| Title: | POPulation IMmunity: Run a Demographic Model of Vaccine Exposure Over Time |
|---|---|
| Description: | Tools for setting up an age-structured population, applying vaccination activities to it, and tracking vaccine-induced immunity through time. |
| Authors: | Tini Garske [aut, cre] |
| Maintainer: | Tini Garske <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.0.1 |
| Built: | 2024-12-16 03:29:07 UTC |
| Source: | https://github.com/mrc-ide/popim |
apply_vacc() applies the vaccination activities listed in the
input object vacc (a data.frame object of class
popim_vacc_activities) to the popim_population object pop,
then returns the updated population object pop.
apply_vacc(pop, vacc)apply_vacc(pop, vacc)
pop |
An object of class |
vacc |
An object of class |
Once objects holding data on the population and vaccination activities
have been set up, the primary functionality provided by popim is to
apply the vaccination activities to the population to evaluate the
population immunity by age over time that results from the given
vaccination activities, which is done with the function
apply_vacc().
100% vaccine effectiveness
no wastage of doses
no waning immunity
mortality is independent of immunity status
While the first two are clearly unrealistic assumptions, if there is a constant vaccine effectiveness (<100%), or a constant wastage factor, the results can easily be scaled up by these constant factors to obtain more realistic values for vaccine demand.
For a potentially deadly disease one would expect that mortality due to the disease would be strongly correlated with immunity status, however, even the most devastating outbreaks typically only affect a small part of the population, and therefore all-cause mortality will be much less impacted by mortality due to the specific disease. This means that this assumption is closer to reality than it might appear at first.
Waning immunity is a potentially important factor to consider for many vaccines, particularly when looking at the long term benefits of vaccination. However, allowing for this is currently not implemented.
The vaccination activities object has columns for coverage, the
proportion of the target population that will be vaccinated, and
doses, the number of vaccine doses to be administered in the
activity. Given a target population size this is redundant (and
potentially conflicting) information. The function apply_vacc() will
always use coverage information in preference over doses, if this
is non-missing. If coverage is missing, the target population size
(based on the population size of the region and age groups targeted)
will be calculated and the corresponding coverage calculated as
doses / target_population.
While the function apply_vacc() will calculate the coverage if it is
missing with respect to the target population size, without modifying
the input popim_vacc_activities object, the function
complete_vacc_activities() will fill in missing coverage and doses
information from the other, and check for inconsistencies in
activities that have both data on coverage and doses. This is done
with respect to a particular popim_population object which contains
the relevant population sizes.
When vaccination is applied to a particular birth cohort that has no immunity, the result is simply that the proportion of the cohort receiving the vaccine will be immune from the time of the vaccination onwards, i.e., the immunity for this cohort will be set to equal the coverage of the vaccination activity in question. Due to the annual time step, vaccination is assumed to take effect at the end of the year in which it is implemented, so the immunity is updated from the next year onwards.
When a vaccination activity is applied to a cohort that has some
previous immunity, we need to make some assumptions about who will be
vaccinated in the new activity. There are 3 different options
implemented which are governed by the parameter targeting, one of
the input parameters to the function apply_vacc(). This parameter
can take the values "targeted", "random" or "correlated".
"targeted" targetingThe most effective way to implement a new vaccination activity is to
use "targeted" targeting, meaning that the vaccine is targeted at
previously non-immunised individuals. This means that the new immunity
of after the vaccination activity is simply the sum of the previous
immunity and the coverage of the current vaccination activity (capped
by 1 which indicates complete immunity of the cohort in question):
immunity_new = min(immunity_prev + coverage, 1)
While this is not a realistic assumption in many settings, it can be
useful for instance in multi-year campaigns that target the same
region (but possibly proceed sub-region by sub-region) over several
years. This multi-year campaign would be coded as separate vaccination
activities for each year, using the option targeting = "targeted".
