Package: aedseo 1.1.0.9000

Lasse Engbo Christiansen

aedseo: Automated and Early Detection of Seasonal Epidemic Onset and Burden Levels

A powerful tool for automating the early detection of seasonal epidemic onsets in time series data. It offers the ability to estimate growth rates across consecutive time intervals, calculate the sum of cases (SoC) within those intervals, and estimate seasonal onsets within user defined seasons. With use of a disease-specific threshold it also offers the possibility to estimate seasonal onset of epidemics. Additionally it offers the ability to estimate burden levels for seasons based on historical data. It is aimed towards epidemiologists, public health professionals, and researchers seeking to identify and respond to seasonal epidemics in a timely fashion.

Authors:Sofia Myrup Otero [aut], Kasper Schou Telkamp [aut], Lasse Engbo Christiansen [aut, cre], Rasmus Skytte Randløv [rev], Statens Serum Institut, SSI [cph, fnd]

aedseo_1.1.0.9000.tar.gz
aedseo_1.1.0.9000.zip(r-4.7)aedseo_1.1.0.9000.zip(r-4.6)aedseo_1.1.0.9000.zip(r-4.5)
aedseo_1.1.0.9000.tgz(r-4.6-any)aedseo_1.1.0.9000.tgz(r-4.5-any)
aedseo_1.1.0.9000.tar.gz(r-4.7-any)aedseo_1.1.0.9000.tar.gz(r-4.6-any)
aedseo_1.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
aedseo/json (API)

# Install 'aedseo' in R:
install.packages('aedseo', repos = c('https://ssi-dk.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ssi-dk/aedseo/issues

Pkgdown/docs site:https://ssi-dk.github.io

On CRAN:

Conda:

burden-of-diseaseepidemic-modelepidemic-modellingonset-detectionsurveillance-systems

6.19 score 1 stars 26 scripts 322 downloads 12 exports 36 dependencies

Last updated from:ef8b7984d3. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK277
source / vignettesOK289
linux-release-x86_64OK249
macos-release-arm64OK152
macos-oldrel-arm64OK185
windows-develOK206
windows-releaseOK209
windows-oldrelOK196
wasm-releaseOK174

Exports:autoplotcombined_seasonal_outputconsecutive_growth_warningsepi_calendarestimate_disease_thresholdfit_growth_ratefit_percentilesgenerate_seasonal_datahistorical_summaryseasonal_burden_levelsseasonal_onsetto_time_series

Dependencies:backportscheckmateclicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglifecyclelubridatemagrittrpillarpkgconfigplyrpracmapurrrR6RColorBrewerRcpprlangS7scalesstringistringrtibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithr

Automated Detection of Seasonal Epidemic Onset and Burden Levels in R
Introduction | Seasonal data | Determining season | Determining the disease specific threshold | Applying the main algorithm | Seasonal onset and burden levels | Seasonal offset and multiple waves | Summary of seasonal onset and burden levels | Plot the comprehensive seasonal analysis | Investigate historical estimates | Example with incidence
Rendered fromaedseo.Rmdusingknitr::rmarkdown

Last update: 2026-01-23
Started: 2025-01-21

Multiple waves
Methodology | Applying the multiple waves algorithm | Estimate disease-specific threshold | Estimate multiple waves | Plot the comprehensive seasonal analysis with multiple waves
Rendered frommultiple_waves.Rmdusingknitr::rmarkdown

Last update: 2026-01-23
Started: 2025-11-14

Seasonal Burden Levels
Methodology | Peak observations | Weighting | Distribution and optimisation | Burden levels | Applying the seasonal_burden_levels() algorithm | Use the intensity_levels method | Use the peak_levels method | Compare intensity_levels, peak_levels and mem algorithms | aedseo and mem levels
Rendered fromburden_levels.Rmdusingknitr::rmarkdown

Last update: 2025-07-14
Started: 2025-01-28

Seasonal Epidemic Onset
Methodology | Exponential growth rate | Applying the seasonal_onset algorithm | Visualising Growth Rates | Predicting Growth Rates | Summarising seasonal_onset results
Rendered fromseasonal_onset.Rmdusingknitr::rmarkdown

Last update: 2025-07-14
Started: 2025-01-28

Simulate Seasonal Epidemic Waves
Simulation | Plot seasonal waves | Example of positive trend (weekly observations) | Example of negative trend (monthly observations) | Example of no trend (daily observations) | Example of phase shift (daily observations) | Examples of different noise scenarios | Deterministic (no noise) | Poisson-distributed noise | Negative binomial-distributed noise (high overdispersion) | Examples of different epidemic concentrations | Pure sinusoidal season | Epidemic concentrated season
Rendered fromgenerate_seasonal_wave.Rmdusingknitr::rmarkdown

Last update: 2025-07-14
Started: 2025-01-21