R/data.R
In annecori/EpiEstim: Estimate Time Varying Reproduction Numbers from Epidemic Curves
#' Data on the 2009 H1N1 influenza pandemic in a school in New York city
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of self-reported and laboratory-confirmed cases of
#' influenza among children in a school in New York city during the 2009
#' H1N1 influenza pandemic (see source and references),
#' 2. interval-censored serial interval data from the 2009 outbreak of H1N1
#' influenza in a New York city school (see references).
#'
#' @name flu_2009_NYC_school
#' @docType data
#' @format A list of two elements:
#'
#' - **incidence**: a dataframe with 14 lines containing dates in first column,
#' and daily incidence in second column ,
#' - **si_data**: a dataframe containing data on the generation time for 16
#' pairs of infector/infected individuals (see references and see argument
#' `si_data` of function [estimate_R()] for details on columns)
#'
#' @source Lessler J. et al. (2009) Outbreak of 2009 pandemic influenza A (H1N1)
#' at a New York City school. New Eng J Med 361: 2628-2636.
#' @md
#' @references
#'
#' Lessler J. et al. (2009) Outbreak of 2009 pandemic influenza A (H1N1) at a
#' New York City school. New Eng J Med 361: 2628-2636.
#'
#' @examples
#' \dontrun{
#' ## Note the following examples use an MCMC routine
#' ## to estimate the serial interval distribution from data,
#' ## so they may take a few minutes to run
#'
#' ## load data on pandemic flu in a New York school in 2009
#' data("flu_2009_NYC_school")
#'
#' ## estimate the reproduction number (method "si_from_data")
#' res <- estimate_R(flu_2009_NYC_school$incidence, method="si_from_data",
#' si_data = flu_2009_NYC_school$si_data,
#' config = make_config(list(
#' t_start = seq(2, 8),
#' t_end = seq(8, 14),
#' si_parametric_distr = "G",
#' mcmc_control = make_mcmc_control(list(burnin = 1000,
#' thin = 10, seed = 1)),
#' n1 = 1000, n2 = 50))
#' )
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
#' }
NULL
################################################################################
#' Data on the 1918 H1N1 influenza pandemic in Baltimore.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of disease in Baltimore during the 1918 H1N1
#' influenza pandemic (see source and references),
#' 2. the discrete daily distribution of the serial interval for influenza,
#' assuming a shifted Gamma distribution with mean 2.6 days, standard
#' deviation 1.5 days and shift 1 day (see references).
#'
#' @name Flu1918
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence**: a vector containing 92 days of observation,
#' - **si_distr**: a vector containing a set of 12 probabilities.
#'
#' @source Frost W. and E. Sydenstricker (1919) Influenza in Maryland:
#' preliminary statistics of certain localities.
#' Public Health Rep.(34): 491-504.
#' @references
#'
#' Cauchemez S. et al. (2011) Role of social networks in shaping disease
#' transmission during a community outbreak of 2009 H1N1 pandemic influenza.
#' Proc Natl Acad Sci U S A 108(7), 2825-2830.
#'
#' Ferguson N.M. et al. (2005) Strategies for containing an emerging influenza
#' pandemic in Southeast Asia. Nature 437(7056), 209-214.
#'
#' Fraser C. et al. (2011) Influenza Transmission in Households During the 1918
#' Pandemic. Am J Epidemiol 174(5): 505-514.
#'
#' Frost W. and E. Sydenstricker (1919) Influenza in Maryland: preliminary
#' statistics of certain localities. Public Health Rep.(34): 491-504.
#'
#' Vynnycky E. et al. (2007) Estimates of the reproduction numbers of Spanish
#' influenza using morbidity data. Int J Epidemiol 36(4): 881-889.
#'
#' White L.F. and M. Pagano (2008) Transmissibility of the influenza virus in
#' the 1918 pandemic. PLoS One 3(1): e1498.