"random" targetingA reasonable first assumption might be to use targeting =
"random", meaning that individuals will receive vaccination
irrespective of their previous immunity status. The immunity after the
new campaign is applied is calculated according to
immunity_new = coverage + immunity_prev - coverage * immunity_prev
"correlated" targetingOn the opposite end of the spectrum is the most pessimistic assumption
that might apply in situations where there is unequal access to
vaccination: under "correlated" targeting, people who have
previously been immunised get vaccinated first in the new vaccination
activity, and those previously not immune will only receive any
vaccine if the new coverage extends further:
immunity_new = max(immunity_prev, coverage)
Note that the order in which vaccination activities are applied to a
population does not matter under any of the targeting assumptions.
The input popim_population class object pop with
updated column immunity to reflect the vaccination
activities supplied in vacc.
Tini Garske
## set up population and vaccination activities: pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002), age_first = 0, age_last = 0, coverage = 0.8, doses = NA, targeting = "random") ## update the population immunity based on the vaccination activities: pop <- apply_vacc(pop, vacc)## set up population and vaccination activities: pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002), age_first = 0, age_last = 0, coverage = 0.8, doses = NA, targeting = "random") ## update the population immunity based on the vaccination activities: pop <- apply_vacc(pop, vacc)
popim_population object from a dataframeChecks if the dataframe is suitable (i.e., contains appropriate
columns and data ranges), and if so converts it to a
popim_population object and returns this.
as_popim_pop(df)as_popim_pop(df)
df |
a dataframe with at least columns region, age, year and pop_size. |
The input dataframe has to have at least the columns region,
age, and year. The output popim_population object is generated
via expand.grid to have consecutive year and age ranges that are
identical for all regions.
If the input dataframe contains a column pop_size, this must be
numeric and non-negative. If it is missing, this column is
generated and initialised to NA.
If the input dataframe contains a colum immunity, this must be
numeric, with values between 0 and 1. If it is missing, this
column is generated and initialised to 0.
Any further colunms are simply carried over into the popim_population object.
an object of class popim_population
Tini Garske
## set up a minimal dataframe to convert to a popim_population object: df <- expand.grid(region = "UK", age = 0:3, year = 2000:2004, stringsAsFactors = FALSE) pop <- as_popim_pop(df)## set up a minimal dataframe to convert to a popim_population object: df <- expand.grid(region = "UK", age = 0:3, year = 2000:2004, stringsAsFactors = FALSE) pop <- as_popim_pop(df)
popim_vacc_activites object from a dataframeChecks if the dataframe is suitable (i.e., contains appropriate
columns and data ranges), and if so converts it to a
popim_vacc_activities object and returns this.
as_vacc_activities(df)as_vacc_activities(df)
df |
dataframe to be converted to class
|
The input dataframe has to have at least the columns region,
year, age_first, age_last, coverage, doses and
targeting. Any further columns are simply carried over into the
popim_vacc_activities object.
an object of class popim_vacc_activities
Tini Garske
popim_vacc_activities() for details of the S3 class.
df <- data.frame(region = "UK", year = c(2000, 2002), age_first = 0, age_last = 0, coverage = 0.8, doses = NA, targeting = "random") vacc <- as_vacc_activities(df)df <- data.frame(region = "UK", year = c(2000, 2002), age_first = 0, age_last = 0, coverage = 0.8, doses = NA, targeting = "random") vacc <- as_vacc_activities(df)
Calculate the overall population immunity (aggregating over age)
from the supplied popim_population object.
calc_pop_immunity(pop)calc_pop_immunity(pop)
pop |
A |
A dataframe containing the popim_population aggregated by age.
