Setup and Requirements | Format weather station data | Calculate epidemic onset | Estimate canopy closure | Calculate growth rate | Calculate canopy closure dates | Plot date values

Introduction | Using nbTransmission for modeling | Creating a Dataset of Pairs | Running the unadjusted model | Unadjusted model results | Table of contribution of covariates | Forest plot of contribution of covariates | Identifying significant variables for the adjusted model | Running the adjusted model | Conclusion

Descripción | Exclusión de responsabilidad | Motivación | Potenciales usuarios | Versiones futuras | Contribuciones | Código de conducta | Instalación | Inicio rápido | Reporte automatizado | Análisis personalizados

Introduction | Example: Simulating a SEIRCONN Model 50 Times | Setup and Running Model | Generating a Saver | Plotting

Scope of regulations | Data privacy and integrity | Internet access | Registration and third parties | Updates

Scope | Output | Package architecture | Design decisions | Dependencies | Contribute

Visualising incidence of onset, hospitalisation and death | Reshape line list data | Base R | Tidyverse | Demographic data | Visualising contact data | Subset contact network to transmission network | Visualising other line list information

EpiGantt examples | Start with the Line List | Transform the Line List into long format for ggplot | Plot the Epigantt chart | Outbreak 2: Fictional Varicella Outbreak in Berlin

The seasonal plot | Seasonal alignment and plot | Combining everything for the seasonal plot | Other visualisations

Uniform population age | Structured population age

Reporting delays | Truncation | Truncate to emulate different stages of outbreak

Constant case fatality risk | Higher risk of case fatality | Continuous time-varying case fatality risk | Stepwise time-varying case fatality risk

Example 1: Simulating an SIR model | Setup and running the model | Plot | Extracting information | Adding more viruses/viruses | Tools

Introduction | Setup The SIR Model | Setup LFMCMC | Run LFMCMC Simulation | Results

Introduction | Example Scenario: Simultaneous COVID-19 and Flu Outbreaks | Model Setup | Add the Flu Virus | Add a Tool (Vaccine) | Add Events | Full Model Summary | Reproductive Numbers

Introduction | Seasonal data | Determining season | Determining the disease specific threshold | Applying the main algorithm | Plot the comprehensive seasonal analysis | Investigate historical estimates

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

Methodology | Exponential growth rate | Applying the seasonal_onset algorithm | Visualising Growth Rates | Predicting Growth Rates | Summarising seasonal_onset results

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

Motivations | linelist in a nutshell | Outline | Should I use linelist? | Getting started | Installation | Key functionalities | Tagging system | Validation | Secured methods | Worked example | Example dataset | Creating a linelist object | Changing tags | Accessing tagged variables | Using safeguards | Changing tag loss action permanently

Verbs operating on rows | dplyr::arrange() ✅ | dplyr:distinct() ✅ | dplyr::filter() ✅ | dplyr::slice() ✅ | Verbs operating on columns | dplyr::mutate() ✓ (partial) | dplyr::pull() ✅ | dplyr::relocate() ✅ | dplyr::rename() & dplyr::rename_with() ✅ | dplyr::select() ✅ | Verbs operating on groups ✘ | Verbs operating on data.frames | dplyr::bind_rows() ✅ | dplyr::bind_cols() ✘ | Joins ✘ | Verbs operating on multiple columns | dplyr::pick() ✘

Setup | Installation | API Keys | Basic Usage | Getting versioned data | Plotting | Finding locations of interest | International data | Finding data sources and signals of interest

Introduction | Prerequisites | Install AMR | Examples of Usage | Cleaning Taxonomy | Explanation | Calculating AMR | Generating Antibiograms | Taxonomic Data Sets Now in Python! | Conclusion

Concept and motivation | Design decisions | Scope | Input | Output | Modules in | 1. Standardization of column names | 2. Removal of empty rows and columns and constant columns | 3. Detection and removal of duplicates | 4. Replacement of missing values with NA | 5. Standardization of date values | 6. Standardization of subject IDs | 7. Dictionary based substitution | 8. Conversion of values when necessary | 9. Verification of the sequence of date-events | 10. Transformation of selected columns | Surrogate functions | Related packages | Dependencies | Contribute

Controlling outbreak size | Case type | Anonymous line list | Population age | Age-stratified hospitalisation and death risks | Simulate contacts table | Simulate both line list and contacts table | Using functions for distributions instead of | Predefined functions | Anonymous functions | Simulating without hospitalisations and/or deaths

