Title: Alerts from Public Health Surveillance Data
Version: 2024.6.24
Description: Helps create alerts and determine trends by using various methods to analyze public health surveillance data. The primary analysis method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>.
Depends: R (≥ 3.3.0)
License: MIT + file LICENSE
URL: https://www.csids.no/csalert/, https://github.com/csids/csalert
BugReports: https://github.com/csids/csalert/issues
Encoding: UTF-8
Imports: data.table, magrittr, ggplot2, glm2, cstidy, cstime, lubridate, stringr, surveillance
Suggests: testthat, knitr, rmarkdown, rstudioapi, glue, covidnor, csdata, csmaps, ggrepel, plnr
RoxygenNote: 7.2.3
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-06-24 11:40:34 UTC; raw996
Author: Beatriz Valcarcel Salamanca [aut], Chi Zhang ORCID iD [aut], Richard Aubrey White ORCID iD [aut, cre], CSIDS [cph]
Maintainer: Richard Aubrey White <hello@rwhite.no>
Repository: CRAN
Date/Publication: 2024-06-24 12:20:12 UTC

Holiday effect —-

Description

The effect of public holiday on a time series of daily counts

Usage

add_holiday_effect(data, holiday_data, holiday_effect = 2)

Arguments

data

A csfmt_rds data object

holiday_data

dates

holiday_effect

Ending date of the simulation period.

Value

A csfmt_rts_data_v1, data.table containing

Prediction thresholds

Description

Prediction thresholds

Usage

prediction_interval(object, newdata, alpha = 0.05, z = NULL, ...)

Arguments

object

Object

newdata

New data

alpha

Two-sided alpha (e.g 0.05)

z

Similar to alpha (e.g. z=1.96 is the same as alpha=0.05)

...

dots

Prediction thresholds

Description

Prediction thresholds

Usage

## S3 method for class 'glm'
prediction_interval(
  object,
  newdata,
  alpha = 0.05,
  z = NULL,
  skewness_transform = "none",
  ...
)

Arguments

object

Object

newdata

New data

alpha

Two-sided alpha (e.g 0.05)

z

Similar to alpha (e.g. z=1.96 is the same as alpha=0.05)

skewness_transform

"none", "1/2", "2/3"

...

dots

Determine the short term trend of a timeseries

Description

The method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>.

Usage

short_term_trend(x, ...)

## S3 method for class 'csfmt_rts_data_v1'
short_term_trend(
  x,
  numerator,
  denominator = NULL,
  prX = 100,
  trend_isoyearweeks = 6,
  remove_last_isoyearweeks = 0,
  forecast_isoyearweeks = trend_isoyearweeks,
  numerator_naming_prefix = "from_numerator",
  denominator_naming_prefix = "from_denominator",
  statistics_naming_prefix = "universal",
  remove_training_data = FALSE,
  include_decreasing = FALSE,
  alpha = 0.05,
  ...
)

Arguments

x

Data object

...

Not in use.

numerator

Character of name of numerator

denominator

Character of name of denominator (optional)

prX

If using denominator, what scaling factor should be used for numerator/denominator?

trend_isoyearweeks

Same as trend_dates, but used if granularity_geo=='isoyearweek'

remove_last_isoyearweeks

Same as remove_last_dates, but used if granularity_geo=='isoyearweek'

forecast_isoyearweeks

Same as forecast_dates, but used if granularity_geo=='isoyearweek'

numerator_naming_prefix

"from_numerator", "generic", or a custom prefix

denominator_naming_prefix

"from_denominator", "generic", or a custom prefix

statistics_naming_prefix

"universal" (one variable for trend status, one variable for doubling dates), "from_numerator_and_prX" (If denominator is NULL, then one variable corresponding to numerator. If denominator exists, then one variable for each of the prXs)

remove_training_data

Boolean. If TRUE, removes the training data (i.e. 1:(trend_dates-1) or 1:(trend_isoyearweeks-1)) from the returned dataset.

include_decreasing

If true, then *_trend*_status contains the levels c("training", "forecast", "decreasing", "null", "increasing"), otherwise the levels c("training", "forecast", "notincreasing", "increasing").

alpha

Significance level for change in trend.

