Package: EpiEstim 2.4

Anne Cori

EpiEstim: Estimate Time Varying Reproduction Numbers from Epidemic Curves

Tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) <doi:10.1093/aje/kwh255>.

Authors:Anne Cori [aut, cre], Simon Cauchemez [ctb], Neil M. Ferguson [ctb], Christophe Fraser [ctb], Elisabeth Dahlqwist [ctb], P. Alex Demarsh [ctb], Thibaut Jombart [ctb], Zhian N. Kamvar [ctb], Justin Lessler [ctb], Shikun Li [ctb], Jonathan A. Polonsky [ctb], Jake Stockwin [ctb], Robin Thompson [ctb], Rolina van Gaalen [ctb], Rebecca Nash [ctb], Sangeeta Bhatia [ctb], Jack Wardle [ctb], Andrea Brizzi [ctb]

EpiEstim_2.4.tar.gz
EpiEstim_2.4.zip(r-4.7-any)EpiEstim_2.4.zip(r-4.6-any)EpiEstim_2.4.zip(r-4.5-any)
EpiEstim_2.4.tgz(r-4.6-any)EpiEstim_2.4.tgz(r-4.5-any)
EpiEstim_2.4.tar.gz(r-4.7-any)EpiEstim_2.4.tar.gz(r-4.6-any)
EpiEstim_2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
EpiEstim/json (API)

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

Bug tracker:https://github.com/mrc-ide/epiestim/issues

Datasets:
  • covid_deaths_2020_uk - Data on the 2020-2022 SARS-CoV-2 epidemic in the UK.
  • flu_2009_NYC_school - Data on the 2009 H1N1 influenza pandemic in a school in New York city
  • Flu1918 - Data on the 1918 H1N1 influenza pandemic in Baltimore.
  • Flu2009 - Data on the 2009 H1N1 influenza pandemic in a school in Pennsylvania.
  • Measles1861 - Data on the 1861 measles epidemic in Hagelloch, Germany.
  • mers_2014_15 - Data on Middle East Respiratory Syndrome (MERS) in Saudi Arabia.
  • MockRotavirus - Mock data on a rotavirus epidemic.
  • SARS2003 - Data on the 2003 SARS epidemic in Hong Kong.
  • Smallpox1972 - Data on the 1972 smallpox epidemic in Kosovo

On CRAN:

Conda:

12.41 score 105 stars 7 packages 1.3k scripts 1.9k downloads 60 mentions 30 exports 52 dependencies

Last updated from:0b50c55a2e. Checks:7 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develNOTE245
source / vignettesOK295
linux-releaseNOTE207
macos-releaseNOTE194
macos-oldrelNOTE168
windows-develNOTE217
windows-releaseNOTE201
windows-oldrelNOTE216
wasm-releaseOK115

Exports:aggregate_incbackimpute_Icheck_cdt_samples_convergencecoarse2estimcompute_lambdacompute_si_cutoffcompute_t_mindefault_mcmc_controlsdefault_priorsdiscr_siDiscrSIdraw_epsilondraw_Restimate_advantageestimate_Restimate_R_aggestimate_R_plotsEstimateRfirst_nonzero_incidget_shape_epsilonget_shape_R_flatinit_mcmc_paramsmake_configmake_mcmc_controloverall_infectivityOverallInfectivityprocess_I_multivariantsample_posterior_Rwallinga_teunisWT

Dependencies:abindaweekclicoarseDataToolscodacpp11distcretedplyrepitrixfarverfitdistrplusgenericsggplot2gluegridExtragtableincidenceisobandlabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsmcmcMCMCpackpatchworkpillarpkgconfigplyrpurrrquantregR6RColorBrewerRcppreshape2rlangS7scalessodiumSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Dealing with missed generations of infections with EpiEstim
Background | Why do we need backimputation? | A simple toy scenario | Real world scenario: UK COVID deaths | No left-censoring: early R~t~ estimates remain similar | Left-censoring: back-imputation reduces bias. | Caveats | References
Rendered fromEpiEstim_backimputation.Rmdusingknitr::rmarkdown

Last update: 2024-08-30
Started: 2024-08-30

EpiEstim: a demonstration
Overview | Estimating R on sliding weekly windows, with a parametric serial interval | Estimating R with a non parametric serial interval distribution | Estimating R accounting for uncertainty on the serial interval distribution | Estimating R and the serial interval using data on pairs infector/infected | Changing the time windows for estimation | Accounting for missed generations of infections | Different ways of specifying the incidence | Specifying imported cases | EpiEstimApp
Rendered fromshort_demo.Rmdusingknitr::rmarkdown