An overview | General data cleaning tasks | Specific data cleaning tasks | Remove constant data | Cleaning column names | Replacing missing entries with NA | Standardizing Dates | Standardizing subject IDs | Detecting incorrect, duplicated, and missing subject IDs | Correct wrong subject ids | Checking date sequence | Converting character columns into numeric | Converting numeric values into date | Finding duplicated rows | Removing duplicates | Dictionary based data substituting | Calculating time span in different time scales (“years”, “months”, “weeks”, or “days”) | Using {cleanepi} functionalities with pipe operators | Printing the report

Use case | What we have | Estimation based on cases with known outcomes | Mathematical explanation for bias | Estimation based on expected number of known death outcomes | Simulated comparison of above methods | Deaths reported but not recoveries | Only total deaths reported | Only total cases and deaths reported

Simulate an outbreak | Removing a line list column | Tidyverse | Base R

Setup | Scenarios of contact reduction

Epi Curve examples | Ebola Outbreak in Kikwit, Democratic Republic of the Congo 1995 | SARS Outbreak in Canada 2003 | Influenza Data from Germany 2020-2025 | Extra

Population-wide risks

Contents | Competing risks | Example data | Confidence intervals for cumulative risks | A simple illustration of ci.Crisk

Contents | Introduction | Representation of follow-up data in Epi | Modeling rates from Lexis objects | Multiple time scales | More states | Lexis functions | References

Contents | Using simLexis | Simulation of transitions in multistate models | References

Contents | Overview and rationale | addCov.Lexis | addDrug.Lexis | References

Contents | Technicalities and theory | Analyses for DM in Denmark | References

Use case | What we have | What we assume | Defining a value for $R_0$ | Getting population estimates | Modelling population susceptibility | Running final_size | A short-cut for homogeneous populations | References

Preparing the data | Basic plotting | Customising the plot | The date scale | Manipulating the tree | Making use of the calendar and epidemic curve

About the package | Objective of | Contributing to the package | Scope of package | Guide to identifying distributions in the literature | Guide to data refinement once sources identified | Guide to extracting parameters | Data quality assessment in | Guide to the {epiparameter} review process | Updating parameters in the database | Database of excluded papers | References

Background | NHANES Data | List NHANES Tables | List Variables in an NHANES Table | Import NHANES Tables | Display Codebook | Apply Code Translations | Download a Complete Survey | Special Cases | Pre-pandemic data | Dual Energy X-Ray Absorptiometry | NHANES National Youth Fitness Survey (NNYFS) | Searching for tables and variables | Searching across the comprehensive list of NHANES variables | Searching for tables that contain a specific variable | Searching for tables by name pattern | Please send any feedback or requests to [email protected]. Hope you enjoy your experience with nhanesA!

Introduction | Overview | Implementation | Manipulating a number_line | Overlaps | Set operations

A brief primer on distributions in R | Using delay distribution densities in cfr | Preparing delay distribution density for cfr | Passing delay distribution density to cfr functions | Using other distribution representations | Using distributional | Using distcrete | Using continuous and discrete distributions | Links to epiparameter | References

Preamble | Visualising cumulative baseline hazards using plot.msfit() | Visualising transition probabilities using plot.probtrans() | Filled/stacked plots | Single and separate plots, confidence intervals | Plotting non-parametric cumulative incidence functions | Visualising multiple probtrans objects | A mirrored plot | Saving outputs | Reproducibility | References

NHANES and Survey Weights | Example | Pull in the relevant data | Create survey design object | Subset the survey design object | Basic data exploration | Calculating means | Calculating quantiles | Calculating variances | Conducting Simple Regression Analysis | Syntax for svyglm | Visualizing differences in weighted and unweighted survey data | Visualizing Regression Results

Cohort Design | Estimating VE without iterative matching process | Estimating VE with iterative matching process | References

Usage | Who are the users / potential users? | What is vaccine effectiveness? | For which designs is this package? | Cohort Design | What type of data is needed to use the package? | Data for Cohort design | Modeling vaccine effectiveness | VE for Cohort design | Key references

Scope | Output | Package architecture | Design decisions | Dependencies | Contribute

Setup | Load Sample Data | Make a Plot

Latest data | Download all-in-one | Download by level | Download by country | Download by location | Vintage data

Introduction | Surveillance Data | Surveillance Algorithms | Discussion and Future Work | Acknowledgements

Introduction | Getting to know the basics of the package | Using surveillance in selected contexts | Implementing surveillance in routine monitoring | Discussion

Scope | Output | Design decisions | Dependencies | Contribute

Scope | Input/Output/Interoperability | Design decisions | Dependencies | Development journey