#'
#' @examples
#' ## load data on pandemic flu in Baltimore in 1918
#' data("Flu1918")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(Flu1918$incidence,
#' method = "non_parametric_si",
#' config = make_config(list(si_distr = Flu1918$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
NULL
################################################################################
#' Data on the 2009 H1N1 influenza pandemic in a school in Pennsylvania.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of acute respiratory illness
#' (ARI, defined as at least two symptoms among fever, cough, sore throat,
#' and runny nose) among children in a school in Pennsylvania during the
#' 2009 H1N1 influenza pandemic (see source and references),
#' 2. the discrete daily distribution of the serial interval for influenza,
#' assuming a shifted Gamma distribution with mean 2.6 days, standard
#' deviation 1.5 days and shift 1 day (see references).
#' 3. interval-censored serial interval data from the 2009 outbreak of H1N1
#' influenza in San Antonio, Texas, USA (see references).
#'
#' @name Flu2009
#' @docType data
#' @md
#' @format A list of three elements:
#'
#' - **incidence:** a dataframe with 32 lines containing dates in first column,
#' and daily incidence in second column (Cauchemez et al., 2011),
#' - **si_distr:** a vector containing a set of 12 probabilities (Ferguson et
#' al, 2005),
#' - **si_data:** a dataframe with 16 lines giving serial interval patient
#' data collected in a household study in San Antonio, Texas throughout the
#' 2009 H1N1 outbreak (Morgan et al., 2010).
#'
#' @source
#' Cauchemez S. et al. (2011) Role of social networks in shaping
#' disease transmission during a community outbreak of 2009 H1N1 pandemic
#' influenza. Proc Natl Acad Sci U S A 108(7), 2825-2830.
#'
#' Morgan O.W. et al. (2010) Household transmission of pandemic (H1N1) 2009, San
#' Antonio, Texas, USA, April-May 2009. Emerg Infect Dis 16: 631-637.
#'
#' @references Cauchemez S. et al. (2011) Role of social networks in shaping
#' disease transmission during a community outbreak of 2009 H1N1 pandemic
#' influenza. Proc Natl Acad Sci U S A 108(7), 2825-2830.
#'
#' Ferguson N.M. et al. (2005) Strategies for containing an emerging influenza
#' pandemic in Southeast Asia. Nature 437(7056), 209-214.
#'
#' @examples
#' ## load data on pandemic flu in a school in 2009
#' data("Flu2009")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(Flu2009$incidence, method="non_parametric_si",
#' config = make_config(list(si_distr = Flu2009$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
#'
#' \dontrun{
#' ## Note the following examples use an MCMC routine
#' ## to estimate the serial interval distribution from data,
#' ## so they may take a few minutes to run
#'
#' ## estimate the reproduction number (method "si_from_data")
#' res <- estimate_R(Flu2009$incidence, method="si_from_data",
#' si_data = Flu2009$si_data,
#' config = make_config(list(mcmc_control = make_mcmc_control(list(
#' burnin = 1000,
#' thin = 10, seed = 1)),
#' n1 = 1000, n2 = 50,
#' si_parametric_distr = "G")))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
#' }
#'
#'
NULL
################################################################################
#' Data on the 1861 measles epidemic in Hagelloch, Germany.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of symptoms in Hallegoch (Germany) during
#' the 1861 measles epidemic (see source and references),
#' 2. the discrete daily distribution of the serial interval for measles,
#' assuming a shifted Gamma distribution with mean 14.9 days, standard
#' deviation 3.9 days and shift 1 day (see references).
#'
#' @name Measles1861
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a vector containing 48 days of observation,
#' - **si_distr:** a vector containing a set of 38 probabilities.
#'
#' @source Groendyke C. et al. (2011) Bayesian Inference for Contact Networks
#' Given Epidemic Data. Scandinavian Journal of Statistics 38(3): 600-616.
#'
#' @references Groendyke C. et al. (2011) Bayesian Inference for Contact
#' Networks Given Epidemic Data. Scandinavian Journal of Statistics 38(3):
#' 600-616.
#'
#' @examples
#' ## load data on measles in Hallegoch in 1861
#' data("Measles1861")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(Measles1861$incidence, method="non_parametric_si",
#' config = make_config(list(
#' t_start = seq(17, 42),
#' t_end = seq(23, 48),
#' si_distr = Measles1861$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
NULL
################################################################################
#' Data on the 2003 SARS epidemic in Hong Kong.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of symptoms in Hong Kong during the 2003
#' severe acute respiratory syndrome (SARS) epidemic (see source and
#' references),
#' 2. the discrete daily distribution of the serial interval for SARS, assuming
#' a shifted Gamma distribution with mean 8.4 days, standard deviation 3.8
#' days and shift 1 day (see references).
