CRAN Task View: Epidemiology

Contributors (in alphabetic order): Neale Batra, Solène Cadiou, Christopher Endres, Rich FitzJohn, Hugo Gruson, Andreas Handel, Michael Höhle, Thibaut Jombart, Joseph Larmarange, Sebastian Lequime, Alex Spina, Tim Taylor, Sean Wu, Achim Zeileis.

Overview

R is increasingly becoming a standard in epidemiology, providing a wide array of tools from study design to epidemiological data exploration, modeling, forecasting, and simulation. This task view provides an overview of packages specifically developed for epidemiology, including infectious disease epidemiology (IDE) and environmental epidemiology. It does not include:

• generic tools which are used in these domains but not specifically developed for the epidemiological context,
• ‘omics’ approaches and genome-wide association studies (GWAS), which can be used in epidemiology but form a largely separate domain.

Packages are grouped in the following categories:

1. Data visualization: tools dedicated to handling and visualization of epidemiological data, e.g. epidemic curves (‘epicurves’), exploration of contact tracing networks, etc.
2. Infectious disease modeling: IDE-specific packages for the analysis of epidemic curves (including outbreak detection / surveillance), estimation of transmissibility, short-term forecasting, compartmental models (e.g. SIR models), simulation of outbreaks, and reconstruction of transmission trees
3. Environmental epidemiology: tools dedicated to the study of environmental factors acting as determinants of diseases
4. Helpers: tools implementing miscellaneous tasks useful for practicing as well as teaching epidemiology, such as sample size calculation, fitting discretized Gamma distributions, or handling linelist data.
5. Data packages: these packages provide access to both empirical and simulated epidemic data; includes a specific section on COVID-19.

Additional links to non specific but highly useful packages (to create tables, manipulate dates, etc.) are provided in the task view’s footnotes.

Inclusion criteria

Packages included in this task view were identified through recommendations of expert epidemiologists as well as an automated CRAN search using pkgsearch::pkg_search() with the keywords: epidemiology, epidemic, epi, outbreak, and transmission. The list was manually curated for the final selection to satisfy the conditions described in the previous paragraph.

Packages are deemed in scope if they provide tools, or data, explicitly targeted at reporting, modeling, or forecasting infectious diseases.

Your input is welcome! Please suggest packages we may have missed by filing an issue in the GitHub repository or by contacting the maintainer.

Data visualization

This section includes packages providing specific tools for the visualization and exploration of epidemiological data.

• epicontacts: Implements a dedicated class for contact data, composed of case line lists and contacts between cases. Also includes procedures for data handling, interactive graphics, and characterizing contact patterns (e.g. mixing patterns, serial intervals). RECON package.
• EpiContactTrace: Routines for epidemiological contact tracing and visualization of networks of contacts.
• EpiCurve: Creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.
• epiDisplay: Package for data exploration and result presentation.
• epiflows: Provides functions and classes designed to handle and visualize epidemiological flows of people between locations. Also contains a statistical method for predicting disease spread from flow data initially described in Dorigatti et al. (2017). RECON package.
• EpiReport: Drafting an epidemiological report in ‘Microsoft Word’ format for a given disease, similar to the Annual Epidemiological Reports published by the European Centre for Disease Prevention and Control.
• incidence: Functions and classes to compute, handle and visualize incidence from dated events for a defined time interval, using various date formats. Also provides wrappers for log-linear models of incidence and estimation of daily growth rate. RECON package. This package is scheduled for deprecation and is replaced by incidence2.
• incidence2: Provides functions and classes to compute, handle and visualize incidence from dated events. Improves the original incidence package in many ways: full flexibility in time intervals used, allows multiple stratifications, and is fully compatible with dplyr and other tidyverse tools. RECON package.
• i2extras: Provides functions to work with ‘incidence2’ objects, including a simplified interface for trend fitting, estimation of growth rates, and peak estimation. RECON package.