Value

The original csfmt_rts_data_v1 dataset with extra columns. *_trend*_status contains a factor with levels c("training", "forecast", "decreasing", "null", "increasing"), while *_doublingdays* contains the expected number of days before the numerator doubles.

Examples

d <- cstidy::nor_covid19_icu_and_hospitalization_csfmt_rts_v1
d <- d[granularity_time=="isoyearweek"]
res <- csalert::short_term_trend(
  d,
  numerator = "hospitalization_with_covid19_as_primary_cause_n",
  trend_isoyearweeks = 6
)
print(res[, .(
  isoyearweek,
  hospitalization_with_covid19_as_primary_cause_n,
  hospitalization_with_covid19_as_primary_cause_trend0_41_status
)])

Determine the short term trend of a timeseries

Description

The method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>. This function has been frozen on 2024-06-24. It is designed to use sts

Usage

short_term_trend_sts_v1(sts, control = list(w = 5, alpha = 0.05))

Arguments

sts

Data object of type sts.

control

Control object, a named list with several elements.

w

Length of the window that is being analyzed.

alpha

Significance level for change in trend.

Value

sts object with the alarms slot set to 0/1 if not-increasing/increasing.

Examples

d <- cstidy::nor_covid19_icu_and_hospitalization_csfmt_rts_v1
d <- d[granularity_time=="isoyearweek"]
sts <- surveillance::sts(
  observed = d$hospitalization_with_covid19_as_primary_cause_n, # weekly number of cases
  start = c(d$isoyear[1], d$isoweek[1]), # first week of the time series
  frequency = 52
)
x <- csalert::short_term_trend_sts_v1(
  sts,
  control = list(
    w = 5,
    alpha = 0.05
  )
)
plot(x)

Determine the short term trend of a timeseries

Description

The method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>.

Usage

signal_detection_hlm(x, ...)

## S3 method for class 'csfmt_rts_data_v1'
signal_detection_hlm(
  x,
  value,
  baseline_isoyears = 5,
  remove_last_isoyearweeks = 0,
  forecast_isoyearweeks = 2,
  value_naming_prefix = "from_numerator",
  remove_training_data = FALSE,
  ...
)

Arguments

x

Data object

...

Not in use.

value

Character of name of value

baseline_isoyears

Number of years in the past you want to include as baseline

remove_last_isoyearweeks

Number of isoyearweeks you want to remove at the end (due to unreliable data)

forecast_isoyearweeks

Number of isoyearweeks you want to forecast into the future

value_naming_prefix

"from_numerator", "generic", or a custom prefix

remove_training_data

Boolean. If TRUE, removes the training data (i.e. 1:(trend_isoyearweeks-1)) from the returned dataset.

Value

The original csfmt_rts_data_v1 dataset with extra columns. *_trend*_status contains a factor with levels c("training", "forecast", "decreasing", "null", "increasing"), while *_doublingdays* contains the expected number of days before the numerator doubles.

Examples

d <- cstidy::nor_covid19_icu_and_hospitalization_csfmt_rts_v1
d <- d[granularity_time=="isoyearweek"]
res <- csalert::signal_detection_hlm(
  d,
  value = "hospitalization_with_covid19_as_primary_cause_n",
  baseline_isoyears = 1
)
print(res[, .(
  isoyearweek,
  hospitalization_with_covid19_as_primary_cause_n,
  hospitalization_with_covid19_as_primary_cause_forecasted_n,
  hospitalization_with_covid19_as_primary_cause_forecasted_n_forecast,
  hospitalization_with_covid19_as_primary_cause_baseline_predinterval_q50x0_n,
  hospitalization_with_covid19_as_primary_cause_baseline_predinterval_q99x5_n,
  hospitalization_with_covid19_as_primary_cause_n_status
)])

Simulate baseline data —- Simulation of baseline data.