Usage | Surveys | Bootstrapping | Demography | Symmetric contact matrices | Contact rates per capita | Splitting contact matrices | Filtering | Participant weights | Temporal aspects and demography | User-defined participant weights | Weight threshold | Numerical example | Get survey data | Weight by day of week | Weight by age | Apply threshold | Plotting | Using ggplot2 | Using R base | References

Scope | Design decisions | Simulation functions | likelihood estimation | Naming and documentation style | Dependencies | Development journey

Reducing the strength of transmission | Population-wide control | Individual-level control. | Preventing superspreading events | Truncating the generation interval | References

Overview | Data | Setting up the inputs | Onset times | Generation time | Offspring distribution | Simulation controls | Modelling assumptions | Running the simulations | Post-processing | Visualization | References

Test Negative Design (Future release) | Screening Method (Future release) | What type of data is needed? | Data for Test-negative design | Data for Screening method design

Introduction | Documentation | Getting Help | EpiModel Citation

Introduction | Current Nodal Attributes | Working With Attributes | Accessing Attributes | Modifying Attributes | Summary | Historical Nodal Attributes | Working with Attribute Histories | Recording Attribute Histories | Accessing the Attribute Histories | Epidemic Trackers | Working With Epidemic Trackers in Modules | Accessing Epidemic Trackers After a Simulation | Custom Epidemic Trackers | Writing Tracker Functions | Using custom tracker functions | Record Any Object (Advanced Debugging)

Introduction | Cumulative Edgelist | Using the Cumulative Edgelist | Accessing the Cumulative Edgelist | For a Specific Network | For Multiple Networks | Contact Tracing

Definition | Probability of epidemic | Empirical superspreading | References

Definitions | Exploring each method

Use case | Library of epidemiological parameters | Single set of epidemiological parameters | Benefit of | Subsetting database | Distribution functions | Plotting epidemiological distributions | Accessors | Parameter conversion and extraction | Conversion | Extraction | Adding library entries and contributing to

Conversion versus extraction | Conversions | Conversion functions | Gamma distribution | Using a character string to name distribution | Using an | Lognormal distribution | Weibull distribution | Negative binomial distribution | Geometric distribution | Extraction | Use cases | Assuming distributions | References

1. What is an endemic channel? | The central tendency measure (CTM) | The upper and lower limits | 2. Historical data needed to build an endemic channel | Historic incidence from the line list or SIVIGILA data | Setting up the data from incidence information | 3. Using epiCo's endemic_channel function | Example | 4. Interpretation and communication of results

Introduction | General flow of the models | Computing probabilities | Transmission probability | Adjusted probabilities | Transmission in connected models[^models]

Introduction | An SEIR model with mixing | Investigating the history

1. Navigating the Codification of the Political Administrative Division of Colombia (DIVIPOLA) | 2. Population pyramids | 3. Demographic variables | 4. Epidemiological data | 5. Estimation of incidence rates | 6. Estimation of risk by age group

plot probability of epidemic across dispersion | plot probability of epidemic across introductions | plot probability of extinction across R for multiple k | Controls on transmission | plot probability of epidemic across introductions for multiple k

Transmission data | Superspreading using alternative distributions | References

What does it do? | Functions at a glance | Basic Usage | Computing incidence from a linelist | Computing incidence from pre-aggregated data | Plotting in style of European Programme for Intervention Epidemiology Training (EPIET) | Support for tidy-select semantics | Working with incidence objects | regroup() | complete_dates() | keep_first(), keep_last() and keep_peaks() | Bootstrapping and estimating peaks | cumulate() | Building on incidence2 | Class preservation | Accessing variable information | Grouping aware methods | Example fitting a poisson model | Example - Adding a rolling average

Model class | Data structure | Modeling and inference | Simulation

Introduction | Surveillance data | Model formulation | Function call and control settings | Conclusion

Model class | Data structure | Modeling and inference | Simulation

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

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

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

SARS-CoV-2 variants

Overview | Available Data | List Versions Functions | List Countries and Species Functions | List Administrative Units | List Raster Function | Is Available Functions | Downloading & Visualising Data: | get* functions & autoplot methods | Parasite Rate Survey Points | Vector Survey Points | Shapefiles | Modelled Rasters | Combined visualisation

1. Real travel times among Colombian municipalities | 2. Defining a neighborhood with epiCo | 3. epiCo's morans_index function | 4. Interpretation and communication of epiCo's morans_index results

Overview | Analyzing plausibility of forecast data | Prepare observed data | Convert observed data into a signal with to_signal() | Create a seed from the observed data via plane_seed() | Prepare forecast data | Convert forecast data into a signal with to_signal() | Run plane_score() and visualize results | Analyzing plausibility of observed data

