Title:	Automated and Early Detection of Disease Outbreaks
Version:	0.1.1
Description:	A powerful tool for automating the early detection of disease outbreaks in time series data. 'aeddo' employs advanced statistical methods, including hierarchical models, in an innovative manner to effectively characterize outbreak signals. It is particularly useful for epidemiologists, public health professionals, and researchers seeking to identify and respond to disease outbreaks in a timely fashion. For a detailed reference on hierarchical models, consult Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.2.3
Suggests:	knitr, MASS, rmarkdown, testthat (≥ 3.0.0), zoo
Config/testthat/edition:	3
Imports:	ggplot2, checkmate, dplyr, lifecycle, magrittr, purrr, tibble, tidyr, utils, rlang
URL:	https://ssi-dk.github.io/aeddo/, https://github.com/ssi-dk/aeddo
VignetteBuilder:	knitr
BugReports:	https://github.com/ssi-dk/aeddo/issues
NeedsCompilation:	no
Packaged:	2023-11-27 08:46:48 UTC; b280787
Author:	Kasper Schou Telkamp [aut], Lasse Engbo Christiansen [aut, cre], Statens Serum Institut, SSI [cph, fnd]
Maintainer:	Lasse Engbo Christiansen <lsec@ssi.dk>
Repository:	CRAN
Date/Publication:	2023-11-27 10:10:08 UTC

aeddo: Automated and Early Detection of Disease Outbreaks

Description

A powerful tool for automating the early detection of disease outbreaks in time series data. 'aeddo' employs advanced statistical methods, including hierarchical models, in an innovative manner to effectively characterize outbreak signals. It is particularly useful for epidemiologists, public health professionals, and researchers seeking to identify and respond to disease outbreaks in a timely fashion. For a detailed reference on hierarchical models, consult Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.

Author(s)

Maintainer: Lasse Engbo Christiansen lsec@ssi.dk (ORCID)

Authors:

• Kasper Schou Telkamp kasper.telkamp@gmail.com (ORCID)

Other contributors:

• Statens Serum Institut, SSI [copyright holder, funder]

See Also

Useful links:

• https://ssi-dk.github.io/aeddo/

• https://github.com/ssi-dk/aeddo

• Report bugs at https://github.com/ssi-dk/aeddo/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Automated and Early Detection of Disease Outbreaks

Description

This function performs automated an early detection of disease outbreaks, (aeddo), on a time series data set. It utilizes hierarchical models in an innovative manner to infer one-step ahead random effects. In turn, these random effects are used directly to characterize an outbreak.

Usage

aeddo(
  data = data.frame(),
  formula = formula(),
  k = integer(),
  sig_level = 0.95,
  exclude_past_outbreaks = TRUE,
  init_theta = numeric(),
  lower = numeric(),
  upper = numeric(),
  method = "BFGS"
)

Arguments

Value

A tibble-like 'aedseo' object containing:

• 'window_data': A list of tibble, each representing the data for this windowed parameter estimation.

• 'reference_data': A list of tibble, each representing the data for the reference time point.

• 'phi': The dispersion parameter.

• 'lambda': The estimated outbreak intensity.

• 'u': The one-step ahead random effect.

• 'u_probability': The probability of observing the one-step ahead random effect.

• 'outbreak_alarm': Logical. Indicates if an outbreak is detected.

Examples

# Create an example aedseo_tsd object
aeddo_data <- data.frame(
  time = as.Date(c(
    "2023-01-01",
    "2023-01-02",
    "2023-01-03",
    "2023-01-04",
    "2023-01-05",
    "2023-01-06"
  )),
  y = c(100, 120, 180, 110, 130, 140),
  n = 1
)

# Supply a model formula
fixed_effects_formula <- y ~ 1

# Choose a size for the rolling window
k <- 2
# ... and quantile for the threshold
sig_level <- 0.9

# Employ the algorithm
aeddo_results <- aeddo(
  data = aeddo_data,
  formula = fixed_effects_formula,
  k = k,
  sig_level = sig_level,
  exclude_past_outbreaks = TRUE,
  init_theta = c(1, 0),
  lower = c(-Inf, 1e-6),
  upper = c(Inf, 1e2),
  method = "L-BFGS-B"
)
# Print the results
print(aeddo_results)

Create a complete 'ggplot' appropriate to a particular data type

Description

This function generates a complete 'ggplot' object suitable for visualizing time series data in an aeddo object. It creates a line plot connecting the observations and adds points at each data point.

