Package 'epireview'

Description

This function defines the column types for the article data frame used in the epireview package. vroom is generally good at guessing the column types, but it is better to be explicit. Moreover, it reads a column of NAs as a logical vector, which is particularly undesirable for us. This function is intended to be used internally by load_epidata_raw where the files are being read.

Usage

article_column_type(pathogen)

Arguments

Value

A list of column types for the article data frame

See Also

parameter_column_type, outbreak_column_type, model_column_type

Description

Assign quality assessment score to each article

Usage

assign_qa_score(articles, ignore_errors = FALSE)

Arguments

Details

We have used a bespoke 7 question quality assessment (QA) questionnaire to assess the quality of articles. The questions can be retrieved using the qa_questions function. The function assigns a QA score to each article as the number of questions answered 'yes' divided by the total number of questions answered (an answer might be NA if the question is not relevant to the article under consideration). Articles with all NA answers are excluded from the QA unless ignore_errors is set to TRUE.

Value

a named list consisting of two elements. The first element of the list is the article data.frame with an updated column containing three new columns: qs_denominator (total number of questions answered), qs_numerator (number of questions answered 'yes') and qa_score (QA score). The second element of the list (named errors) is a data.frame containing articles with all NA answers.

See Also

qa_questions

Examples

lassa <- load_epidata("lassa")
lassa_qa <- assign_qa_score(lassa$articles, ignore_errors = FALSE)
head(lassa_qa$articles[, c("qa_denominator", "qa_numerator", "qa_score")])

Description

This function creates a vroom object (the same type created by read_csv) and checks if there are any problems with the file. If there are it will provide the offending columns and the number problematic rows (per column). A csv with the details of the issue will be written to a tmp file and the location will be provided. This function will prevent data from being loaded until all column types are correct.

Usage

check_column_types(fname, col_types, raw_colnames)

Arguments

Details

The function is intended to be used internally by load_epidata_raw where the files are being read.

See Also

article_column_type parameter_column_type, outbreak_column_type, model_column_type

Description

Sanity checks before meta-analysis

Usage

check_df_for_meta(df, cols_needed)

Arguments

Details

The function carries out a series of checks on the parameter dataframe to ensure that it is in the correct format for conducting a meta-analysis. It checks that the input (1) consists of a single parameter type; (2) has the columns required for the meta-analysis; (3) does not have any row where a value is present but the unit is missing, or vice versa. All such rows are removed; and (4) has the same unit across all values of the parameter. The function will throw an error if either there is more than one value of parameter_type, or if the columns needed for the meta-analysis are missing, or if the parameter_unit is not the same across all values of the parameter.

Value

a parameter dataframe with offending rows removed.

Description

Check upper limit of parameter values

Usage

check_ulim(df, ulim, param)

Arguments

Details

Each forest plot has a default upper limit for the parameter values, based on the expected range of the parameter. This function checks the upper limit of parameter values in a data frame and compares it with the default limit. If the maximum parameter value exceeds the specified upper limit, a warning message is displayed. The function also returns the suggested upper limit. It is used internally by the forest plot functions (only for warning the user). You can also use it directly to update the default upper limit in the forest plot functions.

Value

The suggested upper limit for the parameter values.

Examples

df <- data.frame(parameter_value = c(10, 20, 30))
check_ulim(df, 25, "parameter")

Description

Define a consistent color palette for use in figures. Palettes are currently defined for parameters and countries. Any other variable will return NULL

Usage

color_palette(col_by = c("parameter_type", "population_country"), ...)

Arguments

Value

a named list of colors that can be used in forest plots for manually setting colors

Examples

color_palette("parameter_type")

Description

This function returns a color palette for countries.

Usage

country_palette(x = NULL)

Arguments

Value

A color palette for the specified countries.

Examples

# Get color palette for all countries
country_palette()
#' # Get color palette for specific countries
country_palette(c("Liberia", "Guinea", "Sierra Leone"))

country_palette()

Description

This utility function creates a named color vector from user-supplied vectors of labels and color values. The length of the label and color vectors must be the same. The resulting custom color palette can be used as the color palette in other plotting functions.

Usage

custom_palette(labels, colors)

Arguments

Value

A custom palette in the form of a named color vector.

Examples

labels <- c("Liberia", "Guinea", "Sierra Leone")
colors <- c("#5A5156FF", "#E4E1E3FF", "#5050FFFF")

custom_pal <- custom_palette(labels, colors)
custom_pal

Description

This function converts delays in different units (hours, weeks, months) to days. It checks if all delays are in days and warns the user if not. It then converts hours to days by dividing by 24, weeks to days by multiplying by 7, and months to days by multiplying by 30.

Usage

delays_to_days(df)

Arguments

Value

Updated data.frame with delays converted to days.

Examples

df <- data.frame(
  parameter_value = c(24, 7, 1),
  parameter_unit = c("Hours", "Weeks", "Months")
)
delays_to_days(df)
# Output:
#   parameter_value parameter_unit
# 1               1           Days
# 2              49           Days
# 3              30           Days

Description

Intended for internal use only; setting desirable defaults for reading in data files.