#'
#' @name SARS2003
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a vector containing 107 days of observation,
#' - **si_distr:** a vector containing a set of 25 probabilities.
#'
#' @source Cori A. et al. (2009) Temporal variability and social heterogeneity
#' in disease transmission: the case of SARS in Hong Kong. PLoS Comput Biol 5(8)
#' : e1000471.
#' @references
#'
#' Cori A. et al. (2009) Temporal variability and social heterogeneity in
#' disease transmission: the case of SARS in Hong Kong. PLoS Comput Biol 5(8):
#' e1000471.
#'
#' Lipsitch M. et al. (2003) Transmission dynamics and control of severe acute
#' respiratory syndrome. Science 300(5627): 1966-1970.
#'
#' @examples
#' ## load data on SARS in Hong Kong in 2003
#' data("SARS2003")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(SARS2003$incidence, method="non_parametric_si",
#' config = make_config(list(
#' t_start = seq(14, 101),
#' t_end = seq(20, 107),
#' si_distr = SARS2003$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
NULL
################################################################################
#' Data on the 1972 smallpox epidemic in Kosovo
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of symptoms in Kosovo during the 1972
#' smallpox epidemic (see source and references),
#' 2. the discrete daily distribution of the serial interval for smallpox,
#' assuming a shifted Gamma distribution with mean 22.4 days, standard
#' deviation 6.1 days and shift 1 day (see references).
#'
#' @name Smallpox1972
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a vector containing 57 days of observation,
#' - **si_distr:** a vector containing a set of 46 probabilities.
#'
#' @source Fenner F. et al. (1988) Smallpox and its Eradication. Geneva, World
#' Health Organization.
#' @references
#'
#' Fenner F. et al. (1988) Smallpox and its Eradication. Geneva, World Health
#' Organization.
#'
#' Gani R. and S. Leach (2001) Transmission potential of smallpox in
#' contemporary populations. Nature 414(6865): 748-751.
#'
#' Riley S. and N. M. Ferguson (2006) Smallpox transmission and control: spatial
#' dynamics in Great Britain. Proc Natl Acad Sci U S A 103(33): 12637-12642.
#'
#' @examples
#' ## load data on smallpox in Kosovo in 1972
#' data("Smallpox1972")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(Smallpox1972$incidence, method="non_parametric_si",
#' config = make_config(list(
#' t_start = seq(27, 51),
#' t_end = seq(33, 57),
#' si_distr = Smallpox1972$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
NULL
################################################################################
#' Mock data on a rotavirus epidemic.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of symptoms in a mock outbreak of rotavirus,
#' 2. mock observations of symptom onset dates for 19 pairs of
#' infector/infected individuals.
#'
#' @name MockRotavirus
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a vector containing 53 days of observation,
#' - **si_distr:** a dataframe containing a set of 19 observations; each
#' observation corresponds to a pair of infector/infected individuals. EL
#' and ER columns contain the lower an upper bounds of the dates of
#' symptoms onset in the infectors. SL and SR columns contain the lower an
#' upper bounds of the dates of symptoms onset in the infected individuals.
#' The type column has entries 0, 1, or 2, corresponding to doubly
#' interval-censored, single interval-censored or exact observations,
#' respectively, see Reich et al. Statist. Med. 2009
#'
#' @examples
#' \dontrun{
#' ## Note the following example uses an MCMC routine
#' ## to estimate the serial interval distribution from data,
#' ## so may take a few minutes to run
#'
#' ## load data
#' data("MockRotavirus")
#'
#' ## estimate the reproduction number (method "si_from_data")
#' res <- estimate_R(MockRotavirus$incidence,
#' method = "si_from_data",
#' si_data = MockRotavirus$si_data,
#' config = make_config(list(
#' si_parametric_distr = "G",
#' mcmc_control = make_mcmc_control(list(burnin = 3000, thin = 10)),
#' n1 = 500, n2 = 50)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
#' }
NULL
################################################################################
#' Data on Middle East Respiratory Syndrome (MERS) in Saudi Arabia.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of symptoms of laboratory confirmed human
#' infections with MERS-CoV in Saudi Arabia between the beginning of July
#' 2014 and the end of December 2015, and
#' 2. estimates of the mean and standard deviation of the serial interval for
#' MERS.