Infectious disease modeling

This section includes packages for specifically dedicated to IDE modeling. Note that R offers a wealth of options for general-purpose time series modeling, many of which are listed in the TimeSeries and Survival task views.

Epidemics surveillance

Packages below implement surveillance algorithms, but these approaches can be usefully complemented by spatial analyses. We recommend looking at the Spatial task view, which has a dedicated section on disease mapping and areal data analysis.

• argo: Augmented Regression with General Online data (ARGO) for accurate estimation of influenza epidemics in United States on both national level and regional level. It replicates the method introduced in paper Yang, S., Santillana, M. and Kou, S.C. (2015) and Ning, S., Yang, S. and Kou, S.C. (2019).
• Epi: Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data, in particular representation, manipulation and simulation of multistate data - the Lexis suite of functions, which includes interfaces to mstate, etm and cmprsk packages. Also contains functions for Age-Period-Cohort and Lee-Carter modeling, interval censored data, tabulation, plotting, as well as a number of epidemiological data sets.
• episensr: Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Lash T.L., Fox M.P., and Fink A.K. “Applying Quantitative Bias Analysis to Epidemiologic Data”, (‘Springer’, 2009).
• mem: The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015), allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week.
• memapp: The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015), allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Enables the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. It also gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. ‘memapp’ is a web application created in the Shiny framework for the mem R package.
• nosoi: The aim of nosoi (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. 2020). The package can take into account the influence of multiple variables on the transmission process (e.g. dual-host systems such as arboviruses, within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.
• riskCommunicator: Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting. It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) and has been described in detail by Ahern, Hubbard, and Galea (2009); Snowden, Rose, and Mortimer (2011); and Westreich et al. (2012).
• RSurveillance: Provides a diverse set of functions useful for the design and analysis of disease surveillance activities.
• trendeval: Provides a coherent interface for evaluating models fit with the trending package. RECON package.
• SpatialEpi: Methods and data for cluster detection and disease mapping.
• surveillance: Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the flexible covariate based regression modeling of spatio-temporal point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) and a recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017). Also contains back-projection methods to infer time series of exposure from disease onset and correction of observed time series for reporting delays (nowcasting).
• trendbreaker implements tools for detecting changes in temporal trends of a single response variable. It implements the Automatic Selection of Models and Outlier Detection for Epidemmics (ASMODEE), an algorithm originally designed for detecting changes in COVID-19 case incidence. RECON package.
• trending: Provides a coherent interface to multiple modeling tools for fitting trends along with a standardized approach for generating confidence and prediction intervals. RECON package.

Estimation of transmissibility

• earlyR: Implements a simple, likelihood-based estimation of the reproduction number (R0) using a Poisson branching process. This model requires knowledge of the serial interval distribution, and dates of symptom onsets. It is a simplified version of the model introduced by Cori et al. (2013).
• endtoend: Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.
• EpiEstim: Provides tools for estimating time-varying transmissibility using the instantaneous reproduction number (Rt) introduced in Cori et al. (2013).
• epimdr: Functions, data sets and shiny apps for “Epidemics: Models and Data in R” by Ottar N. Bjornstad (ISBN 978-3-319-97487-3). The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course.
• epinet: A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
• EpiNow2: Provides tools for estimating the time-varying reproduction number, rate of spread, and doubling time of epidemics while accounting for various delays using the approach introducted in Abbott et al. (2020), and Gostic et al. (2020).
• nbTransmission: Estimates the relative transmission probabilities between cases using naive Bayes as introduced in Leavitt et al. (2020). Includes various functions to estimate transmission parameters such as the generation/serial interval and reproductive number as well as finding the contribution of covariates to transmission probabilities and visualizing results.
• R0: Estimation of reproduction numbers for disease outbreak, based on incidence data including the basic reproduction number (R0) and the instantaneous reproduction number (R(t)), alongside corresponding 95% Confidence Interval. Also includes routines for plotting outputs and for performing sensitivity analyses.
• tsiR: The TSIR modeling framework allows users to fit the time series SIR model to cumulative case data, which uses a regression equation to estimate transmission parameters based on differences in cumulative cases between reporting periods. The package supports inference on TSIR parameters using GLMs and profile likelihood techniques, as well as forward simulation based on a fitted model, as described in Becker and Grenfell (2017).