Usage

epireview_read_file(fname, ...)

Arguments

Details

This function is intended for internal use only. It is used to read in data files shipped with the package. The idea is to set desirable defaults in a single place. Mostly it makes reading files quiet by setting show_col_types to FALSE.

Value

A data frame read in from the file.

Author(s)

Sangeeta Bhatia

Description

This function filters the rows of a data frame based on specified conditions for selected columns.

Usage

filter_cols(x, cols, funs = c("in", "==", ">", "<"), vals)

Arguments

Value

A data frame with rows filtered based on the specified conditions.

Examples

x <- load_epidata("marburg")
p <- x$params
filter_cols(p, "parameter_type", "==", "Attack rate")
filter_cols(
  p, "parameter_type", "in",
  list(parameter_type = c("Attack rate", "Seroprevalence - IFA"))
)

Description

Prepare parameter dataframe for meta analysis of means

Usage

filter_df_for_metamean(df)

Arguments

Details

The function checks that the format of df is adequate for conducting a meta analysis of means. It filters the dataframe to only include rows that meet the required format. We can only conduct a meta analysis for a parameter if its estimates have been reported as (a) mean and standard deviation, (b) median and interquartile range, or (c) median and range. This function filters the parameter dataframe to only include rows that meet these criteria. It also checks that the parameter values are all in the same units; and that the sample size is reported for each parameter value.

Value

a parameter dataframe with relevant rows selected and additional columns added to facilitate the meta analysis of means. The additional columns are: xbar, median, q1, q3, min, max.

Examples

## preparing data for meta analyses of delay from symptom onset to
## hospitalisation for Lassa

df <- load_epidata("lassa")[["params"]]
o2h_df <- df[df$parameter_type %in% "Human delay - symptom onset>admission to care", ]
o2h_df_filtered <- filter_df_for_metamean(o2h_df)
## o2h_df_filtered could then be used directly in meta analyses as:
## mtan <- metamean(data = o2h_df_filtered, ...)

Description

Prepare parameter dataframe for meta analysis of proportions

Usage

filter_df_for_metaprop(df, num_col, denom_col)

Arguments

Details

The function checks that the format of df is adequate for conducting a meta analysis of proportions. It filters the dataframe to only include rows that meet the required format by (1) removing rows where the denominator is missing, and (2) removing rows where both the numerator column or parameter value are missing. If the numerator column is missing and the parameter value is present, the numerator is imputed as the parameter value divided by 100 times the denominator.

Value

a parameter dataframe with relevant rows selected to enable meta analysis of proportions.

Examples

## preparing data for meta analyses of CFR for Lassa

df <- load_epidata("lassa")[["params"]]
cfr_df <- df[df$parameter_type %in% "Severity - case fatality rate (CFR)", ]
cfr_filtered <- filter_df_for_metaprop(cfr_df,
  num_col = "cfr_ifr_numerator", denom_col = "cfr_ifr_denominator"
)
## cfr_filtered could then be used directly in meta analyses as:
## mtan <- metaprop(data = cfr_filtered, ...)

Description

Basic forest plot displays central estimate and uncertainty for a parameter from different studies. y-axis lists the study labels and the x-axis displays parameter.

Usage

forest_plot(
  df,
  facet_by = NA,
  shape_by = NA,
  col_by = NA,
  shp_palette = NULL,
  col_palette = NULL,
  unique_label = NA
)

Arguments

Details

Generates a forest plot.

This function generates a forest plot using the provided data frame.

epireview provides a default palette for parameters and countries. If you wish to color by a different variable, you must provide a palette.

Value

A ggplot2 object representing the forest plot.

Examples

df <- data.frame(
  mid = c(1, 2, 3),
  article_label = c("A", "B", "C"),
  low = c(0.5, 1.5, 2.5),
  high = c(1.5, 2.5, 3.5),
  uncertainty_type = c("Range", "Range", "Range**")
)
forest_plot(df)

Description

Create forest plot for human delays

Usage

forest_plot_delay_int(df, ulim, reorder_studies, ...)

Arguments

Details

There are a number of 'delays' that are relevant to the pathogens we study. Some of the more commonly, and hence likely to be extracted for most pathogens, are infectious period, incubation period, and serial interval. However, there are many others reported by relatively few studies or relevant to only a few pathogens. This function is intended to serve as a template for creating forest plots for these delays. It will first reparameterise any delays reported in terms of the gamma distribution to the mean and standard deviation (see reparam_gamma). Then, any parameters reported as inverse (e.g., per day instead of days) will be inverted (see invert_inverse_params). Finally, all delays will be converted to days (see delays_to_days) before reordering the studies (if requested) and plotting the forest plot. We also provide some utility functions for the commonly used delays that are simply wrapper for this function. If however you are interested in some other delay, you will need to use this function directly, ensuring that the data frame you provide has only the relevant delays.