#'
#' @name mers_2014_15
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a dataframe containing 495 days of observations with dates
#' in the first column, and number of local (2nd column) and imported (3rd
#' column) cases of MERS,
#' - **si:** a list of estimates of the mean (mean_si) and standard deviation
#' (std_si) of the serial interval for MERS.
#'
#' @source The incidence data was extracted from the EMPRES I system from FAO
#' (Global Animal Disease Information System - Food and Agriculture Organization
#' of the United Nations, 2017). Note incidence on the first day was originally
#' made of one local case and zero imported cases; this has been modified to
#' zero local cases and one imported case in the dataset shown here so the
#' reproduction number can be estimated from the start using the function
#' [estimate_R()]. The serial interval parameters were those
#' estimated by Cauchemez et al. (2016).
#'
#' @references
#'
#' Global Animal Disease Information System - Food and Agriculture Organization
#' of the United Nations, 2017
#'
#' Cauchemez S, Nouvellet P, Cori A, Jombart T, Garske T, Clapham H, Moore S,
#' Linden Mills H, Salje H, Collins C, et al. 2016.
#' Unraveling the drivers of MERS-CoV transmission.
#' Proc Natl Acad Sci 113: 9081-9086.
#'
#' @examples
#'
#' ## load data
#' data("mers_2014_15")
#'
#' ## estimate the reproduction number (method "parametric_si")
#' bimonthly_R <- estimate_R(mers_2014_15$incidence[,c("local", "imported")],
#' method = "parametric_si",
#' config = make_config(
#' mean_si = mers_2014_15$si$mean_si,
#' std_si = mers_2014_15$si$std_si,
#' t_start = 2:(nrow(mers_2014_15$incidence)-8*7),
#' t_end = (2:(nrow(mers_2014_15$incidence)-8*7)) + 8*7))
#'
#' plot(bimonthly_R, legend = FALSE, add_imported_cases = TRUE,
#' options_I = list(col = c("local" = "black",
#' "imported" = "red"),
#' interval = 7, # show weekly incidence
#' ylab = "Weekly incidence"),
#' options_R = list(ylab = "Bimonthly R"))
#' # The first plot shows the weekly incidence,
#' # with imported cases shown in red and local cases in black
#'
NULL
################################################################################
#' Data on the 2020-2022 SARS-CoV-2 epidemic in the UK.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the UK daily incidence of deaths within 28 days of a positive COVID test
#' (see source and references),
#' 2. the discrete daily distribution of the serial interval for SARS-CoV-2,
#' assuming a shifted Gamma distribution with mean 6.1 days,
#' standard deviation 4.2 days and shift 1 day (see references).
#'
#' @name covid_deaths_2020_uk
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a data.frame containing 672 days of observation,
#' - **si_distr:** a vector containing a set of 30 probabilities.
#'
#' @source Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and
#' transmission of COVID-19 in 391 cases and 1286 of their close contacts in
#' Shenzhen, China: a retrospective cohort study. The Lancet Infectious Diseases.
#' 2020 Aug 1;20(8):911–9.
#'
#' https://coronavirus.data.gov.uk/details/cases
#'
#' @references
#'
#' Nash RK, Cori A, Nouvellet P. Estimating the epidemic reproduction number
#' from temporally aggregated incidence data: a statistical modelling approach
#' and software tool. (medRxiv pre-print)
#'
#' Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and
#' transmission of COVID-19 in 391 cases and 1286 of their close contacts in
#' Shenzhen, China: a retrospective cohort study. The Lancet Infectious Diseases.
#' 2020 Aug 1;20(8):911–9.
#'
#' @examples
#' data("covid_deaths_2020_uk")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(covid_deaths_2020_uk$incidence$Incidence,
#' method="non_parametric_si",
#' config = make_config(list(si_distr = covid_deaths_2020_uk$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
NULL
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#' Data on the 2009 H1N1 influenza pandemic in a school in New York city
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of self-reported and laboratory-confirmed cases of
#' influenza among children in a school in New York city during the 2009
#' H1N1 influenza pandemic (see source and references),
#' 2. interval-censored serial interval data from the 2009 outbreak of H1N1
#' influenza in a New York city school (see references).
