Package {AMRsurveilR}

Generate AMR surveillance alerts

Description

Combines EWMA and CUSUM surveillance signals.

Usage

amr_alert(x, lambda = 0.2, L = 3, h = 5)

Arguments

Value

A data frame containing EWMA, CUSUM and combined alerts.

Detect AMR changes using CUSUM

Description

Calculates a one-sided cumulative sum for increases in resistance.

Usage

amr_cusum(x, target = mean(x), k = 0, h = 5)

Arguments

Value

A data frame containing the cumulative sum and alert status.

Detect antimicrobial resistance changes using EWMA

Description

Applies an exponentially weighted moving average to a time series of resistance proportions.

Usage

amr_ewma(x, lambda = 0.2, L = 3)

Arguments

Value

A data frame containing the observed values, EWMA statistic, and upper/lower control limits.

Examples

x <- c(
  0.20, 0.21, 0.19, 0.22,
  0.23, 0.25, 0.40, 0.45
)

amr_ewma(x)

Example antimicrobial resistance surveillance dataset

Description

A simulated dataset containing antimicrobial susceptibility results, sampling dates, and bacterial species for demonstrating functions in AMRsurveilR.

Usage

amr_example

Format

A data frame with 200 rows and 8 columns:

isolate_id

Unique isolate identifier.

date

Date of isolate collection.

organism

Bacterial species.

AMP

Ampicillin susceptibility result.

TET

Tetracycline susceptibility result.

CIP

Ciprofloxacin susceptibility result.

GEN

Gentamicin susceptibility result.

CTX

Cefotaxime susceptibility result.

Details

The dataset is simulated and is provided solely for examples, testing, and demonstration. Susceptibility results are coded as "R" for resistant and "S" for susceptible.

Source

Simulated data generated for the AMRsurveilR package.

Detect spatial hotspots of antimicrobial resistance

Description

Identifies statistically significant spatial clusters of antimicrobial resistance using Local Moran's I (LISA).

Usage

amr_hotspot(
  data,
  geometry,
  variable,
  queen = TRUE,
  p_value = 0.05,
  zero.policy = TRUE
)

Arguments

Details

The function classifies observations into:

• High-High: high AMR surrounded by high AMR

• Low-Low: low AMR surrounded by low AMR

• High-Low: high AMR surrounded by low AMR

• Low-High: low AMR surrounded by high AMR

• Not significant

The function uses spatial neighbours generated from the supplied sf geometry and calculates Local Moran's I using spdep::localmoran(). The AMR variable is standardized before calculating the spatial clusters.

Value

A data frame containing the original AMR values, Local Moran's I, expected value, variance, Z-score, p-value, and cluster classification.

Examples

library(sf)

# Create example AMR data
amr_data <- data.frame(
  amr_prevalence = c(
    0.10, 0.20, 0.80,
    0.15, 0.75, 0.85,
    0.20, 0.70, 0.90
  )
)

# Create a 3 x 3 grid of neighbouring polygons
polygons <- lapply(0:8, function(i) {
  x <- i %% 3
  y <- i %/% 3

  st_polygon(list(matrix(
    c(
      x, y,
      x + 1, y,
      x + 1, y + 1,
      x, y + 1,
      x, y
    ),
    ncol = 2,
    byrow = TRUE
  )))
})

district_sf <- st_sf(
  geometry = st_sfc(polygons)
)

hotspots <- amr_hotspot(
  data = amr_data,
  geometry = district_sf,
  variable = "amr_prevalence"
)

head(hotspots)

Fit logistic regression for AMR risk factors

Description

Fits a binomial logistic regression model.

Usage

amr_logistic(data, formula)

Arguments

Value

A fitted glm object.

Examples

dat <- data.frame(
  MDR = c(0, 1, 0, 1, 1, 0),
  antibiotic_use = c(0, 1, 0, 1, 1, 0),
  farm_size = c(10, 20, 12, 30, 25, 15)
)

amr_logistic(
  dat,
  MDR ~ antibiotic_use + farm_size
)

Identify multidrug-resistant isolates

Description

Identifies isolates resistant to at least a specified number of antimicrobial agents.

Usage

amr_mdr(
  data,
  antibiotic_columns,
  isolate_id = NULL,
  resistant_value = "R",
  min_classes = 3
)

Arguments

Value

A data frame containing the number of resistant antimicrobials and MDR classification.

Examples

dat <- data.frame(
  isolate = 1:3,
  AMP = c("R", "S", "R"),
  TET = c("R", "S", "S"),
  CIP = c("S", "S", "R")
)

amr_mdr(
  dat,
  c("AMP", "TET", "CIP"),
  isolate_id = "isolate"
)

Calculate antimicrobial resistance prevalence

Description

Calculates the proportion of resistant isolates and its binomial confidence interval.

Usage

amr_prevalence(resistant, total, conf.level = 0.95, method = "exact")

Arguments

Value

A data frame containing resistant isolates, total isolates, prevalence, lower and upper confidence limits.

Examples

amr_prevalence(42, 100)

Calculate antimicrobial resistance profile

Description

Calculates resistance prevalence for multiple antimicrobial agents.

Usage

amr_resistance_profile(data, antibiotic_columns, resistant_value = "R")

Arguments

Value

A data frame containing the number tested, resistant isolates, and resistance percentage for each antimicrobial.

Examples

data <- data.frame(
  AMP = c("R", "S", "R", "S"),
  TET = c("S", "R", "R", "S")
)

amr_resistance_profile(
  data,
  c("AMP", "TET")
)

Calculate temporal antimicrobial resistance trends

Description

Aggregates antimicrobial resistance observations by a specified time period and calculates the proportion of resistant isolates.

Usage

amr_trend(data, date, outcome, resistant_value = "R", period = "month")

Arguments

Value

A data frame containing the time period, number tested, number resistant, and resistance percentage.

Examples

data(amr_example)

trend <- amr_trend(
  data = amr_example,
  date = date,
  outcome = AMP
)

head(trend)

Identify extensively drug-resistant isolates

Description

A simple threshold-based implementation. The definition should be adapted to the organism and antimicrobial-class framework used in the study.

Usage

amr_xdr(data, antibiotic_columns, resistant_value = "R", min_resistant = 5)

Arguments

Value

A data frame with resistant counts and XDR classification.

Plot antimicrobial resistance trend

Description

Produces a temporal antimicrobial resistance trend plot.

Usage

plot_amr_trend(x, title = "Antimicrobial Resistance Trend")

Arguments

Value

A ggplot object.

Examples

data(amr_example)

trend <- amr_trend(
  data = amr_example,
  date = date,
  outcome = AMP
)

plot_amr_trend(trend)