Overview | Data preparation | Difference - plane_diff() | Flagged as implausible | Not flagged as implausible | Coverage - plane_cover() | Taper - plane_taper() | Repeat - plane_repeat() | Controlling sensitivity with parameters | Trend - plane_trend() | Shape - plane_shape() | Zero - plane_zero()

Overview | Weighting scheme within plane_score() | Motivations for weighting | Limitations that arise from seed data | Reliability of seed data | Length of seed data | What to do when a flag is raised | Summary

Use case | What we have | Severity of the 1976 Ebola Outbreak | Onset-to-death delay distribution | Intermediate step: Estimating cases with known outcomes | Estimating the naive and corrected CFR | Severity estimation methods | Severity of the COVID-19 pandemic in the U.K. | Onset-to-death distribution for Covid-19 | Details: Adjusting for delays between two time series | References

Use case | What we have | What we assume | Case and death data | Obtaining data on reporting delays | Estimate disease severity | Estimate ascertainment ratio | Concept: How reporting delays bias CFR estimates | References

Use case | What we have | Potential reasons for changing disease severity | Changing severity of the Covid-19 pandemic in the U.K. | Preparing the raw data | Onset-to-death distribution for Covid-19 | Estimating the naive and corrected CFR | Severity of Covid-19 in multiple countries | Details: Adjusting for delays between two time series | References

Use case | What we have | Ascertainment for the Covid-19 pandemic in the U.K. | Estimating the proportion of cases that have been ascertained | Ascertainment in countries with large early Covid-19 pandemics | References

Branching processes | Simulation | Summary statistics | Inference | Size and length distributions for some offspring distributions | Negative binomial and special cases | Size distributions | Length distributions | Gamma-Borel mixture | Numerical approximations of chain size and length distributions

Introduction | Data preparation | Estimation | Prediction | Competing risks

Scope | Input/Output/Interoperability | Design decisions | Dependencies

Bibliography

Use case | What we have | What we assume | Getting $R_0$, contact and demography data, and susceptibility | Running final_size over $R_0$ samples | Iterate final_size() | With base R | With | Visualise uncertainty in final size

Use case | What we have | What we assume | Modelling the initial epidemic | Modelling waning immunity and influx of susceptibles | Projected estimates of effective $R$ | References

Transmission chains likelihoods | Use case | What we have | What we assume | likelihood() | Joint and individual log-likelihoods | Observation probability | How likelihood() works | Transmission chain simulation | simulate_chains() | simulate_chain_stats() | S3 Methods | Summarising | Aggregating | Plotting | Some notes about interoperability between and objects | References

Example data: simulated Ebola outbreak | The plot.incidence function | Default behaviour | Changing colors | The default palette | Using different palettes | Specifying colors manually | Example 1: changing a single color | Example 2: changing several colors (note that naming colors is optional) | Example 3: using color to highlight specific groups | Useful ggplot2 tweaks | Changing dates on the x-axis | Changing date format | Changing the grid | Handling non-ISO weeks | Labelling every bin | Changing the legend | Applying the style of European Programme for Intervention Epidemiology Training (EPIET) | Display individual cases

Installing the package | What does it do? | Worked example: simulated Ebola outbreak | Loading the data | Computing and plotting incidence | Handling incidence objects | Modelling incidence

Example: Case Count data | Example: lineList data | Additional functionality | Specifying the distribution for caseCounts_line

Overview | A first example | Varying probability of picking individuals

Use case | What we have | What we assume | Primer on heterogeneous susceptibility | Getting $R_0$ and contact and demography data | Susceptibility variation between age groups | Calculate the effective $R_0$ | Calculate the final epidemic size | Susceptibility variation within and between age groups | Getting data on within-group susceptibility differences | Heterogeneous susceptibility without social contact heterogeneity | References

From SIR to final size with homogeneous mixing | Varying susceptibility | Final size equation with heterogeneous mixing | References

Using the documentation | Signal metadata | Interactive tooling | Example Queries | COVIDcast Main Endpoint | Other Covid Endpoints | COVID-19 Hospitalization: Facility Lookup | COVID-19 Hospitalization by Facility | COVID-19 Hospitalization by State | Flu Endpoints | Delphi's ILINet forecasts | FluSurv hospitalization data | Fluview data | Fluview virological data from clinical labs | Fluview metadata | Google Flu Trends data | ECDC ILI | KCDC ILI | NIDSS Flu | ILI Nearby Nowcast | Dengue Endpoints | Delphi's Dengue Nowcast | NIDSS dengue | PAHO Dengue | Other Endpoints | Wikipedia Access | Private methods | CDC | Dengue Digital Surveillance Sensors | Google Health Trends | NoroSTAT metadata | NoroSTAT data | Quidel Influenza testing | Sensors | Twitter

