Result data

ResultData(sim::Simulation; style::String = "DefaultResultData", max_tasks::Integer = POST_PROCESSING_MAX_TASKS)

Create a ResultData object using a Simulation and the name of a ResultDataStyle, that describes the level of detail for the fields to be calculated. This constructor instantiates a default PostProcessor for the passed simulation object. If you want to manually configure the PostProcessor, you need to instantiate it first and pass the PostProcessor to the ResultData constructor instead. Post Processing requires a simulation to be done. At most max_tasks result functions run at the same time.

ResultData(postProcessor::PostProcessor; style::String="", max_tasks::Integer = POST_PROCESSING_MAX_TASKS)

Create a ResultData object using a PostProcessor and a key, that describes the level of detail for the fields to be calculated. Post Processing requires a simulation to be done. At most max_tasks result functions run at the same time; lower it if memory is a bottleneck.

ResultData(postProcessors::Vector{PostProcessor}; style::String="DefaultResultData", print_infos::Bool = false, max_tasks::Integer = POST_PROCESSING_MAX_TASKS)

Create a vector ResultData objects using a vector of associated PostProcessor objects and a key, that describes the level of detail for the fields to be calculated. Post Processing requires a simulation to be done. It supresses the usual info outputs that are being made during the ResultData generation. If you want to enable them, pass print_infos = true. At most max_tasks result functions run at the same time.

ResultData(sim::Vector{Simulation}; style::String = "DefaultResultData", print_infos::Bool = false, max_tasks::Integer = POST_PROCESSING_MAX_TASKS)

Create a vector ResultData objects using a vector of Simulation objects and the name of a ResultDataStyle, that describes the level of detail for the fields to be calculated. If you want to manually configure the PostProcessor, you need to instantiate it first and pass the PostProcessor to the ResultData constructor instead. Post Processing requires a simulation to be done. It supresses the usual info outputs that are being made during the ResultData generation. If you want to enable them, pass print_infos = true. At most max_tasks result functions run at the same time.

aggregated_compartment_periods(postProcessor::PostProcessor)

Calculates the aggregated durations of the disease compartments of all infections and returns a DataFrame containing the normalized counts of durations in each compartment, per pathogen.

The values are normalized by the total number of infections per pathogen to represent the fraction of individuals that spent a certain amount of time in the respective compartment and not just the individuals who were ever in that compartment.

Returns

• DataFrame with the following columns:

aggregated_compartment_periods(rd::ResultData)

Returns the DataFrame with disease state durations (normalized). Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

attack_rate(rd::ResultData)

Returns the simulation's attack rate. It's total infections divided by population size. Returns an empty dictionary if the data is not available in the input ResultData object.

compartment_fill(rd::ResultData)

Returns the compartment_fill infections over time. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

compartment_periods(rd::ResultData)

Returns the DataFrame with duration of exposed and infectious states for all infections. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

config_file(rd::ResultData)

Returns the path to the config file

cpu_data(rd::ResultData)

Returns the processor information (not available for ARM Macs)

cumulative_cases(rd::ResultData)

Returns the cumulative infections over time. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

cumulative_deaths(rd::ResultData)

Returns the cumulative deaths over time. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

cumulative_disease_progressions(rd::ResultData)

Returns the DataFrame with cumultive number of individuals in certain disease states per tick. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

cumulative_quarantines(rd::ResultData)

Returns the DataFrame with number of isolated individuals per tick Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

cumulative_vaccinations(rd::ResultData)

Returns the cumulative vaccinations over time. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

customlogger(rd::ResultData)

Returns the DataFrame of the Simulation object's internal custom logger. Returns an empty dictionary if the data is not available in the input ResultData object.

dataframes(rd::ResultData)

Returns the dataframes of result data. Returns an empty dictionary if the data is not available in the input ResultData object.

deaths(rd::ResultData)

Returns the deaths DataFrame joined with individuals' attributes. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

detected_tick_cases(rd::ResultData)

Returns the detected cases per tick DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

detection_rate(rd::ResultData)

Returns the fraction of detected infections. Returns an empty dictionary if the data is not available in the input ResultData object.

effectiveR(rd::ResultData)

Returns the Effective R value over time DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

execution_date(rd::ResultData)

