cleaning_rules) ## find the date of the initial report (for whole region) covid_afro$first_report <- min(covid_afro$reporting_date) # create epi-week variables (return the first date of the week it is in) covid_afro$epi_week <- floor_date(covid_afro$reporting_date, "week", week_start = 1) covid_afro$epi_week2 <- floor_date(covid_afro$date_of_death, "week", week_start = 1) ## create an epi week variable as calendar week ## so can show prev week easier covid_afro$calendar_week <- week(covid_afro$reporting_date) covid_afro$calendar_week2 <- week(covid_afro$date_of_death)
## Cumulative number of cases ## define a dataset to use for case counts (seems unecessary considering so large, just use direct) cumul_cases <- covid_afro %>% ## only keep rows with a defined country filter(country!= "") ## get the cumulative number of cases (here too could just do directly) cumul_cases_ok <- nrow(cumul_cases) ## define a dataset to use for death counts (seems unecessary considering so large, just use direct) cumul_deaths <- covid_afro %>% ## only keep rows with defined country and died filter(country != "" & outcome == "Dead") ## get the cumulative number of deaths (here too could just do directly) cumul_deaths_ok <- nrow(cumul_deaths) ## CFR CFR <- round( (cumul_deaths_ok / cumul_cases_ok) * 100, digits = 2) ## define a dataset to use for recovery counts (seems unecessary considering so large, just use direct) cumul_recoveries <- covid_afro %>% filter(country != "" & outcome == "Recovered") ## get the cumulative number of recoveries (here too could just do directly) cumul_recoveries_ok <- nrow(cumul_recoveries) perc_recovered <- round( (cumul_recoveries_ok / cumul_cases_ok) * 100, digits = 1) ## define a dataset to use for countries reporting (seems unecessary considering so large, just use direct) tab_afro_cum <- covid_afro %>% ## only keep non-missings that were reported in the last week filter(country !="NA" & # (not why this here, but maybe data comes out weird) !is.na(country) & reporting_date > (this_week - 7) & reporting_date <= this_week) %>% count(country) %>% group_by(country) # not sure group_by necessary, leaving for now (incase used later down) ## get number countries that reported cases this week nb_countries_this_week <- nrow(tab_afro_cum) ## get the percentage of expected that reported perc_countries_this_week <- round( (nb_countries_this_week / expected_countries) * 100, digits = 1) ## get the cummulative number of cases in EMRO (could just use this directly in text?) nb_cases_emro <- covid_emro$cumulative_cases ## get the cumulative number of cases in afro and emro combined nb_cases_afric <- cumul_cases_ok + nb_cases_emro ## get the cumulative number of deaths in EMRO nb_deaths_emro <- covid_emro$cumul_deaths ## get the cumulative num deaths in afro and emro combined nb_deaths_afric <- cumul_deaths_ok + nb_deaths_emro ## get the case fatality ratio for whole continent CFR_Afric <- round( (nb_deaths_afric/nb_cases_afric) * 100, digits = 1) ## define a dataset to use for south africa (not neccesary?) cumul_cases_sa <- covid_afro %>% filter(country == "South Africa") ## get the number of cases in south africa cumul_cases_sa <- nrow(cumul_cases_sa) ## get the percentage that south africa makes of the total perc_cases_sa <- round( (cumul_cases_sa/ cumul_cases_ok) * 100, digits = 1) # Cumul cases past 7 days cumul_cases_past7 <- covid_afro %>% ## only keep non-missings that were reported in the last week filter(country !="NA" & # (not why this here, but maybe data comes out weird) !is.na(country) & reporting_date > (this_week - 7) & reporting_date <= this_week) %>% count(country) %>% group_by(country) # not sure this is necessary either... ## get the total number of cases in last seven days new_cases_last7 <- nrow(cumul_cases_past7) ## define data set to use for getting countries with less than 1000 cases tab_less1000_1 <- covid_afro %>% filter(country!="NA") %>% count(country) %>% filter(n < 1000) ## get counts nb_countries_less1000 <- nrow(tab_less1000_1) ## get case counts for prev week cumul_cases_prev <- covid_afro %>% filter(calendar_week < No_week) %>% nrow() ## get death counts for prev week ## (may need to cross check this with missing dates) ## there are missing dates so maybe just do it based on outcome variable and date of report cumul_deaths_prev <- covid_afro %>% filter(calendar_week2 < No_week & !is.na(calendar_week2)) %>% nrow()
Epidemiological update
-
As of
r this_week, on the African continent there werer nb_cases_africreported cases andr nb_deaths_africdeaths. (r CFR_Afric% of case fatality rate). -
