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.width = 20, # define figure width
fig.height = 8, # 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(
"covidmonitor", # for WHO AFRO data functions
"knitr", # create output docs
"here", # find your files
"rio", # read in data
"lubridate", # work with dates
"tsibble", # for working with dates as time series
"dplyr", # clean/shape data
"ggplot2", # create plots
"countrycode", # recode country ISOs to full names
"tidyr", # clean/shape data
"flextable", # nicely formatted output tables
"matchmaker", # dictionary-based standardization of variables
"forcats" # handle factors
)
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)
}
## define a theme for ggplot (so all look similar)
epi_theme <- theme_classic() +
theme(
text = element_text(size = 45),
## rotate x axis labels
axis.text.x = element_text(angle = 45, vjust = 0.5),
axis.title = element_text(color = "black", face = "bold"),
legend.title = element_blank(),
legend.text = element_text(color = "black"),
legend.position = "bottom",
legend.direction = "horizontal",
## colour and size the grid lines in the plot
# panel.grid.minor = element_line(colour = "grey90", size = 0.5),
panel.grid.major = element_line(colour = "grey90"),
## remove axis lines
axis.line = element_blank()
)
## current week
this_week <- as.Date("2020-12-25")
## week number of current week
num_week <- yearweek(this_week)
## three weeks ago (so can show progress over last month)
weeks_of_interest <- seq(num_week - 3, num_week, by = 1)
## country of interest iso code
country_iso <- "STP"
## define path to folder with raw data
data_folder <- here::here("data", "raw")
## define path to folder with output
output_folder <- here::here("data", "clean")
## read in the dataset of interest in long format
mne_data <- merge_kpi(data_folder,
country_iso,
output_folder,
outputname = paste0("kpi_merged_",
country_iso,
"_",
this_week),
wide = FALSE)
## read in the m&e dictionary
mne_dict <- rio::import(
system.file("extdata", "mne_dictionary.xlsx",
package = "covidmonitor"),
which = "var_defs"
)
## fix the epi week variable
mne_data$epi_week <- yearweek(
paste0(
year(mne_data$date), " W", mne_data$epi_week)
)
## only keep week of interest (and 3 before)
mne_data <- mne_data %>%
filter(epi_week %in% weeks_of_interest)
## temporarily make epi_week a factor
mne_data <- mne_data %>%
mutate(
epi_week = factor(as.character(epi_week),
levels = as.character(weeks_of_interest)))
## add in missing weeks as NAs
mne_data <- mne_data %>%
complete(epi_week, indicator)
## make epi_week a yearweek again
mne_data <- mne_data %>%
mutate(epi_week = yearweek(
as.character(
epi_week)))
## add in the grouping variable from dictionary
mne_data <- mne_data %>%
mutate(grps = match_vec(indicator,
dictionary = mne_dict,
from = "var_name",
"grp_type"))
## add in the english labels for variables
mne_data <- mne_data %>%
mutate(labels = match_vec(indicator,
dictionary = mne_dict,
from = "var_name",
"english_label"))
## remove the word "performance" from the labels column (otherwise very long)
mne_data <- mne_data %>%
mutate(labels = gsub("Performance", "", labels))
## round numerics to 1 digit
mne_data <- mne_data %>%
mutate(num_vars = round(num_vars, digits = 1))
## make evaluation a factor so all levels represented
mne_data <- mne_data %>%
mutate(evaluation = factor(evaluation,
levels = c("Poor", "Acceptable", "Good")),
## make NAs in to missing
evaluation = fct_explicit_na(evaluation, na_level = "Missing")
)
## define datframe
tibble("Country" = countrycode(country_iso, origin = "iso3c", destination = "country.name"),
"Date of report" = format(this_week, format = "%d %B %Y"),
"Epiweek" = num_week) %>%
mutate(across(.fns = as.character)) %>%
pivot_longer(everything(),
names_to = "variable",
values_to = "values") %>%
## show as a flextable with appropriate column width
flextable() %>%
autofit()
Please note that this is an INTERNAL document. The AFRO office is requesting your
honest feedback to better understand the response capacity across the region.