#'
#' @name flu_2009_NYC_school
#' @docType data
#' @format A list of two elements:
#'
#' - **incidence**: a dataframe with 14 lines containing dates in first column,
#' and daily incidence in second column ,
#' - **si_data**: a dataframe containing data on the generation time for 16
#' pairs of infector/infected individuals (see references and see argument
#' `si_data` of function [estimate_R()] for details on columns)
#'
#' @source Lessler J. et al. (2009) Outbreak of 2009 pandemic influenza A (H1N1)
#' at a New York City school. New Eng J Med 361: 2628-2636.
#' @md
#' @references
#'
#' Lessler J. et al. (2009) Outbreak of 2009 pandemic influenza A (H1N1) at a
#' New York City school. New Eng J Med 361: 2628-2636.
#'
#' @examples
#' \dontrun{
#' ## Note the following examples use an MCMC routine
#' ## to estimate the serial interval distribution from data,
#' ## so they may take a few minutes to run
#'
#' ## load data on pandemic flu in a New York school in 2009
#' data("flu_2009_NYC_school")
#'
#' ## estimate the reproduction number (method "si_from_data")
#' res <- estimate_R(flu_2009_NYC_school$incidence, method="si_from_data",
#' si_data = flu_2009_NYC_school$si_data,
#' config = make_config(list(
#' t_start = seq(2, 8),
#' t_end = seq(8, 14),
#' si_parametric_distr = "G",
#' mcmc_control = make_mcmc_control(list(burnin = 1000,
#' thin = 10, seed = 1)),
#' n1 = 1000, n2 = 50))
#' )
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
#' }
NULL
################################################################################
#' Data on the 1918 H1N1 influenza pandemic in Baltimore.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of disease in Baltimore during the 1918 H1N1
#' influenza pandemic (see source and references),
#' 2. the discrete daily distribution of the serial interval for influenza,
#' assuming a shifted Gamma distribution with mean 2.6 days, standard
#' deviation 1.5 days and shift 1 day (see references).
#'
#' @name Flu1918
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence**: a vector containing 92 days of observation,
#' - **si_distr**: a vector containing a set of 12 probabilities.
#'
#' @source Frost W. and E. Sydenstricker (1919) Influenza in Maryland:
#' preliminary statistics of certain localities.
#' Public Health Rep.(34): 491-504.
#' @references
#'
#' Cauchemez S. et al. (2011) Role of social networks in shaping disease
#' transmission during a community outbreak of 2009 H1N1 pandemic influenza.
#' Proc Natl Acad Sci U S A 108(7), 2825-2830.
#'
#' Ferguson N.M. et al. (2005) Strategies for containing an emerging influenza
#' pandemic in Southeast Asia. Nature 437(7056), 209-214.
#'
#' Fraser C. et al. (2011) Influenza Transmission in Households During the 1918
#' Pandemic. Am J Epidemiol 174(5): 505-514.
#'
#' Frost W. and E. Sydenstricker (1919) Influenza in Maryland: preliminary
#' statistics of certain localities. Public Health Rep.(34): 491-504.
#'
#' Vynnycky E. et al. (2007) Estimates of the reproduction numbers of Spanish
#' influenza using morbidity data. Int J Epidemiol 36(4): 881-889.
#'
#' White L.F. and M. Pagano (2008) Transmissibility of the influenza virus in
#' the 1918 pandemic. PLoS One 3(1): e1498.
#'
#' @examples
#' ## load data on pandemic flu in Baltimore in 1918
#' data("Flu1918")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(Flu1918$incidence,
#' method = "non_parametric_si",
#' config = make_config(list(si_distr = Flu1918$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
NULL
################################################################################
#' Data on the 2009 H1N1 influenza pandemic in a school in Pennsylvania.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of acute respiratory illness
#' (ARI, defined as at least two symptoms among fever, cough, sore throat,
#' and runny nose) among children in a school in Pennsylvania during the
#' 2009 H1N1 influenza pandemic (see source and references),
#' 2. the discrete daily distribution of the serial interval for influenza,
#' assuming a shifted Gamma distribution with mean 2.6 days, standard
#' deviation 1.5 days and shift 1 day (see references).