In the WHO African Region there are
r cumul_cases_okcases andr cumul_deaths_okdeaths (cf.r cumul_cases_prevcases andr cumul_deaths_prevdeaths last week) - aboutr perc_cases_sa% of cumulative cases in South Africa. -
r new_cases_last7cases recorded over the past 7 days - top 10 account for over 83% of these cases ( cf. 33,500 and 88% last week) -Table I)
tab_top10_14days <- covid_afro %>% filter(country!="NA") %>% filter(reporting_date>="2020-09-09") %>% select(country) %>% count(country) %>% arrange(desc(n)) %>% mutate(total_cases=sum(n)) %>% mutate(nb_cases=n) %>% mutate(perc_cases=round((nb_cases/total_cases)*100,1)) %>% filter(nb_cases>=536) %>% janitor::adorn_totals("row",fill="-",na.rm=TRUE) %>% select(country, nb_cases, perc_cases) %>% kable(caption="Tableau I: Countries that reported the highest number of cases during the last 14 days") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11) tab_top10_14days
- Still
r nb_countries_less1000countries with fewer than 1000 reported cases (Table II)
tab_less1000 <- covid_afro %>% filter(country!="NA") %>% select(country) %>% count(country) %>% arrange(desc(n)) %>% mutate(nb_cases=n) %>% filter(nb_cases<1000) %>% select(country, nb_cases) %>% kable(caption="Tableau II: Countries twith less than 1,000 cumulative cases") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11) tab_less1000
- South Africa has
r cumul_cases_sacases.
covid_south_africa$date <- as.character(covid_south_africa$date) covid_south_africa$date <- recode(covid_south_africa$date, "2020-09-08"="Sept02_Sep08", "2020-09-01"="Aug26_Sept01") tab_south_Africa <- covid_south_africa %>% select(date, province, cumulative_cases) %>% group_by(date, province) %>% spread(date,cumulative_cases, fill=0) %>% janitor::adorn_totals("row",fill="-",na.rm=TRUE) %>% mutate(perc_increase_in_cumulative=round(((Sept02_Sep08/Aug26_Sept01)-1)*100,1)) %>% kable(caption="Tableau III: Cumulative cases by province in South Africa") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11) tab_south_Africa
change in number of new cases during the last 7 days
r perc_countries_this_week% of countries (r nb_countries_this_weekout of 46) reported new cases during the last 14 days.
The number of cases reported over the past 14 days increased by over 20% compared to the previous 14 days in 24 countries: SEychelles (3 new cases during the last 14 days Vs 0 the previous 14 days), Burkina Faso, Eritrea, Lesotho, Comoros, Sao Tome and Principe, Central Africa Republic, Uganda, Mozambique, South Sudan, Mali, Sierra Leone, Chad, Cape Verde, Botswana, Togo, Zimbabwe, Angola, Mauritania, Burundi, Madagascar, Guinea, Cote d'ivoire, and Cameroon.
covid_afro$period <- ifelse(covid_afro$reporting_date>="2020-09-02", "Sept02_Sept15", ifelse(covid_afro$reporting_date<"2020-09-02"&covid_afro$reporting_date>="2020-08-23", "Aug23_Sept01","Before_Aug23")) table(covid_afro$period) tab_increase <- covid_afro %>% filter(country!="") %>% select(country,period) %>% count(country,period) %>% spread(period, n, fill=0) %>% mutate(perc_increase=round(((Sept02_Sept15-Aug23_Sept01)/Aug23_Sept01)*100,1)) %>% arrange(desc(perc_increase)) %>% filter(perc_increase>=20) %>% select(country, Sept02_Sept15,Aug23_Sept01,perc_increase ) %>% kable(caption="Tableau IV: Countries the number of new cases increased by 20% over the past 14 days") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11) nrow(tab_increase) tab_increase
- The number of cases reported over the past 14 days remained stabe (increased of decreased by less than 20% compared to the previous 14 days) in 17 countries, including South Africa and Algeria where the number of cases during the last 7 days increased respectively by 9.7% and 3.6%.
tab_increase1 <- covid_afro %>% filter(country!="") %>% select(country,period) %>% count(country,period) %>% spread(period, n, fill=0) %>% mutate(perc_increase=round(((Sept02_Sept15-Aug23_Sept01)/Aug23_Sept01)*100,1)) %>% arrange(desc(perc_increase)) %>% filter(perc_increase>-20) %>% filter(perc_increase<20) %>% select(country, Sept02_Sept15,Aug23_Sept01,perc_increase ) %>% kable(caption="Tableau V: Countries the number of new cases increased or decreased by less than 20% over the past 14 days") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11) nrow(tab_increase1) tab_increase1
- The number of cases reported over the past 14 days decreased by over 20% (compared to the previous 14 days) in 5 countries: Gambia (-34.3%), Malawi (-35.6%), Congo (-60.7%), Rwanda (-61.5%) and Guinea-Bissau (-68.8%).