Value

returns plot with a summary of the human delays

See Also

forest_plot_serial_interval

Description

This function calculates the doubling time and creates a forest plot to visualize the results.

Usage

forest_plot_doubling_time(df, ulim = 30, reorder_studies = TRUE, ...)

Arguments

Value

A forest plot object.

Examples

df <- load_epidata("ebola")[["params"]]
forest_plot_doubling_time(df, ulim = 20, reorder_studies = TRUE)

Description

Create forest plot for incubation period

Usage

forest_plot_incubation_period(df, ulim = 30, reorder_studies = TRUE, ...)

Arguments

Details

This function is a wrapper for forest_plot_delay_int that is specifically for the incubation period.

Description

Create forest plot for infectious period

Usage

forest_plot_infectious_period(df, ulim = 30, reorder_studies = TRUE, ...)

Arguments

Details

This function is a wrapper for forest_plot_delay_int that is specifically for the infectious period.

Description

This function generates a forest plot for the reproduction number (Basic R0) using the provided data frame.

Usage

forest_plot_r0(df, ulim = 10, reorder_studies = TRUE, ...)

Arguments

Value

ggplot2 object.

Examples

df <- load_epidata("ebola")[["params"]]
forest_plot_r0(df, ulim = 2.5, reorder_studies = TRUE)

Description

This function generates a forest plot for the effective reproduction number (Rt) using the provided data frame.

Usage

forest_plot_rt(df, ulim = 10, reorder_studies = TRUE, ...)

Arguments

Value

A ggplot2 object representing the forest plot for effective reproduction number (Rt).

Examples

df <- load_epidata("ebola")[["params"]]
forest_plot_rt(df)

Description

Create forest plot for serial interval

Usage

forest_plot_serial_interval(df, ulim = 30, reorder_studies = TRUE, ...)

Arguments

Details

This function is a wrapper for forest_plot_delay_int that is specifically for the serial interval.

Description

Subset the epidemiological parameter columns by parameter type

Usage

get_key_columns(
  data,
  parameter_name = c("cfr", "delays", "sero", "risk_factors", "reproduction_number",
    "genomic", "attack_rate", "doubling_time", "growth_rate", "overdispersion",
    "relative_contribution"),
  all_columns = FALSE
)

Arguments

Value

A data.frame with the key columns for the selected parameter.

Examples

lassa_data <- load_epidata("lassa")
lassa_params <- lassa_data$params
cfr_lassa <- get_parameter(
  data = lassa_params,
  parameter_name = "Severity - case fatality rate (CFR)"
)
get_key_columns(data = cfr_lassa, parameter_name = "cfr")

Description

retrieve all parameters of specified type or class

Usage

get_parameter(data, parameter_name)

Arguments

Value

dataframe with all parameter estimates and columns

Examples

df <- load_epidata(pathogen = "ebola")
get_parameter(data = df$params, parameter_name = "Human delay - serial interval")
df <- load_epidata(pathogen = "marburg")
get_parameter(data = df$params, parameter_name = "Attack rate")

Description

Retrieve all incubation period parameters for a given pathogen

Retrieve all serial interval estimates for a given pathogen

Retrieve all generation time estimates for a given pathogen

Retrieve all delay parameters for a given pathogen

Retrieve all CFR parameters for a given pathogen

Retrieve all risk factor parameters for a given pathogen

Retrieve all genomic parameters for a given pathogen

Retrieve all reproduction number parameters for a given pathogen

Retrieve all seroprevalence parameters for a given pathogen

Retrieve all doubling time parameters for a given pathogen

Retrieve all attack rate parameters for a given pathogen

Retrieve all growth rate parameters for a given pathogen

Retrieve all overdispersion parameters for a given pathogen

Retrieve all overdispersion parameters for a given pathogen

Usage

get_incubation_period(data, all_columns)

get_serial_interval(data, all_columns)

get_generation_time(data, all_columns)

get_delays(data, all_columns)

get_cfr(data, all_columns)

get_risk_factors(data, all_columns)

get_genomic(data, all_columns)

get_reproduction_number(data, all_columns)

get_seroprevalence(data, all_columns)

get_doubling_time(data, all_columns)

get_attack_rate(data, all_columns)

get_growth_rate(data, all_columns)

get_overdispersion(data, all_columns)

get_relative_contribution(data, all_columns)

Arguments

Value

dataframe with all parameter estimates of this type and key columns (see get_key_columns)

Examples

df <- load_epidata(pathogen = "lassa")
get_incubation_period(data = df$params, all_columns = FALSE)

Description

Inverts the values of selected parameters in a data frame. Sometimes parameters are reported in inverse form (e.g., a delay might be reported as per day instead of days). Here we carry out a very simple transformation to convert these to the correct form by inverting the parameter value and the uncertainty bounds. This may not be appropriate in all cases, and must be checked on a case-by-case basis. This function takes a data frame as input and inverts the values of selected parameters. The selected parameters are identified by the column 'inverse_param'. The function performs the following operations:

• Inverts the parameter values of the selected parameters.