Compartmental models

• EpiILM: Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).
• EpiILMCT: Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. An overview of the implemented continuous-time individual level models for epidemics is given by Almutiry and Deardon (2019).
• EpiModel: Tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018).
• odin: Provides a generic, fast and computer-efficient platform for implementing any deterministic or stochastic compartmental models (e.g. SIR, SEIR, SIRS, …), and can include age stratification or spatialization. It uses a domain specific language (DSL) to specify systems of ordinary differential equations (ODE) and integrate them. The DSL uses R’s syntax, but compiles to C in order to efficiently solve the system, using interfaces to the packages deSolve and dde.
• pomp Provides a large set of forward simulation algorithms and MLE or Bayesian inference techniques to work with state-space models. Models may be specified as either deterministic or stochastic and typically follow a compartmental model structure. Time may be either discrete or continuous, depending on the simulation algorithm chosen. Additionally models may be programmed in C and compiled on-the-fly into a format suitable for use in the package to speed up simulation and inference. The R package and some of its algorithms are described in King, Nguyen, and Ionides (2016).
• popEpi (archived): Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models, rates, mean survival times, relative survival, and standardized incidence and mortality ratios (SIRs/SMRs), all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of ‘Lexis’ objects (from package r pkg("Epi", priority = "core") and other computations achieved using r pkg("data.table").
• SimInf: Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and ‘OpenMP’ (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019).
• socialmixr: Provides methods for sampling contact matrices from diary data for use in infectious disease modeling, as discussed in Mossong et al. (2008).

Transmission tree reconstruction

• adegenet: primarily a population genetics package, adegenet implements seqtrack (Jombart et al. 2011), a maximum-parsimony approach for reconstructing transmission trees using the Edmonds/Chu-Liu algorithm
• o2geosocial (outbreaker2 module): Bayesian reconstruction of who infected whom during past outbreaks using routinely-collected surveillance data. Inference of transmission trees using genotype, age specific social contacts, distance between cases and onset dates of the reported cases (Robert A, Kucharski AJ, Gastanaduy PA, Paul P, Funk S. 2020).
• o2mod.transphylo is a module of outbreaker2 which uses the TransPhylo model of within-host evolution.
• outbreaker2: a modular platform for Bayesian reconstruction of disease outbreaks using epidemiological and genetic information as introduced in Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C and Ferguson N. 2014, [Campbell F, Cori A, Ferguson N, Jombart
• TransPhylo: Inference of transmission tree from a dated phylogeny. Includes methods to simulate and analyze outbreaks. The methodology is described in Didelot et al. (2014), Didelot et al. (2017).

Environmental epidemiology

Environmental epidemiology is dedicated to the study of physical, chemical, and biologic agents in the environment acting as determinants of disease. The aims of environmental epidemiology are to infer causality, to identify environmental causes of disease, such as from air and water pollutants, dietary contaminants, built environments, and others.

R packages dedicated to environmental epidemiology include tools dealing with limits of detection of pollutants (left-censoring issues), and various modeling approaches to account for multiple correlations between exposures and infer causality.

• NADA: Nondetects and Data Analysis for Environmental Data, package containing all the functions derived from the methods in Helsel (2011).
• EnvStats: Package for Environmental Statistics, Including US EPA Guidance, graphical and statistical analyses of environmental data, with focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. Major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. Numerous built-in data sets from regulatory guidance documents and environmental statistics literature (Millard 2013).
• bkmr: Implements Bayesian Kernel Machine Regression, a statistical approach for estimating the joint health effects of multiple concurrent exposures, as described in Bobb et al. (2015)
• mediation: Implements parametric and non parametric mediation analysis as discussed in Imai et al. (2010).
• mma: Implements multiple mediation analysis as described in Yu et al. (2017).
• HIMA: Allows to estimate and test high-dimensional mediation effects based on advanced mediator screening and penalized regression techniques ([Zhang et al. 2021])(https://doi.org/10.1093%2Fbioinformatics%2Fbtab564).