Additionally, the corresponding Excel tool describes the frequency of reporting
for each indicator. Data is entered into the “Data Fields” tab, and the indicators
in the first tab are automatically calculated.
The indicators flagged as Weekly should be included in each submission. Indicators
flagged as Bi-weekly should be included in the submissions the first and third
weeks of each month. Finally, indicators flagged as Monthly should be included
in the first report submitted for each month, providing data and details on for
the previous month.
Section I: Situation Overview
In this section, please provide a description of the current situation.
Section II: Critical Analysis of the Response
In this section, please provide a critical analysis of ongoing response actions
by pillar. Please describe areas of progress, (indicators that are in the target
range or moving towards the target range), areas of weakness (indicators that are
outside the target ranges), and explanations for the demonstrated progress or weakness.
Additionally, please discuss the overall capacity within each response pillar to
manage and mitigate the pandemic.
Coordination & incidence management (Indicators 1-5)
- Areas of progress
- Areas of weaknesses
- Others (see instructions)
## create table for indicators of interest
mne_data %>%
## only keep indicators interested in
filter(grps == "Coordination & Incident Management" &
grepl("indicator_", indicator)) %>%
## choose variables want
select(epi_week, num_vars, labels) %>%
## spread in to wide format
pivot_wider(names_from = epi_week,
values_from = num_vars) %>%
## show as a flextable with appropriate column width
flextable() %>%
width(j = ~labels, width = 2.5)
Control at points of entry (Indicators 6-8)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest
indicator_counts <- mne_data %>%
## only keep indicators interested in
filter(grps == "Control at point of entries" &
grepl("indicator_", indicator)) %>%
## choose variables want
select(epi_week, num_vars, labels, evaluation)
## plot data as tiles coloured by evaluation
ggplot(indicator_counts,
aes(x = epi_week,
y = reorder(
sub("\\:.*", "", x = labels),
desc(labels)),
fill = evaluation)) +
geom_tile(colour = "black") +
geom_text(aes(label = num_vars), size = 11) +
scale_fill_manual(values = c(
"Good" = "#91CF60",
"Acceptable" = "#FFFFBF",
"Poor" = "#FC8D59",
"Missing" = "Grey90"
)) +
labs(y = "", x = "Calendar week") +
epi_theme
## to show as a table rather than a plot(uncomment below)
# indicator_counts %>%
# ## drop evaluation column
# select(-evaluation) %>%
# ## spread in to wide format
# pivot_wider(names_from = epi_week,
# values_from = num_vars) %>%
# flextable()
Surveillance & control of transmission (Indicators 9-11)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest
indicator_counts <- mne_data %>%
## only keep indicators interested in
filter(grps == "Surveillance & control of transmission" &
grepl("indicator_", indicator)) %>%
## choose variables want
select(epi_week, num_vars, labels, evaluation)
## plot data as tiles coloured by evaluation
ggplot(indicator_counts,
aes(x = epi_week,
y = sub("\\:.*", "", x = labels),
fill = evaluation)) +
geom_tile(colour = "black") +
geom_text(aes(label = num_vars), size = 11) +
scale_fill_manual(values = c(
"Good" = "#91CF60",
"Acceptable" = "#FFFFBF",
"Poor" = "#FC8D59",
"Missing" = "Grey90"
)) +
labs(y = "", x = "Calendar week") +
epi_theme