Data known "as of" a specific date | Multiple issues of observations | Observations issued with a specific lag

Example 1: Plain text

Scope of regulations | Data privacy and integrity | Internet access | Registration and third parties | Updates

Introduction | Loading and Parsing the Data | Exploring the data

Introduction | Critical assumption of nosoi | Essential building blocks: 3 probabilities and 2 numbers | What happens at each time step? | Setting up the core functions | Expected output | Functions' arguments | t | prestime | current.in and current.env.value | host.count | Extra Parameters | Combining arguments | Going further

nosoiSim object | table.hosts | table.state | Epidemiological insights | Epidemiological summary | Epidemiological dynamics | $R_0$ | Transmission chains | Full transmission chain | Sampling the transmission chain | Exporting the Transmission Tree

Structure of the population | Continuous space | Movement kernel | Setting up the simulation | General parameters | Core functions | pExit, param.pExit, timeDep.pExit, diff.pExit and hostCount.pExit | pMove, param.pMove, timeDep.pMove, diff.pMove and hostCount.pMove | sdMove, param.sdMove, timeDep.sdMove, diff.sdMove and hostCount.sdMove | nContact, param.nContact, timeDep.nContact, diff.nContact and hostCount.nContact | pTrans, param.pTrans, timeDep.pTrans,diff.pTrans and hostCount.pTrans | Miscellaneous | Dual host | Running nosoi | Single host | pExit | pMove | sdMove | nContact | pTrans | Running | pExit.B | pMove.B | sdMove.B | nContact.B | pTrans.B | Going further

Structure of the population | Setting up the simulation | General parameters | Core functions | pExit, param.pExit, timeDep.pExit, diff.pExit and hostCount.pExit | pMove, param.pMove, timeDep.pMove, diff.pMove and hostCount.pMove | nContact, param.nContact, timeDep.nContact, diff.nContact and hostCount.nContact | pTrans, param.pTrans, timeDep.pTrans,diff.pTrans and hostCount.pTrans | Miscellaneous | Dual host | Running nosoi | Single host | pExit | pMove | nContact | pTrans | Running | pExit.B | pMove.B | nContact.B | pTrans.B | Going further

Extract trajectory data with trajectory() | Calculate prevalence from a trajectory using prevalence() | Visualize a trajectory with plot()

Overview | Analysis steps | Example data | Usage | Inputs and outputs | Launching the app

Use case | What we have | What we assume | Primer on susceptibility matrices | Susceptibility matrix | Demography-in-susceptibility matrix | Homogeneous susceptibility | Case 1: Uniform susceptibility | Heterogeneous susceptibility | Case 2: Susceptibility varies between groups | Case 3: Susceptibility varies both between and within groups | Case 4: Susceptibility varies within groups in different proportion | Case 5: Susceptibility varies within three groups | References

Use case | What we have | What we assume | Defining a value for $R_0$ | Heterogeneous social mixing | Getting and preparing contact and demography data | Population susceptibility | Why susceptibility and p_susceptibility are matrices | Running final_size | Visualise final sizes | Final size proportions to counts | References

Setup | Installing the package | Load the package | Direct adjusted rate | Events and population data | Reference population data | Rate computation | Indirect adjusted rate | Events and population reference data

Package Epistats | Description | Cohort study functions: | Case control study functions: | The "Tiramisu" dataset | Working with Epistats and "Tiramisu" dataset | Loading and recoding the dataset | crossTable | Syntax | Examples | Example 1: crossTable ill - tira | Example 2: crossTable ill - sex with column percentage and chi2 stat | Example 3: CrossTable ill - sex with row percentage and Fisher stat | CrossTable beer - sex with column and row percentages and Chi2 stat | CS | Example 1: CS ill - mousse (unformatted) | Example 2: CS ill - beer (formatted) | CSTable - Summary table for cohort studies | Example 1: CSTable results ordered by p-value (unformatted) | Example 2: CSTable results ordered by risk ratio (formatted) | Example 3: CSTable results ordered by p-value from the Fisher's exact test (formatted) | CSInter - Stratified analysis for cohort studies | Example 1 : CSInter ill - wmousse by tira (unformatted) | Example 2 : CSInter ill - beer by tira (formatted) | Example 3: CSInter ill - beer by tportion (formatted) | CC | Example 1: CC ill - mousse (unformatted) | Example 2: CC ill - beer (formatted) | CCTable - Summary table for case control studies | Example 1: CCTable results ordered by p-value (unformatted) | Example 2: CCTable results ordered by odds ratio (formatted) | Example 3: CCTable results ordered by p-value from the Fisher's exact test (formatted) | CCInter - Stratified analysis for case control studies | Example 1: CCInter ill - wmousse by tira (unformatted) | Example 2: CCInter ill - beer by tira (formatted) | Example 3: CCInter ill - beer by tportion (formatted)

