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Trend Classification for Proportions/Percentages

Source: R/classify_trend.R
classify_trend.Rd

The algorithm fits rolling binomial models to a daily time series of percentages or proportions in order to classify the overall trend during the baseline period as significantly increasing, significantly decreasing, or stable.

Usage

classify_trend(
  df,
  t = date,
  data_count = dataCount,
  all_count = allCount,
  B = 12
)

Arguments

df

A data frame, data frame extension (e.g. a tibble), or a lazy data frame.

t

Name of the column of type Date containing the dates

data_count

Name of the column with counts for positive encounters

all_count

Name of the column with total counts of encounters

B

Baseline parameter. The baseline length is the number of days to which each binomial model is fit (default is 12)

Value

A data frame. The first B rows within each group will be missing.

Details

The test statistic and p-value are extracted from each individual model and are used in the following classification scheme:

  • p-value < 0.01 and sign(test_statistic) > 0 ~ "Significant Increase"

  • p-value < 0.01 and sign(test_statistic) < 0 ~ "Significant Decrease"

  • p-value >= 0.01 ~ "Stable"

If there are fewer than 10 encounters/counts in the baseline period, a model is not fit and a value of NA is returned for the test statistic and p-value

Examples

# Example 1
df <- data.frame(
  date = seq.Date(as.Date("2020-01-01"), as.Date("2020-12-31"), by = 1),
  dataCount = floor(runif(366, min = 0, max = 101)),
  allCount = floor(runif(366, min = 101, max = 500))
)

df_trend <- classify_trend(df)
head(df_trend)
if (FALSE) { # \dontrun{
# Example 2 with Data from NSSP-ESSENCE
library(ggplot2)
library(ggthemes)

myProfile <- Credentials$new(askme("Enter your username:"), askme())

url <- "https://essence2.syndromicsurveillance.org/nssp_essence/api/timeSeries?
endDate=20Nov20&percentParam=ccddCategory&datasource=va_hosp&startDate=22Aug20
&medicalGroupingSystem=essencesyndromes&userId=2362&aqtTarget=TimeSeries
&ccddCategory=cli%20cc%20with%20cli%20dd%20and%20coronavirus%20dd%20v2
&geographySystem=hospitalstate&detector=probregv2&timeResolution=daily&hasBeenE=1
&stratVal=&multiStratVal=geography&graphOnly=true&numSeries=0&graphOptions=multipleSmall
&seriesPerYear=false&nonZeroComposite=false&removeZeroSeries=true&sigDigits=true
&startMonth=January&stratVal=&multiStratVal=geography&graphOnly=true&numSeries=0
&graphOptions=multipleSmall&seriesPerYear=false&startMonth=January&nonZeroComposite=false"

url <- url %>% gsub("\n", "", .)

api_data <- myProfile$get_api_data(url)

df <- api_data$timeSeriesData

data_trend <- classify_trend(df, data_count = dataCount, all_count = allCount)

# Visualize Montana State trend
pal <- c("#FF0000", "#1D8AFF", "#FFF70E", "grey90")

data_trend %>%
  mutate(percent = data_count / all_count * 100) %>%
  filter(title == "Montana") %>%
  ggplot(., aes(x = t, y = percent)) +
  geom_line(color = pal[2], alpha = 0.5) +
  geom_hline(yintercept = -0.4, size = 4.5, color = "white") +
  geom_segment(aes(x = t, xend = max(t), y = -0.4, yend = -0.4, color = trend_classification), size = 3) +
  scale_color_manual(values = pal, name = "Trend Classification") +
  theme_few() +
  labs(
    title = "Percent of Emergency Department Visits with Diagnosed COVID-19",
    subtitle = "November 1st, 2020 to February 27th, 2020",
    x = "Date",
    y = "Percent"
  )
} # }