#' 3. interval-censored serial interval data from the 2009 outbreak of H1N1
#' influenza in San Antonio, Texas, USA (see references).
#'
#' @name Flu2009
#' @docType data
#' @md
#' @format A list of three elements:
#'
#' - **incidence:** a dataframe with 32 lines containing dates in first column,
#' and daily incidence in second column (Cauchemez et al., 2011),
#' - **si_distr:** a vector containing a set of 12 probabilities (Ferguson et
#' al, 2005),
#' - **si_data:** a dataframe with 16 lines giving serial interval patient
#' data collected in a household study in San Antonio, Texas throughout the
#' 2009 H1N1 outbreak (Morgan et al., 2010).
#'
#' @source
#' Cauchemez S. et al. (2011) Role of social networks in shaping
#' disease transmission during a community outbreak of 2009 H1N1 pandemic
#' influenza. Proc Natl Acad Sci U S A 108(7), 2825-2830.
#'
#' Morgan O.W. et al. (2010) Household transmission of pandemic (H1N1) 2009, San
#' Antonio, Texas, USA, April-May 2009. Emerg Infect Dis 16: 631-637.
#'
#' @references Cauchemez S. et al. (2011) Role of social networks in shaping
#' disease transmission during a community outbreak of 2009 H1N1 pandemic
#' influenza. Proc Natl Acad Sci U S A 108(7), 2825-2830.
#'
#' Ferguson N.M. et al. (2005) Strategies for containing an emerging influenza
#' pandemic in Southeast Asia. Nature 437(7056), 209-214.
#'
#' @examples
#' ## load data on pandemic flu in a school in 2009
#' data("Flu2009")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(Flu2009$incidence, method="non_parametric_si",
#' config = make_config(list(si_distr = Flu2009$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
#'
#' \dontrun{
#' ## Note the following examples use an MCMC routine
#' ## to estimate the serial interval distribution from data,
#' ## so they may take a few minutes to run
#'
#' ## estimate the reproduction number (method "si_from_data")
#' res <- estimate_R(Flu2009$incidence, method="si_from_data",
#' si_data = Flu2009$si_data,
#' config = make_config(list(mcmc_control = make_mcmc_control(list(
#' burnin = 1000,
#' thin = 10, seed = 1)),
#' n1 = 1000, n2 = 50,
#' si_parametric_distr = "G")))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
#' }
#'
#'
NULL
################################################################################
#' Data on the 1861 measles epidemic in Hagelloch, Germany.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of symptoms in Hallegoch (Germany) during
#' the 1861 measles epidemic (see source and references),
#' 2. the discrete daily distribution of the serial interval for measles,
#' assuming a shifted Gamma distribution with mean 14.9 days, standard
#' deviation 3.9 days and shift 1 day (see references).
#'
#' @name Measles1861
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a vector containing 48 days of observation,
#' - **si_distr:** a vector containing a set of 38 probabilities.
#'
#' @source Groendyke C. et al. (2011) Bayesian Inference for Contact Networks
#' Given Epidemic Data. Scandinavian Journal of Statistics 38(3): 600-616.
#'
#' @references Groendyke C. et al. (2011) Bayesian Inference for Contact
#' Networks Given Epidemic Data. Scandinavian Journal of Statistics 38(3):
#' 600-616.
#'
#' @examples
#' ## load data on measles in Hallegoch in 1861
#' data("Measles1861")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(Measles1861$incidence, method="non_parametric_si",
#' config = make_config(list(
#' t_start = seq(17, 42),
#' t_end = seq(23, 48),
#' si_distr = Measles1861$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
NULL
################################################################################
#' Data on the 2003 SARS epidemic in Hong Kong.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of symptoms in Hong Kong during the 2003
#' severe acute respiratory syndrome (SARS) epidemic (see source and
#' references),
#' 2. the discrete daily distribution of the serial interval for SARS, assuming
#' a shifted Gamma distribution with mean 8.4 days, standard deviation 3.8
#' days and shift 1 day (see references).