Description | Installation | Usage | Import and export | Clean strings | Download administrative names | Clean administrative names | Create unit testing functions | Consistency check | Detecting and handling outliers | Checking missing data and reporting rates over factors

Selection Bias | Misclassification Bias | Uncontrolled Confounders | Bootstrapping

Summary | Package description | Intended audience and goal of the package | Pre-requisites | Installation | Using the package | Level 1: Interactive use through the graphical user interface | Load and run the package | Level 2: Directly interacting with the simulation functions | Level 3: Modifying the simulation functions | Contributing to the package | Further information | Contributors | Citation | References

The packages | Documentation | The TypeScript ecosystem | Roadmap

Estimate R~t~ from temporally aggregated incidence data | estimate_R() for aggregated data | Estimate R~t~ from weekly COVID-19 data | References

Introduction | Preliminaries | LR and GLR-charts | Control-settings | Discussion

Model class | Data structure | Modeling and inference | Simulation

Array Approach for Unmeasured Confounders | Simple Sensitivity Analyses in the Absence of External Information: Array Approach | Using Additional Information: External Adjustment | Unmeasured Confounding and E-value

Single variable: Logistic growth | Specifying parameters | More than one variable: the Lorenz attractor | Delay models | Arrays | Generalised Lotka-Volterra model | Interpolating functions

Introduction to the data | Loading the data and the code | Running an analysis

Ordinary differential equation models | Models implemented in R | Dense output and interpolation | Delay differential equation models | A model in R | Differences with deSolve | Models implemented in C

Summary table of other R packages and tools

SIR model definition | Final size in a uniformly mixing population | Final size with heterogeneous mixing | Prepare population data | Susceptibility varies between groups | Susceptibility varies within groups in different proportions | Complete immunity to infection in part of the population

Using print() | print format strings | Conditional display | Controlling precision | Current limitations

Setup and usage | Return values | Parametrization | Country | Raw data | Date filter | Level | Cache | Vintage | Citations | Star the repo

Quickstart | Usage | Level | Country | Time range | Vintage | Download folder | World Bank Open Data | Google Mobility Reports | Apple Mobility Reports | Reference manual | Star the repo

Setting up | Dependencies | Loading package functions

Overview | Managing Git Credentials | Clone the EpiForsk repository | Contribution workflow | Additional

What does it do? | Fitting a gamma distribution to delay data | Converting a growth rate (r) to a reproduction number (R0) | Standardising labels | Anonymising data

Description | Example

Discrete compartmental models in a nutshell | From continuous to discrete time | Stochastic processes | Binomial distribution | Poisson distribution | Multinomial distribution | Implementation using odin | Deterministic SIR model | Stochastic SIR model | A stochastic SEIRDS model

Basic operators | Array support | Operators | Mathematical operators | Stochastic models

Introduction | Splines | Knots | SMR | Mortality: External cohort and popmort data | splines

Introduction | Epidemiological modeling | Technical description of the simulation framework | Model construction and data analysis: Basic examples | Extending SimInf: New models | Benchmark | Conclusion | Acknowledgments | Pseudo-code for the default simulation solver | Illustration of scheduled events | C code for the SIR model

Introduction | Scenarios | Scenario Definitions | Scenarios with Parameter Vectors | Parameters Input Via Table | Random Parameters | The Model | Adding Random Parameters | Generator Functions | Parameter Sets | Time-Varying Parameters and Control Settings (Advanced) | Parameter Updaters | Incorporating Updaters | Verbosity | Relative Parameter Changes | Time-Varying Control Settings

Customising priors | Customizing likelihood | A null model | A model using symptom onset only | Customising movements | Movements in outbreaker2 | A (fake) Gibbs sampler for mu | A new movement of ancestries

Introduction to riskCommunicator | Getting started | Installation | Description of main package function | Framingham Heart Study | Binary outcome example | Checking model fit | In the manuscript, we compare the results of gComp to standard regression models. Here, we show how to do that so we can re-create Table 2. | Rate outcome example | Rate outcome with subgroups example | The results.df component of the gComp function output is formatted as a data.frame. This makes it very easy to immediately plot the results using ggplot2 or your favorite R plotting functionality. Here's an example for plotting the results of the different subgroups (sexes) for the rate example above. | Let's make a figure comparing the results we got above using riskCommunicator with those we get using standard regression models for this rate example.

