Package: aedseo 0.3.0

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_0.3.0.tar.gz
aedseo_0.3.0.zip(r-4.7-any)aedseo_0.3.0.zip(r-4.6-any)aedseo_0.3.0.zip(r-4.5-any)
aedseo_0.3.0.tgz(r-4.6-any)aedseo_0.3.0.tgz(r-4.5-any)
aedseo_0.3.0.tar.gz(r-4.7-any)aedseo_0.3.0.tar.gz(r-4.6-any)
aedseo_0.3.0.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://epiverse-connect.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

5.42 score 1 stars 44 scripts 370 downloads 13 exports 35 dependencies

Last updated from:ca9153e35f. Checks:9 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK234
source / vignettesOK266
linux-releaseOK215
macos-releaseOK209
macos-oldrelOK182
windows-develOK160
windows-releaseOK168
windows-oldrelOK155
wasm-releaseOK131

Exports:aedseoautoplotcombined_seasonal_outputconsecutive_growth_warningsepi_calendarfit_growth_ratefit_percentilesgenerate_seasonal_datahistorical_summaryseasonal_burden_levelsseasonal_onsetto_time_seriestsd

Dependencies:backportscheckmateclicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglifecyclelubridatemagrittrpillarpkgconfigplyrpracmapurrrR6RColorBrewerRcpprlangS7scalesstringistringrtibbletidyselecttimechangeutf8vctrsviridisLitewithr

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 | Plot the comprehensive seasonal analysis | Investigate historical estimates
Rendered fromaedseo.Rmdusingknitr::rmarkdown

Last update: 2025-04-08
Started: 2025-01-21

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: 2025-04-08
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: 2025-04-07
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: 2025-03-28
Started: 2025-01-21