#'
#' @name SARS2003
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a vector containing 107 days of observation,
#' - **si_distr:** a vector containing a set of 25 probabilities.
#'
#' @source Cori A. et al. (2009) Temporal variability and social heterogeneity
#' in disease transmission: the case of SARS in Hong Kong. PLoS Comput Biol 5(8)
#' : e1000471.
#' @references
#'
#' Cori A. et al. (2009) Temporal variability and social heterogeneity in
#' disease transmission: the case of SARS in Hong Kong. PLoS Comput Biol 5(8):
#' e1000471.
#'
#' Lipsitch M. et al. (2003) Transmission dynamics and control of severe acute
#' respiratory syndrome. Science 300(5627): 1966-1970.
#'
#' @examples
#' ## load data on SARS in Hong Kong in 2003
#' data("SARS2003")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(SARS2003$incidence, method="non_parametric_si",
#' config = make_config(list(
#' t_start = seq(14, 101),
#' t_end = seq(20, 107),
#' si_distr = SARS2003$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
NULL
################################################################################
#' Data on the 1972 smallpox epidemic in Kosovo
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of symptoms in Kosovo during the 1972
#' smallpox epidemic (see source and references),
#' 2. the discrete daily distribution of the serial interval for smallpox,
#' assuming a shifted Gamma distribution with mean 22.4 days, standard
#' deviation 6.1 days and shift 1 day (see references).
#'
#' @name Smallpox1972
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a vector containing 57 days of observation,
#' - **si_distr:** a vector containing a set of 46 probabilities.
#'
#' @source Fenner F. et al. (1988) Smallpox and its Eradication. Geneva, World
#' Health Organization.
#' @references
#'
#' Fenner F. et al. (1988) Smallpox and its Eradication. Geneva, World Health
#' Organization.
#'
#' Gani R. and S. Leach (2001) Transmission potential of smallpox in
#' contemporary populations. Nature 414(6865): 748-751.
#'
#' Riley S. and N. M. Ferguson (2006) Smallpox transmission and control: spatial
#' dynamics in Great Britain. Proc Natl Acad Sci U S A 103(33): 12637-12642.
#'
#' @examples
#' ## load data on smallpox in Kosovo in 1972
#' data("Smallpox1972")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(Smallpox1972$incidence, method="non_parametric_si",
#' config = make_config(list(
#' t_start = seq(27, 51),
#' t_end = seq(33, 57),
#' si_distr = Smallpox1972$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
NULL
################################################################################
#' Mock data on a rotavirus epidemic.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of symptoms in a mock outbreak of rotavirus,
#' 2. mock observations of symptom onset dates for 19 pairs of
#' infector/infected individuals.
#'
#' @name MockRotavirus
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a vector containing 53 days of observation,
#' - **si_distr:** a dataframe containing a set of 19 observations; each
#' observation corresponds to a pair of infector/infected individuals. EL
#' and ER columns contain the lower an upper bounds of the dates of
#' symptoms onset in the infectors. SL and SR columns contain the lower an
#' upper bounds of the dates of symptoms onset in the infected individuals.
#' The type column has entries 0, 1, or 2, corresponding to doubly
#' interval-censored, single interval-censored or exact observations,
#' respectively, see Reich et al. Statist. Med. 2009
#'
#' @examples
#' \dontrun{
#' ## Note the following example uses an MCMC routine
#' ## to estimate the serial interval distribution from data,
#' ## so may take a few minutes to run
#'
#' ## load data
#' data("MockRotavirus")
#'
#' ## estimate the reproduction number (method "si_from_data")
#' res <- estimate_R(MockRotavirus$incidence,
#' method = "si_from_data",
#' si_data = MockRotavirus$si_data,
#' config = make_config(list(
#' si_parametric_distr = "G",
#' mcmc_control = make_mcmc_control(list(burnin = 3000, thin = 10)),
#' n1 = 500, n2 = 50)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
#' }
NULL
################################################################################
#' Data on Middle East Respiratory Syndrome (MERS) in Saudi Arabia.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the daily incidence of onset of symptoms of laboratory confirmed human
#' infections with MERS-CoV in Saudi Arabia between the beginning of July
#' 2014 and the end of December 2015, and
#' 2. estimates of the mean and standard deviation of the serial interval for
#' MERS.