Returns the timestamp of result data generation. Returns an empty dictionary if the data is not available in the input ResultData object.

exportJLD(rd::ResultData, directory::AbstractString)

Exports the ResultData object as a JLD2 file, storing it in the specified directory.

final_tick(rd::ResultData)

Returns the tick counter at the end of the simulation run. Returns an empty dictionary if the data is not available in the input ResultData object.

free_mem_size(rd::ResultData)

Returns the available system memory

GEMS_version(rd::ResultData)

Returns the GEMS version this ResultData object was generated with. Returns an empty dictionary if the data is not available in the input ResultData object.

git_branch(rd::ResultData)

Returns the current git branch.

git_commit(rd::ResultData)

Returns the current git commit.

git_repo(rd::ResultData)

Returns the current git repository.

tick_hosptitalizations(rd::ResultData)

Returns the tests per tick DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

household_attack_rates(rd::ResultData)

Returns household_attack_rates DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

household_secondary_attack_rate(rd::ResultData)

Returns the per-pathogen household secondary attack rate, pooled over household sizes. Returns an empty dictionary if the data is not available in the input ResultData object.

household_secondary_attack_rates(rd::ResultData)

Returns household_secondary_attack_rates DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

id(rd::ResultData)

Returns the unique identifer of the ResultData object.

import_resultdata(filepath::String)

Import the ResultData object from a jld2 file. Returns the ResultData object.

infections(rd::ResultData)

Returns the infection DataFrame joined with individuals' attributes. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

info(rd::ResultData)

Prints info about available fields in the ResultData object.

initial_infections(rd::ResultData)

Returns the number of individuals who are marked as infected during initialization. This happens before the actual simulation run. Returns an empty dictionary if the data is not available in the input ResultData object.

julia_version(rd::ResultData)

Returns the Julia version that was used to generate this result data object. Returns an empty dictionary if the data is not available in the input ResultData object.

kernel(rd::ResultData)

Returns the system kernel information Returns an empty dictionary if the data is not available in the input ResultData object.

label(rd::ResultData)

Returns the lable of the simulation run. Returns an empty dictionary if the data is not available in the input ResultData object.

meta_data(rd::ResultData)

Returns the meta_data dictionary of the ResultData. Returns an empty dictionary if the data is not available in the input ResultData object.

model_size(rd::ResultData)

Returns the size of the simulation model in memory.

number_of_individuals(rd::ResultData)

Returns the total number of individuals in the population model. Returns an empty dictionary if the data is not available in the input ResultData object.

observed_R(rd::ResultData)

Returns the observed reproduction number estimation DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

pathogens(rd::ResultData)

Returns an array of pathogen parameters. Returns an empty dictionary if the data is not available in the input ResultData object.

population_file(rd::ResultData)

Returns the path to the population file Returns an empty dictionary if the data is not available in the input ResultData object.

population_pyramid(rd::ResultData)

Returns the DataFrame required to plot population pyramid (age, sex, count) Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

population_size(rd::ResultData)

Returns the size of the population model in memory.

region_info(rd::ResultData)

Returns a Dataframe with population size and area per municiaplity (if model is geolocalized). Returns an empty dictionary if the data is not available in the input ResultData object.

rolling_observed_SI(rd::ResultData)

Returns the rolling observed serial interval DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

setting_data(rd::ResultData)

Returns a DataFrame containing information on all setting types. Returns an empty dictionary if the data is not available in the input ResultData object.

setting_sizes(rd::ResultData)

Returns a Dictionary containing information on all setting sizes. Returns an empty dictionary if the data is not available in the input ResultData object.

sim_data(rd::ResultData)

Returns the sim_data of result data. Returns an empty dictionary if the data is not available in the input ResultData object.

start_condition(rd::ResultData)

Returns the StartCondition object the simulation was initialized with. Returns an empty dictionary if the data is not available in the input ResultData object.

stop_criterion(rd::ResultData)

Returns the StopCriterion object of the simulation. Returns an empty dictionary if the data is not available in the input ResultData object.

strategies(rd::ResultData)

Returns the strategies included in the simulation. Returns an empty dictionary if the data is not available in the input ResultData object.

symptom_triggers(rd::ResultData)

Returns the symptom triggers included in the simulation. Returns an empty dictionary if the data is not available in the input ResultData object.