Tini Garske
## set up population and vaccination activities: pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002), age_first = 0, age_last = 0, coverage = 0.8, doses = NA, targeting = "random") ## update the population immunity based on the vaccination activities: pop <- apply_vacc(pop, vacc) ## calculate the population immunity aggregated across ages: pop_aggregated <- calc_pop_immunity(pop)## set up population and vaccination activities: pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002), age_first = 0, age_last = 0, coverage = 0.8, doses = NA, targeting = "random") ## update the population immunity based on the vaccination activities: pop <- apply_vacc(pop, vacc) ## calculate the population immunity aggregated across ages: pop_aggregated <- calc_pop_immunity(pop)
popim_vacc_activities objectFor each line in the popim_vacc_activities object the given
information of coverage is converted to doses, or vice versa,
using the target population size implied by the popim_population
object supplied. If both coverage and doses are given for any
activity, the function checks if they are consistent with the
population size, and fails if there are any inconsistencies.
complete_vacc_activities(vacc, pop)complete_vacc_activities(vacc, pop)
vacc |
|
pop |
|
The supplied object of class popim_vacc_activities,
updated to have both doses and coverage information.
Tini Garske
## set up some vaccination activities: vacc <- popim_vacc_activities(region = "UK", year = 2001:2003, age_first = 0, age_last = 0, coverage = c(0.8, 0.8, NA), doses = c(NA, NA, 60), targeting = "random") ## set up a population to which these activities shall apply: pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) pop$pop_size <- 100 ## cohort size of 100 for all cohorts ## fill in missing coverage/doses information based on population size: vacc <- complete_vacc_activities(vacc, pop)## set up some vaccination activities: vacc <- popim_vacc_activities(region = "UK", year = 2001:2003, age_first = 0, age_last = 0, coverage = c(0.8, 0.8, NA), doses = c(NA, NA, 60), targeting = "random") ## set up a population to which these activities shall apply: pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) pop$pop_size <- 100 ## cohort size of 100 for all cohorts ## fill in missing coverage/doses information based on population size: vacc <- complete_vacc_activities(vacc, pop)
popim_population objectPlot the immunity or population size of a popim_population object
plot_immunity(pop, cols = c("whitesmoke", "midnightblue")) plot_pop_size(pop, rel = FALSE, cols = c("whitesmoke", "midnightblue"))plot_immunity(pop, cols = c("whitesmoke", "midnightblue")) plot_pop_size(pop, rel = FALSE, cols = c("whitesmoke", "midnightblue"))
pop |
|
cols |
vector of 2 colours to be used to generate the
(continuous) colour palette for plotting. Defaults to
|
rel |
logical to indicate whether to use relative or absolute
population size in |
The population is displayed in a grid showing the cohorts through time. Time is shown on the x-axis, age on the y-axis, such that a particular cohort tracks along diagonally from bottom left to top right. If there are several regions, these are shown as separate facets.
The colour in each cell corresponds to:
for plot_immunity(): the proportion of each cohort that is
immune, therefore varying between 0 and 1.
for plot_pop_size(): the size of each cohort.
As the returned object is a regular ggplot object, it can be
further modified with the ususal ggplot2 syntax.
A ggplot object.