## to show as a table rather than a plot(uncomment below)
# indicator_counts %>%
# ## drop evaluation column
# select(-evaluation) %>%
# ## spread in to wide format
# pivot_wider(names_from = epi_week,
# values_from = num_vars) %>%
# flextable()
Laboratory capacity (Indicators 12-14)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest
indicator_counts <- mne_data %>%
## only keep indicators interested in
filter(
grepl(
paste0("indicator_", 12:14, collapse = "|"),
indicator)) %>%
## choose variables want
select(epi_week, num_vars, labels, evaluation)
## plot data as tiles coloured by evaluation
ggplot(indicator_counts,
aes(x = epi_week,
y = reorder(
sub("\\:.*", "", x = labels),
desc(labels)),
fill = evaluation)) +
geom_tile(colour = "black") +
geom_text(aes(label = num_vars), size = 11) +
scale_fill_manual(values = c(
"Good" = "#91CF60",
"Acceptable" = "#FFFFBF",
"Poor" = "#FC8D59",
"Missing" = "Grey90"
)) +
labs(y = "", x = "Calendar week") +
epi_theme
## to show as a table rather than a plot(uncomment below)
# indicator_counts %>%
# ## drop evaluation column
# select(-evaluation) %>%
# ## spread in to wide format
# pivot_wider(names_from = epi_week,
# values_from = num_vars) %>%
# flextable()
Case management & IPC (Indicators 15-25)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest
indicator_counts <- mne_data %>%
## only keep indicators interested in
filter(grps == "Case Management and IPC" &
grepl("indicator_", indicator)) %>%
## choose variables want
select(epi_week, num_vars, labels, evaluation)
## plot data as tiles coloured by evaluation
ggplot(indicator_counts,
aes(x = epi_week,
y = reorder(
sub("\\:.*", "", x = labels),
desc(labels)),
fill = evaluation)) +
geom_tile(colour = "black") +
geom_text(aes(label = num_vars), size = 11) +
scale_fill_manual(values = c(
"Good" = "#91CF60",
"Acceptable" = "#FFFFBF",
"Poor" = "#FC8D59",
"Missing" = "Grey90"
)) +
labs(y = "", x = "Calendar week") +
epi_theme
## to show as a table rather than a plot(uncomment below)
# indicator_counts %>%
# ## drop evaluation column
# select(-evaluation) %>%
# ## spread in to wide format
# pivot_wider(names_from = epi_week,
# values_from = num_vars) %>%
# flextable()
Risk communication and community engagement (Indicator 26)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
- Social distancing, mask use and handwashing compliance
## create table for indicators of interest
indicator_counts <- mne_data %>%
## only keep indicators interested in
filter(grps == "Risk communication and community engagement" &
grepl("indicator_", indicator)) %>%
## choose variables want
select(epi_week, num_vars, labels, evaluation)
## plot data as tiles coloured by evaluation
ggplot(indicator_counts,
aes(x = epi_week,
y = reorder(
sub("\\:.*", "", x = labels),
desc(labels)),
fill = evaluation)) +
geom_tile(colour = "black") +
geom_text(aes(label = num_vars), size = 11) +
scale_fill_manual(values = c(
"Good" = "#91CF60",
"Acceptable" = "#FFFFBF",
"Poor" = "#FC8D59",
"Missing" = "Grey90"
)) +
labs(y = "", x = "Calendar week") +
epi_theme
## to show as a table rather than a plot(uncomment below)
# indicator_counts %>%
# ## drop evaluation column
# select(-evaluation) %>%
# ## spread in to wide format
# pivot_wider(names_from = epi_week,
# values_from = num_vars) %>%
# flextable()
Safe essential service delivery (Indicators 27-30)
Please note that these are a subset of indicators used to assess safe delivery
of essential services. The complete list of indicators for this area can be
found here. If you have noticed changes
in any of the areas not captured by the indicators listed in the Excel tool,
please describe them here.