system_data(rd::ResultData)

Returns the system_data of result data. Returns an empty dictionary if the data is not available in the input ResultData object.

tests(rd::ResultData)

Returns the tests DataFrame . Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

testtypes(rd::ResultData)

Returns the test types included in the simulation. Returns an empty dictionary if the data is not available in the input ResultData object.

threads(rd::ResultData)

Returns the number of threads this Julia instance was started with.

tick_cases(rd::ResultData)

Returns the infections per tick DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

tick_cases_per_setting(rd::ResultData)

Returns the tests per tick DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

tick_deaths(rd::ResultData)

Returns the deaths per tick DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

tick_pooltests(rd::ResultData)

Returns the pool tests per tick DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

tick_serial_intervals(rd::ResultData)

Returns the serial intervals per tick DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

tick_serotests(rd::ResultData)

Returns the seroprevalence-tests per tick DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

tick_tests(rd::ResultData)

Returns the tests per tick DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

tick_unit(rd::ResultData)

Returns the unit of time that one tick corresponds to. Returns an empty dictionary if the data is not available in the input ResultData object.

tick_vaccinations(rd::ResultData)

Returns the vaccinations per tick DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

time_to_detection(rd::ResultData)

Returns time to detection DataFrame. Look up the PostProcessor docs to find the column definitions. Returns an empty dictionary if the data is not available in the input ResultData object.

timer_output!(rd::ResultData, timer_output::TimerOutput)

Sets the TimerOutput object for a ResultData object

timer_output(rd::ResultData)

Returns the TimerOutput object used to supply debug report with execution time information Returns an empty dictionary if the data is not available in the input ResultData object.

total_infections(rd::ResultData)

Returns the row count of the PostProcessors' infections-DataFrame. Returns an empty dictionary if the data is not available in the input ResultData object.

total_mem_size(rd::ResultData)

Returns the total system memory

total_quarantines(rd::ResultData)

Returns the total quarantined agent over the course of the simulation. Returns an empty dictionary if the data is not available in the input ResultData object.

total_tests(rd::ResultData)

Returns a dictionary with the the total number of tests per TestType. Returns an empty dictionary if the data is not available in the input ResultData object.

word_size(rd::ResultData)

Returns the system word size.

ResultDataStyle

Abstract type, whose implementations define the structure of a ResutltData object.

DefaultResultData <: ResultDataStyle

The default style for ResultData objects. It contains all that can currently be calculated in the PostProcessor. Therefore, it is both, the most comprehensive and computationally intensive (memory & runtime) option.

Fields

• data::Dict{String, Any}:

  • meta_data::Dict{String, Any}

    • timer_output::TimerOutput: TimerOutput object
    • execution_date::String: Time this ResultData object was generated
    • GEMS_version::VersionNumber: GEMS version this ResultData object was generated with
    • config_file::String: Path to the config file
    • config_file_val::Dict{String, Any}: Deep copy of the supplied TOML config file
    • population_file::String: Path to the population file
    • population_params::Dict{String, Any}: Parameters used to generate population

  • sim_data::Dict{String, Any}

    • label::String: Label of this simulation run (needed for plotting)
    • final_tick::Int16: Tick counter at the end of the simulation run
    • number_of_individuals::Int64: Total number of individuals in the population model
    • r0::DataFrame: Per-pathogen basic reproduction number (pathogen_id, r0)
    • initial_infections::DataFrame: Per-pathogen seeding infection counts (pathogen_id, initial_infections)
    • total_infections::DataFrame: Per-pathogen total infection counts (pathogen_id, total_infections)
    • attack_rate::DataFrame: Per-pathogen attack rate (pathogen_id, attack_rate)
    • household_secondary_attack_rate::DataFrame: Per-pathogen pooled household secondary attack rate (pathogen_id, household_sar, household_sar_home)
    • setting_data::DataFrame: DataFrame containing information on all setting types
    • setting_sizes::Dict{Any, Any}: Dictionary containing the setting sizes distributions for all included settingtypes
    • region_info::Dataframe: Municipality population size and area (if geolocalized model is used)
    • pathogens::Vector{Pathogen}: Array of all pathogen parameters in this simulation
    • vaccine::Vaccine: Vaccine parameter [CURRENTLY DEACTIVATED]
    • vaccination_strategy::VaccineScheduler: Vaccination strategy used in this model [CURRENTLY DEACTIVATED]
    • tick_unit::String: Unit of time that one tick corresponds to
    • start_condition::StartCondition: Initial setup of the simulation
    • prior_immunity::Vector{AbstractPriorImmunity}: Immunity some individuals acquired before the start
    • stop_criterion::StopCriterion: Termination conditions
    • strategies::Vector{Strategy}: Intervention strategies
    • symptom_triggers::Vector{ITrigger}: Strategies that are triggered upon experiencing symptoms
    • testtypes::Vector{AbstractTestType}: Test types used in the model (e.g. Antigen Tests)
    • total_quarantines::Int64: Total person-ticks (e.g. days) spent in isolation
    • total_tests::DataFrame: Per-pathogen test counts (test_type, pathogen_id, count)
    • detection_rate::DataFrame: Per-pathogen fraction of detected infections (pathogen_id, detection_rate)