Tini Garske
## set up population and vaccination activities: pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002), age_first = 0, age_last = 0, coverage = 0.8, doses = NA, targeting = "random") ## update the population immunity based on the vaccination activities: pop <- apply_vacc(pop, vacc) ## plot the population immunity by age and time: plot_immunity(pop) ## adding some population size manually: ## adding some population size manually: pop$pop_size <- pop$cohort - 1990 ## plot the population size by age and time: plot_pop_size(pop) ##----------------------------------------------------------------------- ## setting up a population with multiple regions: pop <- popim_population(region = c("A", "B"), year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) pop$pop_size <- pop$cohort - 1990 pop$pop_size[pop$region == "A"] <- 5 * pop$pop_size[pop$region == "A"] ## adding some vaccination activities: vacc <- popim_vacc_activities(region = c("A", "A", "B"), year = c(2001, 2002, 2003), age_first = c(0,0,0), age_last = c(0,0,10), coverage = 0.8, doses = NA, targeting = "random") pop <- apply_vacc(pop, vacc) plot_immunity(pop) plot_pop_size(pop) plot_pop_size(pop, rel = TRUE)## set up population and vaccination activities: pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002), age_first = 0, age_last = 0, coverage = 0.8, doses = NA, targeting = "random") ## update the population immunity based on the vaccination activities: pop <- apply_vacc(pop, vacc) ## plot the population immunity by age and time: plot_immunity(pop) ## adding some population size manually: ## adding some population size manually: pop$pop_size <- pop$cohort - 1990 ## plot the population size by age and time: plot_pop_size(pop) ##----------------------------------------------------------------------- ## setting up a population with multiple regions: pop <- popim_population(region = c("A", "B"), year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) pop$pop_size <- pop$cohort - 1990 pop$pop_size[pop$region == "A"] <- 5 * pop$pop_size[pop$region == "A"] ## adding some vaccination activities: vacc <- popim_vacc_activities(region = c("A", "A", "B"), year = c(2001, 2002, 2003), age_first = c(0,0,0), age_last = c(0,0,10), coverage = 0.8, doses = NA, targeting = "random") pop <- apply_vacc(pop, vacc) plot_immunity(pop) plot_pop_size(pop) plot_pop_size(pop, rel = TRUE)
popim_population classThe popim_population object is a dataframe that models an
age-structured population through time, tracking population size
and vaccine-induced immunity in the population. The population may
be spatially disaggregated into several regions.
popim_population( region = character(), year_min = integer(), year_max = integer(), age_min = 0, age_max = 100 )popim_population( region = character(), year_min = integer(), year_max = integer(), age_min = 0, age_max = 100 )
region |
character vector, list of regions considered. |
year_min |
integer, first year to be considered. |
year_max |
integer, last year to be considered. |
age_min |
integer, youngest age to be considered, defaults to 0. Must be non-negative. |
age_max |
integer, oldest age to be considered, defaults to 100. Must be non-negative, and >= age_min. |
An object of S3 class popim_population is a dataframe that
contains the columns
region: character.
year: integer.
age: integer, non-negative.
cohort: integer. This is redundant, equals year - age,
and only included for ease of handling.
immunity: numeric, between 0 and 1 (inclusive). Proportion of
the cohort that is immune due to vaccination. Initialised to 0
by the constructor popim_population().
pop_size: numeric, non-negative. Size of the cohort.
Initialised to NA_real by the constructor popim_population().
This constructor sets up the population as fully susceptible
(i.e., immunity = 0), with missing population size (i.e.,
pop_size = NA_real_) throughout. The parameters passed to the
constructor are retained as attributes to the dataframe object.
A population with non-missing population size can be read in from
a suitable file using read_popim_pop(), while vaccine induced
immunity can be generated through applying vaccination activities
to the population with apply_vacc().
An object of class popim_population. This is a dataframe with
columns region, year, age, cohort, immunity and
pop_size. The first three cover the ranges given by the
input parameters. cohort gives the year of birth. It is
reduntant as it is calculated as cohort = year - age. It is
included for ease of handling. Immunity and pop_size are
initialised as 0 and NA, respectively, throughout the whole
population.
Tini Garske
pop <- popim_population(region = "UK", year_min = 2000, year_max = 2010) pop <- popim_population(region = c("FRA", "UK"), year_min = 2000, year_max = 2010, age_min = 0, age_max = 80)pop <- popim_population(region = "UK", year_min = 2000, year_max = 2010) pop <- popim_population(region = c("FRA", "UK"), year_min = 2000, year_max = 2010, age_min = 0, age_max = 80)
popim_vacc_activites classThe popim_vacc_activities object is a dataframe that holds
information on vaccination activities that are typically meant to
be applied to a popim_population object.