• Swaps the upper and lower bounds of the selected parameters.

• Inverts the uncertainty values of the selected parameters.

• Updates the logical vector to indicate that the parameters are no longer inverted.

• Does not change the unit of the parameters, as it remains the same as the original parameter.

Usage

invert_inverse_params(df)

Arguments

Value

The input data frame with the selected parameters inverted.

Examples

df <- data.frame(
  parameter_value = c(2, 3, 4),
  parameter_upper_bound = c(5, 6, 7),
  parameter_lower_bound = c(1, 2, 3),
  parameter_uncertainty_upper_value = c(0.1, 0.2, 0.3),
  parameter_uncertainty_lower_value = c(0.4, 0.5, 0.6),
  inverse_param = c(FALSE, TRUE, FALSE)
)
invert_inverse_params(df)
# Output:

Description

Retrieve pathogen-specific data

Usage

load_epidata(pathogen, mark_multiple = TRUE)

Arguments

Details

The data extracted in the systematic review has been stored in four files - one each for articles, parameters, outbreaks, and transmission models. Data in these files can be linked using article identifier. This function will read in the pathogen-specific files and join them into a data.frame. This function also creates user-friendly short labels for the "parameter_type" column in params data.frame. See short_parameter_type for more details.

Value

a list of length 4. The first element is a data.frame called "articles" which contains all of the information about the articles extracted for this pathogen. The second element is a data.frame called "params" with articles information (authors, publication year, doi) combined with the parameters. The third element is a data.frame called "models" with all transmission models extracted for this pathogen including articles information as above. The fourth element is a data.frame called "outbreaks" which contains all of the outbreaks extracted for this pathogen, where available. If no data is available for a particular table, the corresponding element in the list will be NULL.

Description

Loads raw data for a particular pathogen

Usage

load_epidata_raw(
  pathogen,
  table = c("article", "parameter", "outbreak", "model", "param_name")
)

Arguments

Details

This function will return the raw data as a data.frame. The csv files of the models, outbreaks, and parameters for a pathogen do not contain information on the source but only an "article_id" that can be used to merge them with the articles. If you wish to retrieve linked information or multiple tables at the same time, use load_epidata instead.

Value

data.frame reading in the csv the specified pathogen table

See Also

load_epidata() for a more user-friendly interface

Examples

load_epidata_raw(pathogen = "marburg", table = "outbreak")

Description

Ensure that each article gets a unique id across all tables

Usage

make_unique_id(articles, df, df_name)

Arguments

Details

In some instances, an article is associated with more than one id in the parameters, models, or outbreaks tables. This can lead to unexpected failures because we use the id to join the articles with other dataframes. This function will resolve the issue by first checking if a covidence id is mapped to more than one id. If it is, we replace one of the two ensuring that the same id is used across articles, models, outbreaks, and parameters. This function is not expected to be used directly by the user, but is called by load_epidata. Hence checks on arguments are not implemented.

Value

A dataframe with the same structure as df, but with unique ids for each covidence id.

Need for article ids

Why do we need article ids in the first place? Why not use covidence id? To ease the process of data extraction, we created a separate database for each extractor, with the goal of then merging the databases into a single database. Within each individual database, the different dataframes are linked by Access generated primary keys. These keys are unique to each database, but are not unique across databases. To merge the databases, we therefore generate a unique id for each article using random_id. Note that we cannot use covidence id for merging tables within each database because covidence id is only present in the articles table.

Why does an article end up with multiple ids?

Articles that have been extracted by two extractors will have multiple ids. In principle, this should not be a problem because as extractors resolve differences in data extracted, they generate a consensus entry by deleting one of the entries so that only one of the two ids should enter back into the database when the resolved entries are merged with the rest of the data. However, in practice, while resolving conflicts for multiple parameters or models, extractors may delete the row with Id1 in one case and the row with Id2 in another case. This can lead to the same article having multiple ids in parameters, models, or outbreaks. Because an article that has been double extracted will have been aasigned two ids, it is also possible that the retained id in articles is not the same as the retained id in parameters, models, or outbreaks. This can lead to rows in parameters, models, or outbreaks that are not linked to any article.

Description

This data set provides the details of all extracted articles included in the systematic review for Marburg virus disease (MVD).

Format

A csv file containing the following parameters:

• "article_id" = ID variable for the article.

• "pathogen" = pathogen name.

• "covidence_id" = article identifier used by the Imperial team.

• "first_author_first_name" = first author first name or initials.

• "article_title" = title of article.

• "doi" = the doi of the article.

• "journal" = journal that the article is published in.

• "year_publication" = year of article publication.

• "volume" = journal volume.

• "issue" = journal issue.

• "page_first" = first page number.

• "page_last" = last page number.

• "paper_copy_only" = if the article is not available online.

• "notes" = notes the extractor has made.

• "first_author_surname" = surname of the first author.

• "double_extracted" = either single extracted (0) or double extracted (1).