Introduction to riskCommunicator | Getting started | Installation | Description of main package function and accessing package documentation | Preparing your data | Vignette with the Framingham Heart Study | Binary (dichotomous) outcome example | Categorical exposure example | Continuous exposure example | Rate outcome example | Continuous outcome example | Count outcome example | Checking model fit | Guidance on number of bootstraps | What's going on under the hood

Checking convergence | Interpretation of output

Identifiers | Epidemiological variables | Policy measures | Administrative areas | Coordinates | ISO codes | External Keys

Main features | Example usage | Setup | A succesful model fit | Example of failed model fitting | Multiple models

Overview | Confidence interval | Prediction interval | Comparison to a bootstrap approach | setup | Example 1 - Poisson | Example 2 - Quassipoisson

Data types | Record-level data | Count-level data | Computing Expected counts | Computing event effects | Daily data

Package EpiCurve | Description | Details | The EpiCurve function | Arguments | Depends | Plot non-aggregated cases | Daily - non-aggregated cases | Hourly - non-aggregated cases | Hourly - non-aggregated cases with factors | Plot aggregated data | Daily | Without factors | With factors | Weekly | Monthly | Converted period (aggragated cases) | "day" to "week" | "day" to "month" | "week" to "month" | "week" to "month" with factors

Installation | Data refresh | Italy summary | Italy region level | Italy province level

Probabilistic Sensitivity Analysis for Exposure Misclassification | Probabilistic Sensitivity Analysis for Uncontrolled Confounding

Base functions | Basic usage | Dataset | Linear regression using fit_lin() | Model fit stats | Model coefficients | Global stats | Model predictions | Plot of predictions | Publication-ready plots | Non-linear regression | Two-parameters | Estimating K (maximum disease) | Fit models to multiple DPCs | Data | Using fit_multi() | Want to estimate K? | Graphical outputs | DPCs and fitted curves | Apparent infection rate | References

Introduction | Hands On | Packages | The basics of the simulation | Exponential | Monomolecular | The Logistic model | Gompertz | Combo | References

Introduction | Analytical Paradigm | Using functions incprops and inccounts | Examples | Function prevcounts

Introduction | Historical context | Overview of Functionality and Use | Analysis in support of survey design | Analysis of survey data | Estimation of key performance characteristics of Tests for Recent Infection | Glossary | HIV infected individual | Mean duration of recent infection (MDRI) | False-recent rate (FRR) | Incidence as a (or, an “instantaneous”) rate | Annual(ised) risk of infection | Recency time cut-off T (“Big T”) | Relative standard error (RSE) | Null hypothesis | p-value | Significance | Sample size | Design effect | Citation | Acknowledgements | Appendix: Technical details | Case 1: Surveys share a single estimate for MDRI and a single estimate for FRR | Case 2: Surveys share a single estimate for MDRI, but have independent FRR estimates | Case 3: Both MDRI and FRR are independently estimated in each survey | Note: | Crucial note on choice of null hypothesis, calculation of p-values and meaning of “statistical power”

Function incpower | Examples | Function incprecision

Introduction | Estimating MDRI using binomial regression | Example of mdrical using the complementary log-log functional form and pre-classified data | Example of mdrical using the logit cubic functional form and two independent thresholds on biomarkers | Obtaining fitting parameters for bootstrap iterations | Estimating FRR using binomial proportions with frrcal

Description | Background | 1. Datasets to be used in the Epidemiological Report package | 1.1. Disease dataset specification | 1.2. Report parameters dataset specification | 1.3. Member States correspondence table dataset | 2. How to generate the Epidemiological Report in Microsoft Word format | 2.1. External disease dataset | 2.2. Word template | 2.3. Word bookmarks | 3. How to generate each epidemiological outputs independently | 3.1. Table: distribution of cases by Member State | 3.2. Seasonality plot: distribution of cases by month | 3.3. Trend plot: trend and number of cases by month | 3.4. Maps: distribution of cases by Member State | 3.5. Age and gender bar graph

Running the analysis with defaults | Analysing the results | Graphics | Using summary | Customising settings and priors

Installing the package | Main visible functions of the package | Main internal functions

List of available functions | Function signatures | Arguments

Exporting results | Importing pre-computed incidence

Structure of an incidence object. | $dates | $counts | $timespan | $interval | $n | $weeks

Posterior Predictive Forecasting | Model Selection

Data structure | Query and summarise the data | Cases summary by canton | Calculating rates for Canton of Geneva | Separating between Switzerland and Principality of Liechtenstein