popim_vacc_activities( region = character(), year = integer(), age_first = integer(), age_last = integer(), coverage = double(), doses = double(), targeting = character() )popim_vacc_activities( region = character(), year = integer(), age_first = integer(), age_last = integer(), coverage = double(), doses = double(), targeting = character() )
region |
character vector: specifies the geographic region to which each vaccination activity is to be administered. |
year |
integer vector: specifies the year in which each vaccination activity takes place. |
age_first, age_last
|
non-negative integer vectors: specify
the age range targeted in the vaccination
activity. |
coverage |
numeric vector, 0 <= |
doses |
numeric vector, non-negative. Specifies the number of doses to be used in each vaccination activity. |
targeting |
character vector, permissible entries are "random", "correlated", "targeted". Defines how vaccine is allocated if there is pre-existing immunity in the population: For "random" targeting individuals are vaccinated irrespective of immunity status, so if prior to the vaccination activity the proportion immune was x, then a proportion x of the vaccine will be administered to already vaccinated individuals and therefore be wasted. For "correlated" targeting vaccine is administered first to those already immune before any susceptible individuals receive vaccine. This option models the case of unequal access to vaccination. For "targeted" targeting, vaccine will be given first to as yet non-immune individuals. This is the most effective use of vaccine. It may be realistic in the case of multi-year campaigns targeting different areas within the geographical region specified. |
The input parameters are the columns of the popim_vacc_activities
object to be returned, they should be vectors of the same length,
or will be recycled as appropriate by data.frame().
S3 object of class popim_vacc_activities: a dataframe with
columns region, year, age_first, age_last, coverage,
doses, targeting.
Tini Garske
# setting up an empty class `popim_vacc_activities` object of the # correct structure: vacc <- popim_vacc_activities() # setting up a two specific vaccination activities vacc <- popim_vacc_activities(region = c("UK", "FRA"), year = c(2010, 2005), age_first = c(0, 0), age_last = c(0, 10), coverage = c(0.8, 0.5), doses = NA, targeting = "random")# setting up an empty class `popim_vacc_activities` object of the # correct structure: vacc <- popim_vacc_activities() # setting up a two specific vaccination activities vacc <- popim_vacc_activities(region = c("UK", "FRA"), year = c(2010, 2005), age_first = c(0, 0), age_last = c(0, 10), coverage = c(0.8, 0.5), doses = NA, targeting = "random")
Reads a population data from a .csv file, checks if
the data fulfils the requirements for a popim_population
object, and if so returns this object.
read_popim_pop(file)read_popim_pop(file)
file |
Name of the .csv file from which the population data are to be read. If it does not contain an absolute path, the file name is relative to the current working directory. |
The requirements are that the data contain the columns region,
age, year which will be coerced to character, integer,
integer. Columns pop_size and immunity are optional; they will
be coerced to numeric, and if missing will be initialised to 0 and
NA, respectively be set to NA and 0, respectively. Any additional
columns will be retained in the popim_population object.
Limitations for values:
age must be non-negative integer
immunity must be between 0 and 1 (inclusive)
pop_size must be non-negative
An object of class popim_population, a dataframe with one row
per birth cohort/year/region, with columns region, year,
age, cohort, immunity, pop_size.
Tini Garske
popim_population() for details of the S3 class, and
utils::read.csv() which handles the reading of the .csv
file.
filename <- system.file("extdata", "pop_sample.csv", package = "popim") pop <- read_popim_pop(file = filename)filename <- system.file("extdata", "pop_sample.csv", package = "popim") pop <- read_popim_pop(file = filename)
Reads a list of vaccination activities from a .csv file, checks if
the data fulfils the requirements for a popim_vacc_activities
object, and if so returns this object.
read_vacc_activities(file)read_vacc_activities(file)
file |
Name of the file from which the vaccination activities are to be read from. If it does not contain an absolute path, the file name is relative to the current working directory. |
The requirements are that the data contain the columns region,
year, age_first, age_last, coverage and targeting, of
types character, integer, integer, integer, double, character,
respectively.
0 <= age_first <= age_last 0 <= coverage <= 1 targeting must be one of "random", "correlated", "targeted".
Column region details the (geographical) region to which each
vaccination activity is to be administered.
Column year details the year in which each vaccination activity
occurs.
