Prepare population and initial conditions | Examine the baseline | Modelling overlapping interventions | School closures | Workplace closures | Combining interventions | Re-applying workplace closures

Scope | Output | Package architecture | Design decisions | Epidemic modelling | ODE systems and models | Stochastic models | Classes | Function vectorisation | Miscellaneous decisions | Dependencies | Contribute

Modifications for epidemics | Prepare population and initial conditions | Prepare a two dose vaccination campaign | Model epidemic using Vacamole | Visualise model outcomes | Vacamole ODE system for | References

Prepare population and initial conditions | Prepare a vaccination campaign

Prepare population and initial conditions | Note on social contacts data | Run epidemic model | Prepare data and visualise infections | References

Prepare population and initial conditions | Note on social contacts data | Prepare an intervention | Run epidemic model | Prepare data and visualise infections | References

Modelling an outbreak with pre-existing immunity | Modelling an outbreak with changing population sizes | References

Combining two populations | Note on connectivity matrix | Combining two populations using a gravity model | Note on gravity connectivity matrix | Combining n populations using a gravity model

Which model components can be passed as lists | Setting up the epidemic context | Creating a list of intervention sets | Output type for list intervention inputs | Combinations of intervention and vaccination scenarios | Modelling epidemic response scenarios with parameter uncertainty | Output type for intervention and parameter set combinations | Comparing response scenarios with parameter uncertainty | Counter-intuitive effects of time-limited interventions | References

Prepare population and initial conditions | Modelling an intervention on the transmission rate

Obtaining estimates of disease transmission rate | Passing a vector of transmission rates | Output type for vector parameter inputs | Passing parameter sets | Passing vectors of epidemic duration | References

Prepare population and initial conditions | Prepare model parameters | Run epidemic model | Prepare data and visualise infections | Applying interventions that reduce transmission | Modelling the roll-out of vaccination | Modelling a multi-pronged ebola response | Details: Discrete-time Ebola virus disease model | Hospitalisation, funerals, and removal | References

Setup and initial conditions | Defining a time-dependent function | Model with time-dependent transmission | Non-pharmaceutical interventions and time-dependence | Timing vaccination to prevent epidemic peaks

Different use cases of finalsize and epidemics | Converting scenarios between finalsize and epidemics | Prepare population and model parameters | Implementing vaccination in epidemics | Calculating individuals vaccinated in epidemic model | Implementing vaccination in finalsize | Consideration of computational speed | References

Population-wide risks

Uniform population age | Structured population age

Introduction | RDBMS query parameters | Examples | DHIS2 query parameters | SORMAS query parameters

Overview | Need for MS drivers | Authentication | Reading data from RDBMS | Reading data from HIS | Importing data from DHIS2 | Importing data from SORMAS

Concept and motivation | Scope | Output | Design decisions | Authentication | Data import | Dependencies | Contribute

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

Simulate an outbreak | Censoring dates | Under-reporting of cases and contacts | Tidyverse | Base R | Removing a line list column

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

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

MS SQL drivers for OSX-based systems | MS SQL drivers for Linux-based systems

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

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 | References

Scope | A guide to package structure | Adding or removing model parameters | Modifying compartmental flows without changing compartments | Modelling births, immigration, and background mortality | Modelling sources of infectious individuals such as from zoonotic spillover_rate | Modelling waning immunity | Modifying which parameters can be time dependent | Group-specific infection parameters | Adding epidemiological compartments | Changing vaccination rates over time

Transmission data | Superspreading using alternative distributions | References

An overview | General data cleaning tasks | Using {cleanepi} functionalities with pipe operators | Printing the report | 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 | Correct misspelled values | Calculating time span in different time scales (“years”, “months”, “weeks”, or “days”)

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

Scope | Output | Design decisions | Dependencies | Contribute

Definition | Probability of epidemic | Empirical superspreading | References

Definitions | Exploring each method

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

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

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

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

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() ✘

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

Setup | Scenarios of contact reduction

Scope | Output | Design Decisions | Dependencies | Additional Considerations

Initial Exploration: Basic Data Handling with ColOpenData | Documentation access | Data load | Data filter and plot

Naming and structure | Understanding Demographic Datasets | Understanding Geospatial Datasets | Understanding Climate Dataset | Understanding Population Projections | List Data | List Data Using Keywords | Geospatial dictionaries | Climate tags | DIVIPOLA

Retrieving climate data for a ROI using stations' data | Other methods | Disclaimer

Downloading geospatial data | Static plots (ggplot2) | Dynamic plots (leaflet)

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

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

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

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

How to merge geospatial and demographic data | Documentation access

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

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

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

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

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

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

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

Colours | Sizes of images | Capitalising axes | Axes | Multipanel Figures | Number of lines/items per plot | Defining standard geoms | Display of uncertainty | Bayesian | Frequentist | Formatting for age group intervals | Alt-text | Flexibility

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

Scope | Input/Output/Interoperability | Design decisions | Alignment with Epiverse-TRACE web theme | Accessibility requirements | Dependencies

Line graphs/time series | Scatter plots | Bar / column charts | Maps | Distribution plots (parametric and non-parametric) | Densities | Heatmaps | Networks | Concept maps / flowcharts

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

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

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