Description

This function simulates a time series of daily counts in the absence of outbreaks. Data is simulated using a poisson/negative binomial model as described in Noufaily et al. (2019). Properties of time series such as frequency of baseline observations, trend, seasonal and weekly pattern can be specified in the simulation.

Usage

simulate_baseline_data(
  start_date,
  end_date,
  seasonal_pattern_n,
  weekly_pattern_n,
  alpha,
  beta,
  gamma_1,
  gamma_2,
  gamma_3,
  gamma_4,
  phi,
  shift_1
)

Arguments

start_date

Starting date of the simulation period. Date is in the format of 'yyyy-mm-dd'.

end_date

Ending date of the simulation period. Date is in the format of 'yyyy-mm-dd'.

seasonal_pattern_n

Number of seasonal patterns. For no seasonal pattern seasonal_pattern_n = 0. Seasonal_pattern_n = 1 represents annual pattern. Seasonal_pattern_n = 2 indicates biannual pattern.

weekly_pattern_n

Number of weekly patterns. For no specific weekly pattern, weekly_pattern_n = 0. Weekly_pattern_n = 1 represents one weekly peak.

alpha

The parameter is used to specify the baseline frequencies of reports

beta

The parameter is used to specify to specify linear trend

gamma_1

The parameter is used to specify the seasonal pattern

gamma_2

The parameter is used to specify the seasonal pattern

gamma_3

The parameter is used to specify day-of-the week pattern

gamma_4

The parameter is used to specify day-of-the week pattern

phi

Dispersion parameter. If phi =0, a Poisson model is used to simulate baseline data.

shift_1

Horizontal shift parameter to help control over week/month peaks.

Value

A csfmt_rts_data_v1, data.table containing a time series of counts

wday

day-of-the week

n

cases

Examples

baseline  <- simulate_baseline_data(
start_date = as.Date("2012-01-01"),
end_date = as.Date("2019-12-31"),
seasonal_pattern_n = 1,
weekly_pattern_n = 1,
alpha = 3,
beta = 0,
gamma_1 = 0.8,
gamma_2 = 0.6,
gamma_3 = 0.8,
gamma_4 = 0.4,
phi = 4,
shift_1 = 29 )

Simulate seasonal outbreaks —-

Description

Simulation of seasonal outbreaks for syndromes/diseases that follows seasonal trends. Seasonal outbreaks are more variable both in size and timing than seasonal patterns. The number of seasonal outbreaks occur in a year are defined by n_season_outbreak. The parameters week_season_start and week_season_end define the season window. The start of the seasonal outbreak is drawn from the season window weeks, with higher probability of outbreak occurs around the peak of the season (week_season_peak). The seasonal outbreak size (excess number of cases that occurs during the outbreak) is simulated using a poisson distribution as described in Noufaily et al. (2019).

Usage

simulate_seasonal_outbreak_data(
  data,
  week_season_start = 40,
  week_season_peak = 4,
  week_season_end = 20,
  n_season_outbreak = 1,
  m = 50
)

Arguments

data

A csfmt_rds data object

week_season_start

Starting season week number

week_season_peak

Peak of the season week number

week_season_end

Ending season week number

n_season_outbreak

Number of seasonal outbreaks to be simulated

m

Parameter to determine the size of the outbreak (m times the standard deviation of the baseline count at the starting day of the seasonal outbreak)

Value

A csfmt_rts_data_v1, data.table

Simulate spiked outbreaks —-

Description

Simulation of spiked outbreak as described in Noufaily et al. (2019). The method for simulating spiked outbreak is similar to seasonal outbreaks simulation but they are shorter in duration and are added only the last year of data (prediction data). Spiked outbreaks can start at any week during the prediction data

Usage

simulate_spike_outbreak_data(data, n_sp_outbreak = 1, m)

Arguments

data

A csfmt_rds data object

n_sp_outbreak

Number of spiked outbreaks to be simulated

m

Parameter to determine the size of the outbreak (m times the standard deviation of the baseline count at the starting day of the seasonal outbreak)

Value

A csfmt_rts_data_v1, data.table

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