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest
indicator_counts <- mne_data %>%
## only keep indicators interested in
filter(grps == "Safe essential service delivery" &
grepl("indicator_", indicator)) %>%
## choose variables want
select(epi_week, num_vars, labels, evaluation)
## plot data as tiles coloured by evaluation
ggplot(indicator_counts,
aes(x = epi_week,
y = reorder(
sub("\\:.*", "", x = labels),
desc(labels)),
fill = evaluation)) +
geom_tile(colour = "black") +
geom_text(aes(label = num_vars), size = 11) +
scale_fill_manual(values = c(
"Good" = "#91CF60",
"Acceptable" = "#FFFFBF",
"Poor" = "#FC8D59",
"Missing" = "Grey90"
)) +
labs(y = "", x = "Calendar week") +
epi_theme
## to show as a table rather than a plot(uncomment below)
# indicator_counts %>%
# ## drop evaluation column
# select(-evaluation) %>%
# ## spread in to wide format
# pivot_wider(names_from = epi_week,
# values_from = num_vars) %>%
# flextable()
Procurement of critical supplies (Indicator 31)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest
indicator_counts <- mne_data %>%
## only keep indicators interested in
filter(grps == "Procurement of Critical Supplies" &
grepl("indicator_", indicator)) %>%
## choose variables want
select(epi_week, num_vars, labels, evaluation)
## plot data as tiles coloured by evaluation
ggplot(indicator_counts,
aes(x = epi_week,
y = reorder(
sub("\\:.*", "", x = labels),
desc(labels)),
fill = evaluation)) +
geom_tile(colour = "black") +
geom_text(aes(label = num_vars), size = 11) +
scale_fill_manual(values = c(
"Good" = "#91CF60",
"Acceptable" = "#FFFFBF",
"Poor" = "#FC8D59",
"Missing" = "Grey90"
)) +
labs(y = "", x = "Calendar week") +
epi_theme
## to show as a table rather than a plot(uncomment below)
# indicator_counts %>%
# ## drop evaluation column
# select(-evaluation) %>%
# ## spread in to wide format
# pivot_wider(names_from = epi_week,
# values_from = num_vars) %>%
# flextable()
Additionally, please discuss how the specific response efforts are guided by the
epidemiologic information collected through the disease surveillance system.
Section III: Areas of concern
In this section, please discuss and elaborate on any areas of concern. For example,
please consider discussing the social, cultural, economic and political implications
and impacts of the outbreak and how this affects the public health response.
Section IV: Best practices
In this section, please discuss any best practices that have been noted in any of
the response pillars or in response coordination.
Section V: Situation interpretation and next steps
Please provide your honest, professional perspective on the response efforts and
partnerships. Additionally, please describe the immediate priorities and proposed
next steps for the response efforts.
R4IDSR/covidmonitor documentation built on March 29, 2021, 12:05 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# 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.width = 20, # define figure width fig.height = 8, # 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( "covidmonitor", # for WHO AFRO data functions "knitr", # create output docs "here", # find your files "rio", # read in data "lubridate", # work with dates "tsibble", # for working with dates as time series "dplyr", # clean/shape data "ggplot2", # create plots "countrycode", # recode country ISOs to full names "tidyr", # clean/shape data "flextable", # nicely formatted output tables "matchmaker", # dictionary-based standardization of variables "forcats" # handle factors ) 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) } ## define a theme for ggplot (so all look similar) epi_theme <- theme_classic() + theme( text = element_text(size = 45), ## rotate x axis labels axis.text.x = element_text(angle = 45, vjust = 0.5), axis.title = element_text(color = "black", face = "bold"), legend.title = element_blank(), legend.text = element_text(color = "black"), legend.position = "bottom", legend.direction = "horizontal", ## colour and size the grid lines in the plot # panel.grid.minor = element_line(colour = "grey90", size = 0.5), panel.grid.major = element_line(colour = "grey90"), ## remove axis lines axis.line = element_blank() )