  • system_data::Dict{String, Any}

    • kernel::String: System kernel
    • julia_version::String: Julia version that was used to generate this data object
    • word_size::Int64: System word size
    • threads::Int64: Number of threads this Julia instance was started with
    • cpu_data::Markdown.MD: Information on the processor (not available for ARM Macs)
    • total_mem_size::Float64: Total system memory
    • free_mem_size::Float64: Available system memory
    • git_repo::SubString{String}: Current Git repository
    • git_branch::SubString{String}: Current Git branch
    • git_commit::SubString{String}: Current Git commit ID
    • model_size::Int64: Size of the simulation model in memory [CURRENTLY DACTIVATED]
    • population_size::Int64: Size of the population model in memory [CURRENTLY DACTIVATED]

  • setting_age_contacts::Dict{String, Any}

    • Household: age X age contact matrix for Households based on sampled data [CURRENTLY DACTIVATED]
    • GlobalSetting: age X age contact matrix for GlobalSettings based on sampled data [CURRENTLY DACTIVATED]

  • aggregated_setting_age_contacts::Dict{String, Any}

    • Household::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for Households based on sampled data
    • SchoolClass::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for SchoolClass based on sampled data
    • School::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for School based on sampled data
    • SchoolComplex::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for SchoolComplex based on sampled data
    • Office::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for Office based on sampled data
    • Department::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for Department based on sampled data
    • Workplace::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for Workplace based on sampled data
    • WorkplaceSite::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for WorkplaceSite based on sampled data
    • Municipality::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for Municipality based on sampled data
    • GlobalSetting::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for GlobalSetting based on sampled data

  • dataframes::Dict{String, Any}

    • infections::DataFrame: Infection data joined with individuals' attributes
    • vaccinations::DataFrame: Vaccination data joined with individuals' attributes
    • deaths::DataFrame: Death data joined with individuals' attributes
    • tests::DataFrame: Test data joined with individuals' attributes
    • effectiveR::DataFrame: Effective R value over time
    • compartment_periods::DataFrame: Duration of exposed and infectious states for all infections
    • aggregated_compartment_periods::DataFrame: Statistics on time individuals spend in each disease compartment
    • tick_cases::DataFrame: Infections per tick
    • tick_deaths::DataFrame: Deaths per tick
    • tick_vaccinations::DataFrame: Vaccinations per tick
    • tick_serial_intervals::DataFrame: Aggregated data on serial intervals per tick
    • tick_generation_times::DataFrame: Aggregated data on generation timess per tick
    • tick_tests::DataFrame: Number of tests performed per tick
    • tick_pooltests::DataFrame: Number of (pooled) tests per tick
    • tick_serotests::DataFrame: Number of seroprevalence-tests performed per tick
    • tick_cases_per_setting::DataFrame: Tick cases aggregated by settingtype,
    • detected_tick_cases::DataFrame: Number of detected infections per tick
    • compartment_fill::DataFrame: Number of individuals currently in any of the disease compartments
    • cumulative_cases::DataFrame: Cumulative infections over time
    • cumulative_deaths::DataFrame: Cumulative deaths over time
    • cumulative_vaccinations::DataFrame: Cumulative vaccinations over time
    • cumulative_disease_progressions::DataFrame: Cumulative information on disease states N ticks after exposure
    • cumulative_quarantines::DataFrame: Number of quarantined individuals per tick
    • age_incidence::DataFrame: Incidence over time stratified by age groups
    • population_pyramid::DataFrame: Data required to plot population pyramid (age, sex, count)
    • rolling_observed_SI::DataFrame: Serial interval estimation based on the last 14 days of detected cases
    • observed_R::DataFrame: Reproduction number estimation based on detected cases and the SI estimation
    • tick_hosptitalizations::DataFrame: DataFrame containing the daily hospitalizations etc.
    • time_to_detection::DataFrame: Statistics on the time between exposure and first detection of an infection through a test
    • household_attack_rates::DataFrame: Statistics on the seconary infections in households
    • household_secondary_attack_rates::DataFrame: Household secondary attack rate counts per household size, defined like a household study
    • customlogger::DataFrame: Dataframe obtained from any custom logger that might have been set
    • weekly_county_incidence::DataFrame: DataFrame with 7-day incidence per 100,000 per county and week (only for georeferential population models)
    • r0_per_county::DataFrame: DataFrame with basic reproduction number per county

