Package: surveillance 1.23.1.9000
surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena
Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.
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surveillance.pdf |surveillance.html✨
surveillance/json (API)
NEWS
# Install 'surveillance' in R: |
install.packages('surveillance', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://r-forge.r-project.org/projects/surveillance
- MMRcoverageDE - MMR coverage levels in the 16 states of Germany
- abattoir - Abattoir Data
- campyDE - Campylobacteriosis and Absolute Humidity in Germany 2002-2011
- deleval - Surgical Failures Data
- fluBYBW - Influenza in Southern Germany
- h1_nrwrp - RKI SurvStat Data
- ha - Hepatitis A in Berlin
- ha.sts - Hepatitis A in Berlin
- hagelloch - 1861 Measles Epidemic in the City of Hagelloch, Germany
- hagelloch.df - 1861 Measles Epidemic in the City of Hagelloch, Germany
- hepatitisA - Hepatitis A in Germany
- husO104Hosp - Hospitalization date for HUS cases of the STEC outbreak in Germany, 2011
- imdepi - Occurrence of Invasive Meningococcal Disease in Germany
- imdepifit - Example 'twinstim' Fit for the 'imdepi' Data
- influMen - Influenza and meningococcal infections in Germany, 2001-2006
- k1 - RKI SurvStat Data
- m1 - RKI SurvStat Data
- m2 - RKI SurvStat Data
- m3 - RKI SurvStat Data
- m4 - RKI SurvStat Data
- m5 - RKI SurvStat Data
- measles.weser - Measles in the Weser-Ems region of Lower Saxony, Germany, 2001-2002
- measlesDE - Measles in the 16 states of Germany
- measlesWeserEms - Measles in the Weser-Ems region of Lower Saxony, Germany, 2001-2002
- meningo.age - Meningococcal infections in France 1985-1997
- momo - Danish 1994-2008 all-cause mortality data for eight age groups
- n1 - RKI SurvStat Data
- n2 - RKI SurvStat Data
- q1_nrwh - RKI SurvStat Data
- q2 - RKI SurvStat Data
- rotaBB - Rotavirus cases in Brandenburg, Germany, during 2002-2013 stratified by 5 age categories
- s1 - RKI SurvStat Data
- s2 - RKI SurvStat Data
- s3 - RKI SurvStat Data
- salmAllOnset - Salmonella cases in Germany 2001-2014 by data of symptoms onset
- salmHospitalized - Hospitalized Salmonella cases in Germany 2004-2014
- salmNewport - Salmonella Newport cases in Germany 2004-2013
- salmonella.agona - Salmonella Agona cases in the UK 1990-1995
- shadar - Salmonella Hadar cases in Germany 2001-2006
- stsNewport - Salmonella Newport cases in Germany 2001-2015
Last updated 7 hours agofrom:4366ee9cf6. Checks:OK: 7 NOTE: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Sep 07 2024 |
R-4.5-win-x86_64 | NOTE | Sep 07 2024 |
R-4.5-linux-x86_64 | OK | Sep 07 2024 |
R-4.4-win-x86_64 | OK | Sep 07 2024 |
R-4.4-mac-x86_64 | OK | Sep 07 2024 |
R-4.4-mac-aarch64 | OK | Sep 07 2024 |
R-4.3-win-x86_64 | NOTE | Sep 07 2024 |
R-4.3-mac-x86_64 | OK | Sep 07 2024 |
R-4.3-mac-aarch64 | OK | Sep 07 2024 |
Exports:addFormattedXAxisaddSeason2formulaaggregatealarmsalarms<-algo.bayesalgo.bayes1algo.bayes2algo.bayes3algo.bayesLatestTimepointalgo.callalgo.cdcalgo.cdcLatestTimepointalgo.comparealgo.cusumalgo.farringtonalgo.farrington.assign.weightsalgo.farrington.fitGLMalgo.farrington.fitGLM.fastalgo.farrington.fitGLM.populationOffsetalgo.farrington.thresholdalgo.glrnbalgo.glrpoisalgo.hmmalgo.outbreakPalgo.qualityalgo.rkialgo.rki1algo.rki2algo.rki3algo.rkiLatestTimepointalgo.rogersonalgo.summaryalgo.twinsanimateanimate_nowcastsanimate.epidataCSanscombe.residualsarlCusumas.data.frameas.epidataas.epidata.data.frameas.epidata.defaultas.epidata.epidataCSas.epidataCSas.hhh4simslistas.xts.stsat2ndChangeatChangeatMedianautoplot.stsbackprojNPbayesbestCombinationbodabodaDelaycalibrationTestcalibrationTest.defaultcategoricalCUSUMcheckResidualProcessclapplycoeflistcoefWcontrolcontrol<-coxcreate.disProgcusumdecompose.hhh4delayCDFdiscpolydisProg2stsdssearsCepidataCS2stsepidataCSplot_spaceepidataCSplot_timeepitestepochepoch<-epochInYearestimateGLRNbHookfanplotfarringtonfarringtonFlexiblefind.khfindHfindKfixefformatDateformatPvalgetMaxEVgetMaxEV_seasongetNEweightsgetSourceDistsglm_epidataCSglrnbglrpoishhh4hValuesiafplotintensity.twinstimintensityplotintensityplot.simEpidataintensityplot.simEpidataCSintensityplot.twinSIRintensityplot.twinstimintersectPolyCircleintersperseisoWeekYearknoxks.plot.uniflayout.labelslayout.scalebarlinelist2stslogsLRCUSUM.runlengthmagic.dimmakeControlmarksmarks.epidataCSmeanHHHmultinomialTSmultinomialTS<-multiplicitynbOrderneighbourhoodneighbourhood<-nowcastobservedobserved<-oneStepAheadoutbreakPpairedbinCUSUMpairedbinCUSUM.runlengthpermutationTestpermute.epidataCSpitpit.defaultplapplyplotplotHHH4_fittedplotHHH4_fitted1plotHHH4_mapsplotHHH4_maxEVplotHHH4_neweightsplotHHH4_riplotHHH4_seasonplotHHH4sims_fanplotHHH4sims_sizeplotHHH4sims_timepoly2adjmatpolyAtBorderpopulationpopulation<-predintprimeFactorsR0ranefrefvalIdxByDatereportingTrianglereset.surveillance.optionsrkirpsscorescoressessiafsiaf.constantsiaf.exponentialsiaf.gaussiansiaf.powerlawsiaf.powerlaw1siaf.powerlawLsiaf.stepsiaf.studentsim.pointSourcesim.seasonalNoisesimEndemicEventssimEpidatasimEpidataCSsimpleR0simulate.twinSIRsimulate.twinstimsizeHHHstateplotstcdstepComponentstKteststssts_creationsts_observationsts2disProgstsplot_alarmstsplot_spacestsplot_spacetimestsplot_timestsplot_time1summary.twinstimsurveillance.optionstiaftiaf.constanttiaf.exponentialtiaf.steptidy.ststoLatextwinSIRtwinstimunionSpatialPolygonsuntieupdate.epidataCSupdate.hhh4update.twinstimupperboundupperbound<-W_npW_powerlawwrap.algoxtable.summary.twinstimyearzetaweights
Dependencies:deldirlatticeMASSMatrixnlmepolyclippolyCubspspatstat.dataspatstat.geomspatstat.univarspatstat.utilsxtable
algo.glrnb: Count data regression charts using the generalized likelihood ratio statistic
Rendered fromglrnb.Rnw
usingutils::Sweave
on Sep 07 2024.Last update: 2023-03-19
Started: 2012-07-25
Getting started with outbreak detection
Rendered fromsurveillance.Rnw
usingutils::Sweave
on Sep 07 2024.Last update: 2024-05-03
Started: 2012-07-24
hhh4 (spatio-temporal): Endemic-epidemic modeling of areal count time series
Rendered fromhhh4_spacetime.Rnw
usingknitr::knitr
on Sep 07 2024.Last update: 2024-05-03
Started: 2016-03-29
hhh4: An endemic-epidemic modelling framework for infectious disease counts
Rendered fromhhh4.Rnw
usingutils::Sweave
on Sep 07 2024.Last update: 2023-10-25
Started: 2012-07-25
Monitoring count time series in R: Aberration detection in public health surveillance
Rendered frommonitoringCounts.Rnw
usingknitr::knitr
on Sep 07 2024.Last update: 2024-05-03
Started: 2016-05-14
twinSIR: Individual-level epidemic modeling for a fixed population with known distances
Rendered fromtwinSIR.Rnw
usingknitr::knitr
on Sep 07 2024.Last update: 2023-05-16
Started: 2016-03-24
twinstim: An endemic-epidemic modeling framework for spatio-temporal point patterns
Rendered fromtwinstim.Rnw
usingknitr::knitr
on Sep 07 2024.Last update: 2024-05-03
Started: 2016-03-04