Helpers

This section includes packages providing tools to facilitate epidemiological analysis as well as for training (e.g. computing sample size, contingency tables, etc).

• DSAIDE: Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios. The purpose of the package is to help individuals learn about infectious disease epidemiology from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models.
• epibasix: Contains elementary tools for analyzing common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity). Appropriate print and summary statements are also written to facilitate interpretation wherever possible. The target audience includes advanced undergraduate and graduate students in epidemiology or biostatistics courses, and clinical researchers.
• epiR: Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modeling analyses.
• epitools: Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
• epitrix: A collection of small functions useful for epidemics analysis and infectious disease modeling. This includes computation of basic reproduction numbers (R0) from daily growth rates, generation of hashed labels to anonymize data, and fitting discretized Gamma distributions.
• linelist: Implements the linelist class for storing case line list data, which extends data.frame and tibble by adding the ability to tag key epidemiological variables, validate them, and providing safeguards against accidental deletion or alteration of these data to help make data pipelines more straightforward and robust.
• powerSurvEpi: Functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effects in the survival analysis of randomized clinical trials.

Data

Here are packages providing different epidemiologic datasets, either simulated or real, useful for research purposes or field applications with a specific COVID-19 section.

Epidemic outbreak data

• contactdata: Data package for the supplementary data in Prem et al. (2017). Provides easy access to contact data for 152 countries, for use in epidemiological, demographic or social sciences research.
• outbreaks: Empirical or simulated disease outbreak data, provided either as RData or as text files.