Columns age_first and age_last give the age range targeted in
each vaccination activity.
Column coverage gives the proportion of the target population
that will be vaccinated, while column doses gives the absolute
number of doses used in the vaccination activity. For a given
population size (which is not recorded in the
popim_vacc_activities object generated here), these can be
converted into each other, when both are given, they may be
inconsistent with each other once applied to a specific
popim_population object. The consistency between these two colums
cannot be confirmed in without reference to a popim_population
object, but this function requires that at least one of these is
non-missing in each row.
The column targeting defines how vaccine is allocated if there
is pre-existing immunity in the population: For "random" targeting
individuals are vaccinated irrespective of immunity status, so if
prior to the vaccination activity the proportion immune was x,
then a proportion x of the vaccine will be administered to already
vaccinated individuals and therefore be wasted.
For "correlated" targeting vaccine is administered first to those already immune before any susceptible individuals receive vaccine. This option models the case of unequal access to vaccination.
For "targeted" targeting, vaccine will be given first to as yet non-immune individuals. This is the most effective use of vaccine. It may be realistic in the case of multi-year campaigns targeting different areas within the geographical region specified.
object of class popim_vacc_activities, a dataframe with
one row per vaccination activity, with columns year,
age_first, age_last, coverage, targeting.
Tini Garske
popim_vacc_activities() for details of the S3 class, and
utils::read.csv() which handles the reading of the .csv
file.
filename <- system.file("extdata", "vacc_activities.csv", package = "popim") vacc <- read_vacc_activities(filename)filename <- system.file("extdata", "vacc_activities.csv", package = "popim") vacc <- read_vacc_activities(filename)
Given a popim_population object and an assumption of how vaccine
is targeted in a partially immune population, this function infers
the vaccination activities that have given rise to the specified
population immunity. When there is ambiguity (e.g., due to
subsequent campaigns achieving full coverage), the minimum
coverage/doses needed will be returned.
vacc_from_immunity(pop, targeting = "random", n_digits = 10)vacc_from_immunity(pop, targeting = "random", n_digits = 10)
pop |
|
targeting |
character, determines the assumption of how doses
are allocated. Valid options are "random" (the default),
"correlated", "targeted", see |
n_digits |
number of digits to which the coverage is to be rounded, defaults to 10. |
Default is the targeting option "random", which assumes that individuals receive vaccine independently of vaccination status, resulting in some double vaccination if there is pre-existing immunity in the population. The option "targeted" gives the most effective vaccine distribution, vaccinating unvaccinated people first, while the option "correlated" models a situation of inequalities in access to vaccination - in this extreme cases, all previously vaccinated individuals will be vaccinated first, before any remaining doses are given to unvaccinated individuals.
Note that this function will not return any potential vaccination
activities that don't change the immunity, for instance if
targeting is set to "correlated" and the coverage is too small
to increase population immunity, or if the immunity prior to the
activity was already at 1.
The vaccination of the oldest age group in the population will also never be picked up as this age group will have aged out of the population (i.e., died) before the immunity is updated in the next year.
popim_vacc_activites object
Tini Garske
## set up population and vaccination activities: pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) pop$pop_size <- 100 vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002), age_first = 0, age_last = 0, coverage = 0.8, doses = NA, targeting = "random") ## update the population immunity based on the vaccination activities: pop <- apply_vacc(pop, vacc) ## get the vaccination activities that have created the population ## immunity - this should match the input `vacc` vacc_out <- vacc_from_immunity(pop, targeting = "random")## set up population and vaccination activities: pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005, age_min = 0, age_max = 10) pop$pop_size <- 100 vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002), age_first = 0, age_last = 0, coverage = 0.8, doses = NA, targeting = "random") ## update the population immunity based on the vaccination activities: pop <- apply_vacc(pop, vacc) ## get the vaccination activities that have created the population ## immunity - this should match the input `vacc` vacc_out <- vacc_from_immunity(pop, targeting = "random")