• "qa_m1" = quality assessment: "Is the methodological/statistical approach suitable? Q1. Clear and reproducible?" (either 0, 1, or NA)

• "qa_m2" = quality assessment: "Is the methodological/statistical approach suitable? Q2. Robust and appropriate for the aim?" (either 0, 1, or NA)

• "qa_a3" = quality assessment: "Are the assumptions appropriate? Q3. Clear and reproducible?" (either 0, 1, or NA)

• "qa_a4" = quality assessment: "Are the assumptions appropriate? Q4. Justified?" (either 0, 1, or NA)

• "qa_d5" = quality assessment: "Are the data appropriate for the selected methodological approach? Q5. Clearly described and reproducible?" (either 0, 1, or NA)

• "qa_d6" = quality assessment: "Are issues in data... Q6. Clearly discussed and acknowledged?" (either 0, 1, or NA)

• "qa_d7" = quality assessment: "Are issues in data... Q7. Accounted for in chosen methodological approach?" (either 0, 1, or NA)

• "score" = overall quality assessment score.

Source

Cuomo-Dannenburg G, McCain K, McCabe R, Unwin HJT, Doohan P, Nash RK, et al. Marburg Virus Disease outbreaks, mathematical models, and disease parameters: a Systematic Review. medRxiv; 2023. 2023.07.10.23292424. Available from: https://www.medrxiv.org/content/10.1101/2023.07.10.23292424v1

Description

This data set provides all the dropdown menu options extractors had when extracting data for mathematical models applied to MVD.

Format

A csv file containing the dropdown options for:

• Model type

• Stochastic or deterministic

• Transmission route

• Compartment type

• Assumptions

• Interventions

Source

Cuomo-Dannenburg G, McCain K, McCabe R, Unwin HJT, Doohan P, Nash RK, et al. Marburg Virus Disease outbreaks, mathematical models, and disease parameters: a Systematic Review. medRxiv; 2023. 2023.07.10.23292424. Available from: https://www.medrxiv.org/content/10.1101/2023.07.10.23292424v1

Description

This data set provides all the dropdown menu options extractors had when extracting outbreak data for MVD.

Format

A csv file containing the dropdown options for:

• Outbreak country

• Detection mode

Source

Cuomo-Dannenburg G, McCain K, McCabe R, Unwin HJT, Doohan P, Nash RK, et al. Marburg Virus Disease outbreaks, mathematical models, and disease parameters: a Systematic Review. medRxiv; 2023. 2023.07.10.23292424. Available from: https://www.medrxiv.org/content/10.1101/2023.07.10.23292424v1

Description

This data set provides all the dropdown menu options extractors had when extracting parameter data for MVD.

Format

A csv file containing the dropdown options for:

• Population Country

• Parameter type

• Units

• Parameter uncertainty - single type

• Parameter uncertainty - paired type

• Distribution type

• Distribution parameter 1 - type

• Distribution parameter 2 - type

• Disaggregated by

• Sex

• Setting

• Group

• Timing

• Reproduction number method

• Time from

• Time to

• IFR_CFR_method

• Riskfactor outcome

• Riskfactor name

• Riskfactor occupation

• Riskfactor significant

• Riskfactor adjusted

Source

Cuomo-Dannenburg G, McCain K, McCabe R, Unwin HJT, Doohan P, Nash RK, et al. Marburg Virus Disease outbreaks, mathematical models, and disease parameters: a Systematic Review. medRxiv; 2023. 2023.07.10.23292424. Available from: https://www.medrxiv.org/content/10.1101/2023.07.10.23292424v1

Description

This data set provides all extracted data for mathematical models applied to Marburg virus disease (MVD).

Format

A csv file containing the following parameters:

• model_data_id = ID variable for the model.

• article_id = ID variable for the article the model came from.

• model_type = whether the model was compartmental, branching process, agent/individual based, other, or unspecified.

• compartmental_type = if the model is compartmental, whether the model is SIS, SIR, SEIR, or "other".

• stoch_deter = whether the article specified whether the model was stochastic, deterministic, or both.

• theoretical_model = "TRUE" or "FALSE". "TRUE" if the model is not fit to data.

• interventions_type = interventions modelled e.g. vaccination, quarantine, vector control, treatment, contact tracing, hospitals, treatment centres, safe burials, behaviour changes, other, or unspecified.

• code_available = whether the code was made available in the article.

• transmission_route = which transmission route was modelled, e.g. airborne or close contact, human to human, vector to human, animal to human, sexual, or unspecified.

• assumptions = assumptions used in the model e.g. homogenous mixing, latent period is the same as incubation period, heterogeneity in transmission rates between groups or over time, age dependent susceptibility, or unspecified.

• covidence_id = article identifier used by the Imperial team.

Source

Cuomo-Dannenburg G, McCain K, McCabe R, Unwin HJT, Doohan P, Nash RK, et al. Marburg Virus Disease outbreaks, mathematical models, and disease parameters: a Systematic Review. medRxiv; 2023. 2023.07.10.23292424. Available from: https://www.medrxiv.org/content/10.1101/2023.07.10.23292424v1

Description

This data set provides all extracted data for outbreaks of Marburg virus disease (MVD).