## current week this_week <- as.Date("2020-12-25") ## week number of current week num_week <- yearweek(this_week) ## three weeks ago (so can show progress over last month) weeks_of_interest <- seq(num_week - 3, num_week, by = 1) ## country of interest iso code country_iso <- "STP"
## define path to folder with raw data data_folder <- here::here("data", "raw") ## define path to folder with output output_folder <- here::here("data", "clean") ## read in the dataset of interest in long format mne_data <- merge_kpi(data_folder, country_iso, output_folder, outputname = paste0("kpi_merged_", country_iso, "_", this_week), wide = FALSE) ## read in the m&e dictionary mne_dict <- rio::import( system.file("extdata", "mne_dictionary.xlsx", package = "covidmonitor"), which = "var_defs" )
## fix the epi week variable mne_data$epi_week <- yearweek( paste0( year(mne_data$date), " W", mne_data$epi_week) ) ## only keep week of interest (and 3 before) mne_data <- mne_data %>% filter(epi_week %in% weeks_of_interest) ## temporarily make epi_week a factor mne_data <- mne_data %>% mutate( epi_week = factor(as.character(epi_week), levels = as.character(weeks_of_interest))) ## add in missing weeks as NAs mne_data <- mne_data %>% complete(epi_week, indicator) ## make epi_week a yearweek again mne_data <- mne_data %>% mutate(epi_week = yearweek( as.character( epi_week))) ## add in the grouping variable from dictionary mne_data <- mne_data %>% mutate(grps = match_vec(indicator, dictionary = mne_dict, from = "var_name", "grp_type")) ## add in the english labels for variables mne_data <- mne_data %>% mutate(labels = match_vec(indicator, dictionary = mne_dict, from = "var_name", "english_label")) ## remove the word "performance" from the labels column (otherwise very long) mne_data <- mne_data %>% mutate(labels = gsub("Performance", "", labels)) ## round numerics to 1 digit mne_data <- mne_data %>% mutate(num_vars = round(num_vars, digits = 1)) ## make evaluation a factor so all levels represented mne_data <- mne_data %>% mutate(evaluation = factor(evaluation, levels = c("Poor", "Acceptable", "Good")), ## make NAs in to missing evaluation = fct_explicit_na(evaluation, na_level = "Missing") )
## define datframe tibble("Country" = countrycode(country_iso, origin = "iso3c", destination = "country.name"), "Date of report" = format(this_week, format = "%d %B %Y"), "Epiweek" = num_week) %>% mutate(across(.fns = as.character)) %>% pivot_longer(everything(), names_to = "variable", values_to = "values") %>% ## show as a flextable with appropriate column width flextable() %>% autofit()
Please note that this is an INTERNAL document. The AFRO office is requesting your honest feedback to better understand the response capacity across the region.
Additionally, the corresponding Excel tool describes the frequency of reporting for each indicator. Data is entered into the “Data Fields” tab, and the indicators in the first tab are automatically calculated.
The indicators flagged as Weekly should be included in each submission. Indicators flagged as Bi-weekly should be included in the submissions the first and third weeks of each month. Finally, indicators flagged as Monthly should be included in the first report submitted for each month, providing data and details on for the previous month.
Section I: Situation Overview
In this section, please provide a description of the current situation.
Section II: Critical Analysis of the Response
In this section, please provide a critical analysis of ongoing response actions by pillar. Please describe areas of progress, (indicators that are in the target range or moving towards the target range), areas of weakness (indicators that are outside the target ranges), and explanations for the demonstrated progress or weakness. Additionally, please discuss the overall capacity within each response pillar to manage and mitigate the pandemic.