Introduction | Downloading and preparing the data | Annotate the mutational events | Merge neighboring events of SNP, insertion and deletion | Provide effects of each SNP, insertion and deletion | Mutation statistics and profile | Basic descriptions for the mutational events. | The most frequent mutational events for each protein | Preprocess nucmer object to add group information | Global SNP profiling in virus genome | Global protein mutational events profiling | Plot mutation distribution and mutation statistics | Plot mutation counts for certain genes | Assays efficiency | Calculate the mutation ratio using different assays | Bacth assay analysis for last five Nr of primers | Detection of co-occurring mutations using double-assay | Packages used | References

Abstract | Introduction | Implementation | Overall structure | Likelihood and priors | Genotype | Conditional report ratio | Time | Space | Age | Overall likelihood | Tree proposals | Tutorial | Part one: First models and outputs | Run the models with `outbreaker()' | Model evaluation | Interpretation of the results | Part two: Modify the likelihoods, priors and movements lists: the Stouffer's rank module | Conclusion

The update_swiss_data function | Reading the data from the CSV version

Overview | Installation | Package Version | Naming Convention | Dataset Contents | Dataset Hierarchy

Introduction | Creating a Dataset of Pairs | Estimating Relative Transmission Probabilties | Covariates Associated with Probable Transmission | Table of contribution of covariates | Forest plot of contribution of covariates | Finding the Most Likely Infectors | Clustering Illustration | Visualizing Results | Network with all pairs | Network with only top probability pairs | Heatmap with stars on top cluster | Estimating the Reproductive Number | Estimating the Serial Interval | Conclusion

Simulated Data | Run single and multiple tree inference algorithm | Compare parameter estimates

Setting up the simulation | General parameters | Core functions | pExit, param.pExit and timeDep.pExit | nContact, param.nContact and timeDep.nContact | pTrans, param.pTrans and timeDep.pTrans | Miscellaneous | Dual host | Running nosoi | Single host | pExit | nContact | pTrans | Running | pExit.B | nContact.B | pTrans.B | Going further

Basic information | Author and maintainer | Contributors | Citing this package | Getting help | Recent updates | Quick start example | Model specification | User-defined models | Built-in models | SIR | SIR with demography | SIR with vaccination at birth | SIS | SIS with demography | SIRS | SIRS with demography | SIRS with vaccination | More detailed examples | References

Overview | Using survtab | Relative/net survival | Adjusting estimates | Other survival time functions | Using survtab_ag

Structure of an incidence_fit object | Building an incidence_fit object from scratch | Step 1: create the model | Step 2: creation of the $info list: | Step 3: combine lists and create object | Structure of an incidence_fit_list object | Example: A tale of two fits | Example: Nested incidence_fit

Installing the package | What does it do? | Estimation of risk | Example | Data structure to store flows and metadata | Basic methods | Plotting | Accessors | References

Introduction | Construction of the epiflows object from data frames | Using set_vars() to update variable keys in the object | Example: different durations of stay | data preparation | Using set_vars() | Using a custom argument | References

Data | Contingency tables | Adding supplementary information | Column-wise measures | Row-wise measures | Tips | Publication quality tables | neat_table to HTML and PDF | kable | Word | Custom summary functions | Cross-tabulated | Column-wise | Row-wise

Abstract | Introduction | Data sets | Conceptual framework | Statistical methods | Aggregation indices | Distribution fitting | Power law | Spatial hierarchy | Spatial Analysis by Distance IndicEs (SADIE) | Map comparison (MAPCOMP) | Conclusion | Acknowledgments | Annexes | References

Variable definitions | Parameters of the beta-binomial distribution | Definitions | Base relationships | Parameters of the binary power law | Two possible formulas for parametrization | Relationship tables | Relationship between beta-binomial and binary power law parameters | References

make_epicontacts() | $linelist | $contacts | Generics | print.epicontacts() | summary.epicontacts() | subset.epicontacts() | plot.epicontacts()

Overview of package | Basic example of using the package | Piece-by-piece breakdown of package utility | Generating exposure data | Generating outcome data | Fitting models | Evaluating the models | Variance across estimated log relative risk and mean variance of estimates | Relative bias | Generating power curves | Using custom functions | Customizing the exposure trend | Customizing the outcome simulation | References

Introduction | Loading Data | Example Dataset | Creating epicontacts Object | Data Manipulation | Access Unique Identifiers | Subsetting | Visualisation | Default Plotting Method | 3D plots | Analysis | Extract Characteristics of Pairwise Nodes | Identify Clusters | Calculate Degree | References