Format

A csv file containing the following parameters:

• outbreak_id = ID variable for the outbreak.

• article_id = ID variable for the article the outbreak data came from.

• outbreak_start_day = Day that the outbreak started (numeric - DD).

• outbreak_start_month = Month that the outbreak started (three letter abbreviation e.g. "Aug").

• outbreak_start_year = Year that the outbreak started (numeric - YYYY).

• outbreak_end_day = Day that the outbreak ended (numeric - DD).

• outbreak_end_month = Month that the outbreak ended (three letter abbreviation e.g. "Aug").

• outbreak_date_year = Year that the outbreak ended (numeric - YYYY).

• outbreak_duration_months = Outbreak duration in months.

• outbreak_size = total outbreak size.

• asymptomatic_transmission = whether asymptomatic transmission occurred, either TRUE or FALSE.

• outbreak_country = country in which the outbreak occurred.

• outbreak_location = location that the outbreak occurred.

• cases_confirmed = total number of confirmed cases.

• cases_mode_detection = either diagnosed based on symptoms alone ("Symptoms"), confirmed via a laboratory test such as PCR ("Molecular (PCR etc)"), "Not specified", or NA.

• cases_suspected = total number of suspected cases.

• cases_asymptomatic = total number of asymptomatic cases.

• deaths = total number of deaths.

• cases_severe_hospitalised = total number of severe hospitalised cases.

• covidence_id = article identifier used by the Imperial team.

Source

Cuomo-Dannenburg G, McCain K, McCabe R, Unwin HJT, Doohan P, Nash RK, et al. Marburg Virus Disease outbreaks, mathematical models, and disease parameters: a Systematic Review. medRxiv; 2023. 2023.07.10.23292424. Available from: https://www.medrxiv.org/content/10.1101/2023.07.10.23292424v1

Description

This data set provides all extracted parameters for Marburg virus disease (MVD).

Format

A csv file containing the following parameters:

• parameter_data_id = ID variable for the parameter.

• article_id = ID variable for the article the outbreak data came from.

• parameter_type = extracted parameter (see marburg_dropdown_parameters for the full list of parameters extracted).

• parameter_value = extracted parameter value as stated in the article.

• parameter_unit = units of the extracted parameter (see marburg_dropdown_parameters for the full list of parameter units).

• parameter_lower_bound = minimum value of the parameter across any dimension of disaggregation.

• parameter_upper_bound = maximum value of the parameter across any dimension of disaggregation.

• parameter_value_type = whether the parameter value is the mean, median, standard deviation.

• parameter_uncertainty_single_value = extracted uncertainty if only a single value provided.

• parameter_uncertainty_single_type = uncertainty type if only a single value is reported e.g. "Standard Error (SE)" (see marburg_dropdown_parameters for the full list of options).

• parameter_uncertainty_lower_value = lower value of paired uncertainty.

• parameter_uncertainty_upper_value = upper value of paired uncertainty.

• parameter_uncertainty_type = uncertainty type if a pair of values are provided, e.g. "CI90%", "CRI95%" (see marburg_dropdown_parameters for the full list of options).

• cfr_ifr_numerator = numerator of the cfr or ifr provided in the article.

• cfr_ifr_denominator = denominator of the cfr or ifr provided in the article.

• distribution_type = if uncertainty is given as a distribution (see marburg_dropdown_parameters for the full list of options).

• distribution_par1_value = value for distribution parameter 1.

• distribution_par1_type = distribution parameter 1 type, e.g. shape, scale (see marburg_dropdown_parameters for the full list of options).

• distribution_par1_uncertainty = whether the article reported uncertainty for the distribution parameter estimates (TRUE/FALSE).

• distribution_par2_value = value for distribution parameter 2.

• distribution_par2_type = distribution parameter 2 type, e.g. shape, scale (see marburg_dropdown_parameters for the full list of options).

• distribution_par2_uncertainty = whether the article reported uncertainty for the distribution parameter estimates (TRUE/FALSE).

• method_from_supplement = whether the parameter was taken from the supplementary material of the article. TRUE/FALSE.

• method_moment_value = time period, either "Pre outbreak", Start outbreak", "Mid outbreak", "Post Outbreak", or "Other", if specified in the article.

• cfr_ifr_method = whether the method used to calculate the cfr/ifr was "Naive", "Adjusted", "Unknown".

• method_r = method used to estimate the reproduction number (see marburg_dropdown_parameters for the full list of options).

• method_disaggregated_by = how the parameter is disaggregated e.g. by Age, Sex, Region.

• method_disaggregated = whether the parameter is disaggregated (TRUE/FALSE).

• method_disaggregated_only = whether only disaggregated data is available (TRUE/FALSE).

• riskfactor_outcome = the outcome(s) for which the risk factor was evaluated e.g. "Death" or "Infection" (see marburg_dropdown_parameters for the full list of options).

