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.
classify_trend( df, t = date, data_count = dataCount, all_count = allCount, B = 12 )
| 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) |
A data frame. The first B rows within each group will be missing.
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
# 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) { # 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" ) }