COVID-19

• bets.covid19: Implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020).
• corona: Manipulate and view coronavirus data and other societally relevant data at a basic level.
• coronavirus: Provides a daily summary of the Coronavirus (COVID-19) cases by state/province. Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus.
• COVID19: Download COVID-19 data across governmental sources at national, regional, and city level, as described in Guidotti and Ardia (2020). Includes the time series of vaccines, tests, cases, deaths, recovered, hospitalizations, intensive therapy, and policy measures by ‘Oxford COVID-19 Government Response Tracker. Provides a seamless integration with’World Bank Open Data‘,’Google Mobility Reports‘, ’Apple Mobility Reports](https://covid19.apple.com/mobility)’.
• covid19.analytics: Load and analyze updated time series worldwide data of reported cases for the Novel CoronaVirus Disease (CoViD-19) from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) data repository. The datasets are available in two main modalities, as a time series sequences and aggregated for the last day with greater spatial resolution. Several analysis, visualization and modeling functions are available in the package that will allow the user to compute and visualize total number of cases, total number of changes and growth rate globally or for an specific geographical location, while at the same time generating models using these trends; generate interactive visualizations and generate Susceptible-Infected-Recovered (SIR) model for the disease spread.
• covid19br: Set of functions to import COVID-19 pandemic data into R. The Brazilian COVID-19 data, obtained from the official Brazilian repository at https://covid.saude.gov.br/, is available at country, region, state, and city-levels. The package also downloads the world-level COVID-19 data from the John Hopkins University’s repository.
• covid19dbcand: Provides different datasets parsed from ‘Drugbank’ database using dbparser package. It is a smaller version from dbdataset package. It contains only information about COVID-19 possible treatment.
• covid19italy: Provides a daily summary of the Coronavirus (COVID-19) cases in Italy by country, region and province level. Data source: Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile.
• covid19france: Imports and cleans https://github.com/opencovid19-fr/data data on COVID-19 in France.
• covid19germany: An R package to load, visualize and analyze daily updated data on the COVID-19 outbreak in Germany.
• covid19mobility: Scrapes trends in mobility after the Covid-19 outbreak from different sources. Currently, the package scrapes data from Google (https://www.google.com/covid19/mobility/), Apple (https://www.apple.com/covid19/mobility), and will add others. The data returned uses the tidy Covid19R project data standard as well as the controlled vocabularies for measurement types.
• covid19nytimes: Accesses the NY Times Covid-19 county-level data for the US, described in https://www.nytimes.com/article/coronavirus-county-data-us.html and available at https://github.com/nytimes/covid-19-data. It then returns the data in a tidy data format according to the Covid19R Project data specification. If you plan to use the data or publicly display the data or results, please make sure cite the original NY Times source. Please read and follow the terms laid out in the data license at https://github.com/nytimes/covid-19-data/blob/master/LICENSE.
• covid19sf: Provides a verity of summary tables of the Covid19 cases in San Francisco. Data source: San Francisco, Department of Public Health - Population Health Division.
• covid19swiss: Provides a daily summary of the Coronavirus (COVID-19) cases in Switzerland cantons and Principality of Liechtenstein. Data source: Specialist Unit for Open Government Data Canton of Zurich https://www.zh.ch/de/politik-staat/opendata.html.
• covid19us: A wrapper around the ‘COVID Tracking Project API’ https://covidtracking.com/api/ providing data on cases of COVID-19 in the US.
• CovidMutations: A feasible framework for mutation analysis and reverse transcription polymerase chain reaction (RT-PCR) assay evaluation of COVID-19, including mutation profile visualization, statistics and mutation ratio of each assay. The mutation ratio is conducive to evaluating the coverage of RT-PCR assays in large-sized samples. Mercatelli, D. and Giorgi, F. M. (2020).
• CovidMutations: A feasible framework for mutation analysis and reverse transcription polymerase chain reaction (RT-PCR) assay evaluation of COVID-19, including mutation profile visualization, statistics and mutation ratio of each assay. The mutation ratio is conducive to evaluating the coverage of RT-PCR assays in large-sized samples. Mercatelli, D. and Giorgi, F. M. (2020).
• covidregionaldata: An interface to subnational and national level COVID-19 data sourced from both official sources, such as Public Health England in the UK, and from other COVID-19 data collections, including the World Health Organisation (WHO), European Centre for Disease Prevention and Control (ECDC), John Hopkins University (JHU), Google Open Data and others. This package is designed to streamline COVID-19 data extraction, cleaning, and processing from a range of data sources in an open and transparent way. For all countries supported, data includes a daily time-series of cases and, wherever available, data on deaths, hospitalisations, and tests.
• oxcovid19: The OxCOVID19 Project aims to increase our understanding of the COVID-19 pandemic and elaborate possible strategies to reduce the impact on the society through the combined power of statistical, mathematical modeling, and machine learning techniques. The OxCOVID19 Database is a large, single-centre, multimodal relational database consisting of information (using acknowledged sources) related to COVID-19 pandemic. This package provides an R-specific interface to the OxCOVID19 Database based on widely-used data handling and manipulation approaches in R.

Other data packages

• nhanesA: provides ready access to the National Health and Nutrition Examination Survey (NHANES) data tables.

CRAN packages

Related links

• The R Epidemics Consortium (RECON), a non-profit organization dedicated to the development of free, open-source outbreak analytics resources.
• Epiverse, an initiative created by data.org for the development of open-source resources for epidemic preparedness and response. Epiverse-TRACE is dedicated to creating an ecosystem of R packages for outbreak analytics.
• The R handbook for field epidemiology.

Other resources

• CRAN Task View: Spatial
• CRAN Task View: Survival
• CRAN Task View: TimeSeries
• GitHub Project: covid19germany
• GitHub Project: covid19mobility
• GitHub Project: covid19nytimes
• GitHub Project: covidregionaldata
• GitHub Project: dbdataset
• GitHub Project: EpiNow2
• GitHub Project: o2mod.transphylo
• GitHub Project: oxcovid19
• GitHub Project: trendbreaker