• riskfactor_name = the potential risk factor(s) evaluated e.g. "Age" (see marburg_dropdown_parameters for the full list of options).

• riskfactor_occupation = specific occupation(s) if occupation is a risk factor (see marburg_dropdown_parameters for the full list of options).

• riskfactor_significant = either "Significant", "Not significant", "Unspecified" or NA.

• riskfactor_adjusted = either "Adjusted", "Not adjusted", "Unspecified".

• population_sex = the sex of the study population, either "Female", "Male", "Both", "Unspecified".

• population_sample_type = how the study was conducted e.g. "Hospital based", "Population based" (see 'Setting' in marburg_dropdown_parameters for the full list of options).

• population_group = population group e.g. "Healthcare workers" (see marburg_dropdown_parameters for the full list of options).

• population_age_min = minimum age in sample (if reported).

• population_age_max = maximum age in the sample (if reported).

• population_sample_size = total number of participants/samples tested.

• population_country = country/countries that the study took place in.

• population_location = location that the study took place e.g. region, city.

• population_study_start_day = day study started (numeric - DD)

• population_study_start_month = month study started (three letter abbreviation e.g. "Feb")

• population_study_start_year = year study started (numeric - YYYY)

• population_study_end_day = day study ended (numeric - DD)

• population_study_end_month = month study ended (three letter abbreviation e.g. "Feb")

• population_study_end_year = year study ended (numeric - YYYY)

• genome_site = the portion of the pathogen’s genome used to estimate any extracted parameters. I.e. the gene, gene segment, codon position, or a more generic description (‘whole genome’ or ‘intergenic positions’).

• genomic_sequence_available = whether the study sequenced new pathogen isolates and their accession numbers have been provided for retrieval from a public database. TRUE/FALSE.

• parameter_class = class of the extracted parameter. Either "Human delay", "Seroprevalence", "Severity", "Reproduction number", "Mutations", "Risk factors", or "Other transmission parameters".

• covidence_id = article identifier used by the Imperial team.

Source

Cuomo-Dannenburg G, McCain K, McCabe R, Unwin HJT, Doohan P, Nash RK, et al. Marburg Virus Disease outbreaks, mathematical models, and disease parameters: a Systematic Review. medRxiv; 2023. 2023.07.10.23292424. Available from: https://www.medrxiv.org/content/10.1101/2023.07.10.23292424v1

Description

Distinguish multiple estimates from the same study

Usage

mark_multiple_estimates(
  df,
  col = "parameter_type",
  label_type = c("letters", "numbers")
)

Arguments

Details

If a study has more than one estimate/model/outbreak for the same parameter_type/model/outbreak, we add a suffix to the article_label to distinguish them otherwise they will be plotted on the same line in the forest plot. Say we have two estimates for the same parameter_type (p) from the same study (s), they will then be labeled as s 1 and s 2.

Value

The modified data frame with updated article_label

Examples

df <- data.frame(
  article_label = c("A", "A", "B", "B", "C"),
  parameter_type = c("X", "X", "Y", "Y", "Z")
)
mark_multiple_estimates(df, label_type = "numbers")

Description

This function defines the column types for the models in the dataset. It returns a list of column types with their corresponding names.

Usage

model_column_type()

Value

A list of column types for the article data frame

See Also

parameter_column_type, outbreak_column_type, model_column_type

Examples

model_column_type()

Description

This function defines the column types for the outbreaks in the dataset. It returns a list of column types with their corresponding names.

Usage

outbreak_column_type()

Value

A list of column types for the article data frame

See Also

parameter_column_type, outbreak_column_type, model_column_type

Examples

outbreak_column_type()

Description

This function updates the parameter uncertainty columns in a data frame when the uncertainty is given by a single value (standard deviation or standard error). It creates new columns called ⁠low' (parameter central value - uncertainty) and ⁠high' (parameter central value + uncertainty). These columns are used by forest_plot to plot the uncertainty intervals. If the uncertainty is given by a range, the midpoint of the range is used as the central value (mid) and the lower and upper bounds are used as the low and high values respectively.

Usage

param_pm_uncertainty(df)

Arguments

Value

The updated data frame with parameter uncertainty columns

Examples

df <- data.frame(
  parameter_value = c(10, 20, 30),
  parameter_uncertainty_single_value = c(1, 2, 3),
  parameter_uncertainty_lower_value = c(5, 15, 25),
  parameter_uncertainty_upper_value = c(15, 25, 35),
  parameter_uncertainty_type = c(NA, NA, NA),
  parameter_uncertainty_singe_type = c("Standard Deviation", "Standard Error", NA)
)
updated_df <- param_pm_uncertainty(df)
updated_df

Description

This function defines the column types for the parameters in the dataset. It returns a list of column types with their corresponding names.

