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Surveillance indicator table

generate_surv_ind_tab.Rd

Generates the surveillance indicator table for each year. Outputs the number of AFP cases, national NPAFP rate and stool adequacy,percentage of population living in districts with greater than or equal to 100,000 U15 meeting both indicators.

Usage

generate_surv_ind_tab(
  ctry.data,
  ctry.extract,
  dist.extract,
  cstool,
  dstool,
  afp.case,
  country_name = Sys.getenv("DR_COUNTRY")
)

Arguments

ctry.data

list Large list containing polio data of a country.

ctry.extract

tibble Country NPAFP rate. Output of f.npafp.rate.01() calculated at the country level.

dist.extract

tibble District NPAFP rate. Output of f.npafp.rate.01() calculated at the district level.

cstool

tibble Country stool adequacy. Output of f.stool.ad.01() calculated at the country level.

dstool

tibble District stool adequacy. Output of f.stool.ad.01() calculated at the district level.

afp.case

tibble AFP case counts. Output of generate_afp_by_month_summary() with by="year".

country_name

str Name of the country.

Value

flextable Table summarizing yearly trends in NPAFP and stool adequacy at the national level.

Examples

if (FALSE) { # \dontrun{
ctry.data <- init_dr("algeria")
ctry.extract <- f.npafp.rate.01(
  afp.data = ctry.data$afp.all.2,
  pop.data = ctry.data$ctry.pop,
  start.date = start_date,
  end.date = end_date,
  spatial.scale = "ctry",
  pending = T,
  rolling = F,
  sp_continuity_validation = F
)
dist.extract <- f.npafp.rate.01(
  afp.data = ctry.data$afp.all.2,
  pop.data = ctry.data$ctry.pop,
  start.date = start_date,
  end.date = end_date,
  spatial.scale = "dist",
  pending = T,
  rolling = F,
  sp_continuity_validation = F
)
cstool <- f.stool.ad.01(
  afp.data = ctry.data$afp.all.2,
  admin.data = ctry.data$ctry.pop,
  start.date = start_date,
  end.date = end_date,
  spatial.scale = "ctry",
  missing = "good",
  bad.data = "inadequate",
  rolling = F,
  sp_continuity_validation = F
)
dstool <- f.stool.ad.01(
  afp.data = ctry.data$afp.all.2,
  admin.data = ctry.data$dist.pop,
  start.date = start_date,
  end.date = end_date,
  spatial.scale = "dist",
  missing = "good",
  bad.data = "inadequate",
  rolling = F,
  sp_continuity_validation = F
)
afp.by.month <- generate_afp_by_month(ctry.data$afp.all.2, "2021-01-01", "2023-12-31")
afp.case <- generate_afp_by_month_summary(afp.by.month, ctry.data, start_date, end_date, "year")
generate_surv_ind_tab(ctry.data, ctry.extract, dist.extract, cstool, dstool, afp.case)
} # }