Last update: 2024-08-30
Started: 2021-04-09

EpiEstim Vignette
Input data | Step A: Serial Interval | Step B: Incidence | Estimate R~t~ | Step C: estimate_R() | Prior for R~t~ | Forecast future incidence | Step D: Projections package | Example A: Entire workflow | Example B: Estimating R~t~ using non-parametric distribution | Example C: Estimating R~t~ using infector-infected cases | Example D: Estimating R~t~ using "si_from_sample" | Example E: Estimating R~t~ using "uncertain_si" | Example F: Changing the prior for estimating R~t~ | Example G: Changing the time window to estimate R~t~ | References
Rendered fromfull_EpiEstim_vignette.Rmdusingknitr::rmarkdown

Last update: 2024-08-30
Started: 2021-04-09

MV-EpiEstim
SARS-CoV-2 variants
Rendered fromMV_EpiEstim_vignette.Rmdusingknitr::rmarkdown

Last update: 2024-08-30
Started: 2021-11-26

EpiEstim for aggregated incidence data
Estimate R~t~ from temporally aggregated incidence data | estimate_R() for aggregated data | Estimate R~t~ from weekly COVID-19 data | References
Rendered fromEpiEstim_aggregated_data.Rmdusingknitr::rmarkdown

Last update: 2023-07-03
Started: 2022-12-07

Alternative software for estimating the reproduction number
Summary table of other R packages and tools
Rendered fromalternative_software.Rmdusingknitr::rmarkdown

Last update: 2023-04-20
Started: 2022-04-01

Readme and manuals

Help Manual

Help pageTopics
Aggregating daily incidence to longer time windowsaggregate_inc
Impute unobserved generations of infectionbackimpute_I
Checking convergence of an MCMC chain by using the Gelman-Rubin algorithmcheck_cdt_samples_convergence
Link coarseDataTools and EpiEstimcoarse2estim
Compute the overall infectivitycompute_lambda
Index before which at most a given probability mass is capturedcompute_si_cutoff
Compute the smallest index at which joint estimation should startcompute_t_min
Data on the 2020-2022 SARS-CoV-2 epidemic in the UK.covid_deaths_2020_uk
Set default for MCMC controldefault_mcmc_controls
Set default for Gamma priorsdefault_priors
Discretized Generation Time Distribution Assuming A Shifted Gamma Distributiondiscr_si
Function to ensure compatibility with EpiEstim versions <2.0DiscrSI
Draw epsilon from marginal posterior distributiondraw_epsilon
Draw R from marginal posterior distributiondraw_R
Jointly estimate the instantaneous reproduction number for a reference pathogen/strain/variant and the relative transmissibility of a "new" pathogen/strain/variantestimate_advantage
Estimated Instantaneous Reproduction Numberestimate_R
Estimated Instantaneous Reproduction Number from coarsely aggregated dataestimate_R_agg
Wrapper for plot.estimate_Restimate_R_plots
Function to ensure compatibility with EpiEstim versions <2.0EstimateR
Get the first day of non-zero incidence across all variants and locations.first_nonzero_incid
Data on the 2009 H1N1 influenza pandemic in a school in New York cityflu_2009_NYC_school
Data on the 1918 H1N1 influenza pandemic in Baltimore.Flu1918
Data on the 2009 H1N1 influenza pandemic in a school in Pennsylvania.Flu2009
Precompute shape of posterior distribution for epsilonget_shape_epsilon
Precompute shape of posterior distribution for Rget_shape_R_flat
init_mcmc_params Finds clever starting points for the MCMC to be used to estimate the serial interval, e.g. when using option 'si_from_data' in 'estimate_R'init_mcmc_params
Set and check parameter settings of estimate_Rmake_config
make_mcmc_control Creates a list of mcmc control parameters to be used in 'config$mcmc_control', where 'config' is an argument of the 'estimate_R' function. This is used to configure the MCMC chain used to estimate the serial interval within 'estimate_R' (with method "si_from_data").make_mcmc_control
Data on the 1861 measles epidemic in Hagelloch, Germany.Measles1861
Data on Middle East Respiratory Syndrome (MERS) in Saudi Arabia.mers_2014_15
Mock data on a rotavirus epidemic.MockRotavirus
Overall Infectivity Due To Previously Infected Individualsoverall_infectivity
Function to ensure compatibility with EpiEstim versions <2.0OverallInfectivity
Plot outputs of estimate_rplot.estimate_R
Process incidence input for multivariant analyses with estimate_advantageprocess_I_multivariant
sample from the posterior R distributionsample_posterior_R
Data on the 2003 SARS epidemic in Hong Kong.SARS2003
Data on the 1972 smallpox epidemic in KosovoSmallpox1972
Estimation of the case reproduction number using the Wallinga and Teunis methodwallinga_teunis
Function to ensure compatibility with EpiEstim versions <2.0WT