Usage

autoplot(object, ...)

## S3 method for class 'aeddo'
autoplot(object, ...)

Arguments

Value

A 'ggplot' object for visualizing the time series data.

Examples

# Create an example aeddo object
aeddo_data <- data.frame(
  time = as.Date(c(
    "2023-01-01",
    "2023-01-02",
    "2023-01-03",
    "2023-01-04",
    "2023-01-05",
    "2023-01-06"
  )),
  y = c(100, 120, 180, 110, 130, 140),
  n = 1
)

# Supply a model formula
fixed_effects_formula <- y ~ 1

# Choose a size for the rolling window
k <- 2
# ... and quantile for the threshold
sig_level <- 0.9

# Employ the algorithm
aeddo_results <- aeddo(
  data = aeddo_data,
  formula = fixed_effects_formula,
  k = k,
  sig_level = sig_level,
  exclude_past_outbreaks = TRUE,
  init_theta = c(1, 0),
  lower = c(-Inf, 1e-6),
  upper = c(Inf, 1e2),
  method = "L-BFGS-B"
)

# Create a ggplot visualization for the aeddo object
autoplot(aeddo_results)

Check and assert the inputs for the aeddo function.

Description

This function is for internal use and checks the validity of inputs to the aeddo function. It performs various checks using the checkmate package to ensure that the inputs conform to expected types and values.

Usage

check_aeddo_inputs(
  data,
  formula,
  k,
  sig_level,
  exclude_past_outbreaks,
  init_theta,
  lower,
  upper,
  method
)

Arguments

Value

Returns nothing. Raises errors if the inputs do not meet the specified criteria.

Examples

## Not run: 
# This function is for internal use and is called within the aeddo function.
# It is not intended to be called directly by users.

## End(Not run)

Check and assert the inputs for the nll_poisson_gamma function.

Description

This function is for internal use and checks the validity of inputs to the nll_poisson_gamma function. It performs various checks using the checkmate package to ensure that the inputs conform to expected types and values.

Usage

check_nll_poisson_gamma_inputs(theta, data, formula)

Arguments

Value

Returns nothing. Raises errors if the inputs do not meet the specified criteria.

Examples

## Not run: 
# This function is for internal use and is called within the
# nll_poisson_gamma function. It is not intended to be called directly by
# users.

## End(Not run)

Negative Log-Likelihood for Poisson Gamma Model

Description

Calculate the negative log-likelihood for the Poisson Gamma modeling framework.

Usage

nll_poisson_gamma(theta, data, formula)

Arguments

Value

The negative log-likelihood value.

Examples

# Initial parameters
theta <- c(0.5, 0.1)

# Sample data
data <- data.frame(
  y = c(10, 15, 20, 30, 50, 100, 200, 40, 20, 10),
  n = c(100, 150, 200, 300, 500, 1000, 2000, 400, 200, 100)
)

# Fixed effects model formula
fixed_effects_formula <- y ~ 1

# Calculate negative log likelihood
nll_poisson_gamma(
  theta = theta,
  data = data,
  formula = fixed_effects_formula
)

Create a complete 'ggplot' appropriate to a particular data type

Description

This function generates a complete 'ggplot' object suitable for visualizing time series data in an aeddo object. It creates a line plot connecting the observations and adds points at each data point.

Usage

## S3 method for class 'aeddo'
plot(x, ...)

Arguments

Value

A 'ggplot' object for visualizing the time series data.

See Also

autoplot()

Examples

# Create an example aeddo object
aeddo_data <- data.frame(
  time = as.Date(c(
    "2023-01-01",
    "2023-01-02",
    "2023-01-03",
    "2023-01-04",
    "2023-01-05",
    "2023-01-06"
  )),
  y = c(100, 120, 180, 110, 130, 140),
  n = 1
)

# Supply a model formula
fixed_effects_formula <- y ~ 1

# Choose a size for the rolling window
k <- 2
# ... and quantile for the threshold
sig_level <- 0.9

# Employ the algorithm
aeddo_results <- aeddo(
  data = aeddo_data,
  formula = fixed_effects_formula,
  k = k,
  sig_level = sig_level,
  exclude_past_outbreaks = TRUE,
  init_theta = c(1, 0),
  lower = c(-Inf, 1e-6),
  upper = c(Inf, 1e2),
  method = "L-BFGS-B"
)

# Create a ggplot visualization for the aeddo object
plot(aeddo_results)