Coordination & incidence management (Indicators 1-5)
- Areas of progress
- Areas of weaknesses
- Others (see instructions)
## create table for indicators of interest mne_data %>% ## only keep indicators interested in filter(grps == "Coordination & Incident Management" & grepl("indicator_", indicator)) %>% ## choose variables want select(epi_week, num_vars, labels) %>% ## spread in to wide format pivot_wider(names_from = epi_week, values_from = num_vars) %>% ## show as a flextable with appropriate column width flextable() %>% width(j = ~labels, width = 2.5)
Control at points of entry (Indicators 6-8)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest indicator_counts <- mne_data %>% ## only keep indicators interested in filter(grps == "Control at point of entries" & grepl("indicator_", indicator)) %>% ## choose variables want select(epi_week, num_vars, labels, evaluation) ## plot data as tiles coloured by evaluation ggplot(indicator_counts, aes(x = epi_week, y = reorder( sub("\\:.*", "", x = labels), desc(labels)), fill = evaluation)) + geom_tile(colour = "black") + geom_text(aes(label = num_vars), size = 11) + scale_fill_manual(values = c( "Good" = "#91CF60", "Acceptable" = "#FFFFBF", "Poor" = "#FC8D59", "Missing" = "Grey90" )) + labs(y = "", x = "Calendar week") + epi_theme ## to show as a table rather than a plot(uncomment below) # indicator_counts %>% # ## drop evaluation column # select(-evaluation) %>% # ## spread in to wide format # pivot_wider(names_from = epi_week, # values_from = num_vars) %>% # flextable()
Surveillance & control of transmission (Indicators 9-11)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest indicator_counts <- mne_data %>% ## only keep indicators interested in filter(grps == "Surveillance & control of transmission" & grepl("indicator_", indicator)) %>% ## choose variables want select(epi_week, num_vars, labels, evaluation) ## plot data as tiles coloured by evaluation ggplot(indicator_counts, aes(x = epi_week, y = sub("\\:.*", "", x = labels), fill = evaluation)) + geom_tile(colour = "black") + geom_text(aes(label = num_vars), size = 11) + scale_fill_manual(values = c( "Good" = "#91CF60", "Acceptable" = "#FFFFBF", "Poor" = "#FC8D59", "Missing" = "Grey90" )) + labs(y = "", x = "Calendar week") + epi_theme ## to show as a table rather than a plot(uncomment below) # indicator_counts %>% # ## drop evaluation column # select(-evaluation) %>% # ## spread in to wide format # pivot_wider(names_from = epi_week, # values_from = num_vars) %>% # flextable()
Laboratory capacity (Indicators 12-14)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest indicator_counts <- mne_data %>% ## only keep indicators interested in filter( grepl( paste0("indicator_", 12:14, collapse = "|"), indicator)) %>% ## choose variables want select(epi_week, num_vars, labels, evaluation) ## plot data as tiles coloured by evaluation ggplot(indicator_counts, aes(x = epi_week, y = reorder( sub("\\:.*", "", x = labels), desc(labels)), fill = evaluation)) + geom_tile(colour = "black") + geom_text(aes(label = num_vars), size = 11) + scale_fill_manual(values = c( "Good" = "#91CF60", "Acceptable" = "#FFFFBF", "Poor" = "#FC8D59", "Missing" = "Grey90" )) + labs(y = "", x = "Calendar week") + epi_theme ## to show as a table rather than a plot(uncomment below) # indicator_counts %>% # ## drop evaluation column # select(-evaluation) %>% # ## spread in to wide format # pivot_wider(names_from = epi_week, # values_from = num_vars) %>% # flextable()
Case management & IPC (Indicators 15-25)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest indicator_counts <- mne_data %>% ## only keep indicators interested in filter(grps == "Case Management and IPC" & grepl("indicator_", indicator)) %>% ## choose variables want select(epi_week, num_vars, labels, evaluation) ## plot data as tiles coloured by evaluation ggplot(indicator_counts, aes(x = epi_week, y = reorder( sub("\\:.*", "", x = labels), desc(labels)), fill = evaluation)) + geom_tile(colour = "black") + geom_text(aes(label = num_vars), size = 11) + scale_fill_manual(values = c( "Good" = "#91CF60", "Acceptable" = "#FFFFBF", "Poor" = "#FC8D59", "Missing" = "Grey90" )) + labs(y = "", x = "Calendar week") + epi_theme ## to show as a table rather than a plot(uncomment below) # indicator_counts %>% # ## drop evaluation column # select(-evaluation) %>% # ## spread in to wide format # pivot_wider(names_from = epi_week, # values_from = num_vars) %>% # flextable()