Usage

parameter_column_type()

Value

A list of column types for the article data frame

See Also

parameter_column_type, outbreak_column_type, model_column_type

Examples

parameter_column_type()

Description

Define a consistent color palette for use in figures

Usage

parameter_palette(x = NULL)

Arguments

Value

a named list of colors that can be used in forest plots for manually setting colors with for example scale_color_manual

Author(s)

Sangeeta Bhatia

Examples

parameter_palette()

Description

This function generates pretty labels for articles. The labels are created by combining the surname of the first year and year of publication of an article. If the surname is missing, we will use the first name. If both are missing, a warning is issued and the Covidence ID is used instead.

Usage

pretty_article_label(articles, mark_multiple)

Arguments

Value

A modified data frame with an additional column "article_label" containing the generated labels.

Examples

articles <- data.frame(
  first_author_surname = c("Smith", NA, "Johnson"),
  first_author_first_name = c(NA, "John", NA),
  year_publication = c(2010, NA, 2022),
  covidence_id = c("ABC123", "DEF456", "GHI789")
)
pretty_article_label(articles, mark_multiple = TRUE)

Description

This data set gives the list of WHO priority pathogens for which the Pathogen Epidemiology Review Group (PERG) has carried out a systematic review. The data set gives the name of the pathogen as used in the package and associated information with the review.

Usage

priority_pathogens()

Details

Data on the priority pathogens included in the systematic review

Value

data.frame with the following fields

• pathogen: name of the pathogen as used in the package

• articles_screened: number of titles and abstracts screened for inclusion

• articles_extracted: number of articles from which data were extracted

• doi: doi of the accompanying systematic review

Examples

priority_pathogens()

Description

Quality assessment questionnaire This function returns the list of 7 questions used to assess quality of articles.

Usage

qa_questions()

Value

a data.frame with the following columns: (a) qnames: these are the names of the corresponding columns in the articles data.frame; (b) qtext: the text of the question, and (c) notes: any additional notes.

Description

This function takes a dataframe as input (this will typically be params data.frame from the output of load_epidata) and reorders it to provide a sensible order for plotting. For each country, studies are ordered by the parameter value or the midpoint of the parameter range if the parameter value is missing. It creates a new column 'y' which is an ordered factor with levels corresponding to the article_label. To order the studies in some other way, set reorder_studies to FALSE in the parameter specific forest plot functions (e.g. forest_plot_rt).

Usage

reorder_studies(df, reorder_by = "population_country")

Arguments

Value

The reordered dataframe

See Also

forest_plot_rt forest_plot_r0

Examples

ebola <- load_epidata("ebola")
params <- ebola$params
rt <- params[params$parameter_type == "Reproduction number (Effective, Re)", ]

reorder_studies(param_pm_uncertainty(rt))

Description

This function reparameterizes the gamma distribution in a given data frame. If a parameter has been expressed as a gamma distribution with shape and scale, we convert these to mean and standard deviation for plotting.

Usage

reparam_gamma(df)

Arguments

Value

data.frame modified data frame with reparameterized gamma distributions.

Description

This function generates a shape palette based on the specified shape_by parameter.

Usage

shape_palette(shape_by = c("parameter_value_type"), ...)

Arguments

Value

A shape palette based on the specified shape_by parameter.

Examples

shape_palette("parameter_value_type")

Description

Short labels parameters for use in figures

Usage

short_parameter_type(x, parameter_type_full, parameter_type_short)

Arguments

Details

This function assigns short labels to otherwise very long parameter names. It is generally not intended to be called directly but is used by load_epidata when the data is loaded. The short parameter names are read from the file "param_name.csv" in the package. If you want to supply your own short names, you can do so by specifying the parameter_type_full and parameter_type_short arguments. Note however that if parameter_type_full does not contain all the parameter types in the data, the short label (parameter_type_short) will be NA for missing values. It is therefore easier and recommended that you update the column parameter_type_short once the data are loaded via load_epidata.

Value

data.frame with a new column called "parameter_type_short"

Author(s)

Sangeeta Bhatia

Description

Plotting theme for epireview A standard theme for figures in epireview.

Usage

theme_epireview(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

Arguments

Description

This function appends pretty_article_label to include the journal and we use this information to fill in any entry where the DOI is missing. (Note: DOIs were introduced in the late 1990s and so articles from before this time often do not have one.)

Usage

update_article_info(articles)

Arguments

Value

A modified data frame with an updated column "doi" with NA values replaced with either (1) article_label appended to include journal where available or (2) just article_label when journal entry is NA.

Examples

articles <- data.frame(
  doi = c("10.123", NA, "10.234"),
  article_label = c("Smith 2020", "Smith 1979", "Smith 2023"),
  journal = c("Science", "Nature", "The Lancet")
)
update_article_info(articles)

Description

Define a consistent shape palette for use in forest plots We map shape aesthetic to value type i.e., mean, median etc. This function defines a shape palette that can be used in forest plots

Usage

value_type_palette(x = NULL)

Arguments

Value

a named list of shapes where names are value types (mean, median, std dev etc.)

Author(s)

Sangeeta Bhatia

Examples

value_type_palette()