#'
#' @name mers_2014_15
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a dataframe containing 495 days of observations with dates
#' in the first column, and number of local (2nd column) and imported (3rd
#' column) cases of MERS,
#' - **si:** a list of estimates of the mean (mean_si) and standard deviation
#' (std_si) of the serial interval for MERS.
#'
#' @source The incidence data was extracted from the EMPRES I system from FAO
#' (Global Animal Disease Information System - Food and Agriculture Organization
#' of the United Nations, 2017). Note incidence on the first day was originally
#' made of one local case and zero imported cases; this has been modified to
#' zero local cases and one imported case in the dataset shown here so the
#' reproduction number can be estimated from the start using the function
#' [estimate_R()]. The serial interval parameters were those
#' estimated by Cauchemez et al. (2016).
#'
#' @references
#'
#' Global Animal Disease Information System - Food and Agriculture Organization
#' of the United Nations, 2017
#'
#' Cauchemez S, Nouvellet P, Cori A, Jombart T, Garske T, Clapham H, Moore S,
#' Linden Mills H, Salje H, Collins C, et al. 2016.
#' Unraveling the drivers of MERS-CoV transmission.
#' Proc Natl Acad Sci 113: 9081-9086.
#'
#' @examples
#'
#' ## load data
#' data("mers_2014_15")
#'
#' ## estimate the reproduction number (method "parametric_si")
#' bimonthly_R <- estimate_R(mers_2014_15$incidence[,c("local", "imported")],
#' method = "parametric_si",
#' config = make_config(
#' mean_si = mers_2014_15$si$mean_si,
#' std_si = mers_2014_15$si$std_si,
#' t_start = 2:(nrow(mers_2014_15$incidence)-8*7),
#' t_end = (2:(nrow(mers_2014_15$incidence)-8*7)) + 8*7))
#'
#' plot(bimonthly_R, legend = FALSE, add_imported_cases = TRUE,
#' options_I = list(col = c("local" = "black",
#' "imported" = "red"),
#' interval = 7, # show weekly incidence
#' ylab = "Weekly incidence"),
#' options_R = list(ylab = "Bimonthly R"))
#' # The first plot shows the weekly incidence,
#' # with imported cases shown in red and local cases in black
#'
NULL
################################################################################
#' Data on the 2020-2022 SARS-CoV-2 epidemic in the UK.
#'
#' @description
#'
#' This data set gives:
#'
#' 1. the UK daily incidence of deaths within 28 days of a positive COVID test
#' (see source and references),
#' 2. the discrete daily distribution of the serial interval for SARS-CoV-2,
#' assuming a shifted Gamma distribution with mean 6.1 days,
#' standard deviation 4.2 days and shift 1 day (see references).
#'
#' @name covid_deaths_2020_uk
#' @docType data
#' @md
#' @format A list of two elements:
#'
#' - **incidence:** a data.frame containing 672 days of observation,
#' - **si_distr:** a vector containing a set of 30 probabilities.
#'
#' @source Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and
#' transmission of COVID-19 in 391 cases and 1286 of their close contacts in
#' Shenzhen, China: a retrospective cohort study. The Lancet Infectious Diseases.
#' 2020 Aug 1;20(8):911–9.
#'
#' https://coronavirus.data.gov.uk/details/cases
#'
#' @references
#'
#' Nash RK, Cori A, Nouvellet P. Estimating the epidemic reproduction number
#' from temporally aggregated incidence data: a statistical modelling approach
#' and software tool. (medRxiv pre-print)
#'
#' Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and
#' transmission of COVID-19 in 391 cases and 1286 of their close contacts in
#' Shenzhen, China: a retrospective cohort study. The Lancet Infectious Diseases.
#' 2020 Aug 1;20(8):911–9.
#'
#' @examples
#' data("covid_deaths_2020_uk")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' res <- estimate_R(covid_deaths_2020_uk$incidence$Incidence,
#' method="non_parametric_si",
#' config = make_config(list(si_distr = covid_deaths_2020_uk$si_distr)))
#' plot(res)
#' ## the second plot produced shows, at each each day,
#' ## the estimate of the reproduction number
#' ## over the 7-day window finishing on that day.
NULL
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