tab_increase2 <- covid_afro %>% filter(country!="") %>% select(country,period) %>% count(country,period) %>% spread(period, n, fill=0) %>% mutate(perc_increase=round(((Sept02_Sept15-Aug23_Sept01)/Aug23_Sept01)*100,1)) %>% arrange(desc(perc_increase)) %>% filter(perc_increase<=-20) %>% select(country, Sept02_Sept15,Aug23_Sept01,perc_increase )%>% kable(caption="Tableau VI: Countries the number of new cases decreased by 20% over the past 14 days") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11) tab_increase2
HCW infections
- The region continues to record infections in Health workers. As of
r this_week, 41 540 infections in health workers have been recorded in 42 countries of the region, reppresenting 3.8% of all cases (Table V). The % of cases among health workers is above 5% in 12 countries: Liberia, Niger, Guinea-Bissau, Eq Guinea,Mauritius,Sierra Leone,Burundi, Chad, Burkina Faso, Benin, Mozambique and Eswatini. - Comoros, Eritrea, Mali, Rwanda and Seychelles are the only five countries that have not yet recorded infections in health workers.
tab_hcw <- covid_afro %>% filter(country!="") %>% subset(healthcare_worker=="Yes"|healthcare_worker=="yes") %>% select(country) %>% count(country) %>% group_by(country) %>% summarise(nb_hcw=sum(n)) %>% select(country, nb_hcw) tab_afro_cases <- covid_afro %>% filter(country!="NA") %>% select(country) %>% count(country) %>% mutate(total_cases=n) %>% select(country, total_cases) tab_hcw_cases <- tab_afro_cases %>% left_join(tab_hcw, by="country") %>% mutate_all(funs(replace_na(., 0))) %>% janitor::adorn_totals("row",fill="-",na.rm=TRUE) %>% mutate(perc_cases_hcw=round((nb_hcw/total_cases)*100,1)) %>% select(country, total_cases, nb_hcw, perc_cases_hcw) %>% arrange(desc(perc_cases_hcw)) %>% set_names("Country","Cumulative cases","Nb HWs","%") %>% kable(caption="Tableau VII: Distribution of COVID-19 cases among health workers by country in the Africa region") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11) tab_hcw_cases
Testing update
- As of 03 September 2020, a total of 8,831,870 tests have been performed in the region (85.8 tests per 10,000 people and 12% of positivity rate). Nine countries have performed over 200 tests per 10,000 (Cape Verde, South Africa, Gabon, Mauritius, Botswana, Equatorial Guinea, Seychelles, Rwanda, and Namibia). Twenty-one countries have a positivity rate between 3% and 12% , but only four have over 200 cumulative tests per 10,000 people (Seychelles, Equatorial Guinea and Cape verde).
names(covid_testing) tab_tests <- covid_testing %>% filter(cumulative_pcr_sept03!="") %>% select(country, cumulative_pcr_sept03,test_capita_sept03, positivity_sept03 ) %>% arrange(desc(test_capita_sept03)) %>% kable(caption="Tableau VIII: Cumulative test per capita and positivity rate by country in the Africa nregion") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11) tab_tests
- Among 23 countries where the number of new cases decreased by at least 20% over the past 14 days, 18 recorded less than 5% of positivity rate during the last 14 days. In these 18 countries, the pandemic is about to be controlled, assuming that surveillance for suspected cases is comprehensive.
tab_increase3 <- covid_afro %>% filter(country!="") %>% select(country,period) %>% count(country,period) %>% spread(period, n, fill=0) %>% mutate(perc_increase=round(((Aug26_Sept08-Aug12_Aug25)/Aug12_Aug25)*100,1)) %>% arrange(desc(perc_increase)) %>% filter(perc_increase<=-20) tab_tests2 <- covid_testing %>% filter(tests_previous14!="") %>% filter(positivity_rate_last14>0) %>% select(country, positivity_rate_last14 ) tab_positivity_countries_declining <- tab_increase3 %>% left_join(tab_tests2, by="country") %>% mutate_all(funs(replace_na(., 0))) %>% arrange(desc(positivity_rate_last14)) %>% select(country,perc_increase, positivity_rate_last14 ) %>% kable(caption="Tableau IX: positivity rate in countries where is declining trend") %>% kable_styling(bootstrap_options = "striped", full_width = F, position = "center", font_size = 11) tab_positivity_countries_declining
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