Risk communication and community engagement (Indicator 26)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
- Social distancing, mask use and handwashing compliance
## create table for indicators of interest indicator_counts <- mne_data %>% ## only keep indicators interested in filter(grps == "Risk communication and community engagement" & grepl("indicator_", indicator)) %>% ## choose variables want select(epi_week, num_vars, labels, evaluation) ## plot data as tiles coloured by evaluation ggplot(indicator_counts, aes(x = epi_week, y = reorder( sub("\\:.*", "", x = labels), desc(labels)), fill = evaluation)) + geom_tile(colour = "black") + geom_text(aes(label = num_vars), size = 11) + scale_fill_manual(values = c( "Good" = "#91CF60", "Acceptable" = "#FFFFBF", "Poor" = "#FC8D59", "Missing" = "Grey90" )) + labs(y = "", x = "Calendar week") + epi_theme ## to show as a table rather than a plot(uncomment below) # indicator_counts %>% # ## drop evaluation column # select(-evaluation) %>% # ## spread in to wide format # pivot_wider(names_from = epi_week, # values_from = num_vars) %>% # flextable()
Safe essential service delivery (Indicators 27-30)
Please note that these are a subset of indicators used to assess safe delivery of essential services. The complete list of indicators for this area can be found here. If you have noticed changes in any of the areas not captured by the indicators listed in the Excel tool, please describe them here.
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest indicator_counts <- mne_data %>% ## only keep indicators interested in filter(grps == "Safe essential service delivery" & grepl("indicator_", indicator)) %>% ## choose variables want select(epi_week, num_vars, labels, evaluation) ## plot data as tiles coloured by evaluation ggplot(indicator_counts, aes(x = epi_week, y = reorder( sub("\\:.*", "", x = labels), desc(labels)), fill = evaluation)) + geom_tile(colour = "black") + geom_text(aes(label = num_vars), size = 11) + scale_fill_manual(values = c( "Good" = "#91CF60", "Acceptable" = "#FFFFBF", "Poor" = "#FC8D59", "Missing" = "Grey90" )) + labs(y = "", x = "Calendar week") + epi_theme ## to show as a table rather than a plot(uncomment below) # indicator_counts %>% # ## drop evaluation column # select(-evaluation) %>% # ## spread in to wide format # pivot_wider(names_from = epi_week, # values_from = num_vars) %>% # flextable()
Procurement of critical supplies (Indicator 31)
- Drivers of progress
- Drivers of weaknesses
- Others (see instructions)
## create table for indicators of interest indicator_counts <- mne_data %>% ## only keep indicators interested in filter(grps == "Procurement of Critical Supplies" & grepl("indicator_", indicator)) %>% ## choose variables want select(epi_week, num_vars, labels, evaluation) ## plot data as tiles coloured by evaluation ggplot(indicator_counts, aes(x = epi_week, y = reorder( sub("\\:.*", "", x = labels), desc(labels)), fill = evaluation)) + geom_tile(colour = "black") + geom_text(aes(label = num_vars), size = 11) + scale_fill_manual(values = c( "Good" = "#91CF60", "Acceptable" = "#FFFFBF", "Poor" = "#FC8D59", "Missing" = "Grey90" )) + labs(y = "", x = "Calendar week") + epi_theme ## to show as a table rather than a plot(uncomment below) # indicator_counts %>% # ## drop evaluation column # select(-evaluation) %>% # ## spread in to wide format # pivot_wider(names_from = epi_week, # values_from = num_vars) %>% # flextable()
Additionally, please discuss how the specific response efforts are guided by the epidemiologic information collected through the disease surveillance system.
Section III: Areas of concern
In this section, please discuss and elaborate on any areas of concern. For example, please consider discussing the social, cultural, economic and political implications and impacts of the outbreak and how this affects the public health response.
Section IV: Best practices
In this section, please discuss any best practices that have been noted in any of the response pillars or in response coordination.
Section V: Situation interpretation and next steps
Please provide your honest, professional perspective on the response efforts and partnerships. Additionally, please describe the immediate priorities and proposed next steps for the response efforts.
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