LightRD <: ResultDataStyle

Similar to the default style for ResultData objects but without any raw data (e.g. the ìnfections-, deaths- or tests- dataframes) as the raw data makes around 80% of the DefaultResultData style memory footprint. It contains everything that can currently be calculated in the PostProcessor.

This RD-style cannot be used to generate geographical maps or infection videos.

Fields

• data::Dict{String, Any}: Dictionary holding the following sub-dictionaries

  • meta_data::Dict{String, Any}

    • timer_output::TimerOutput: TimerOutput object

    (Note: This data is only available if the simulation runs were done via the main() function)

    • execution_date::String: Time this ResultData object was generated
    • GEMS_version::VersionNumber: GEMS version this ResultData object was generated with
    • config_file::String: Path to the config file
    • config_file_val::Dict{String, Any}: Deep copy of the supplied TOML config file
    • population_file::String: Path to the population file
    • population_params::Dict{String, Any}: Parameters used to generate population

  • sim_data::Dict{String, Any}

    • label::String: Label of this simulation run (needed for plotting)
    • final_tick::Int16: Tick counter at the end of the simulation run
    • number_of_individuals::Int64: Total number of individuals in the population model
    • r0::DataFrame: Per-pathogen basic reproduction number (pathogen_id, r0)
    • initial_infections::DataFrame: Per-pathogen seeding infection counts (pathogen_id, initial_infections)
    • total_infections::DataFrame: Per-pathogen total infection counts (pathogen_id, total_infections)
    • attack_rate::DataFrame: Per-pathogen attack rate (pathogen_id, attack_rate)
    • household_secondary_attack_rate::DataFrame: Per-pathogen pooled household secondary attack rate (pathogen_id, household_sar, household_sar_home)
    • setting_data::DataFrame: DataFrame containing information on all setting types
    • setting_sizes::Dict{Any, Any}: Dictionary containing the setting sizes distributions for all included settingtypes
    • region_info::Dataframe: Municipality population size and area (if geolocalized model is used)
    • pathogens::Vector{Pathogen}: Array of all pathogen parameters in this simulation
    • tick_unit::String: Unit of time that one tick corresponds to
    • start_condition::StartCondition: Initial setup of the simulation
    • prior_immunity::Vector{AbstractPriorImmunity}: Immunity some individuals acquired before the start
    • stop_criterion::StopCriterion: Termination conditions
    • strategies::Vector{Strategy}: Intervention strategies
    • symptom_triggers::Vector{ITrigger}: Strategies that are triggered upon experiencing symptoms
    • testtypes::Vector{AbstractTestType}: Test types used in the model (e.g. Antigen Tests)
    • total_quarantines::Int64: Total person-ticks (e.g. days) spent in isolation
    • total_tests::DataFrame: Per-pathogen test counts (test_type, pathogen_id, count)
    • detection_rate::DataFrame: Per-pathogen fraction of detected infections (pathogen_id, detection_rate)

  • system_data::Dict{String, Any}

    • kernel::String: System kernel
    • julia_version::String: Julia version that was used to generate this data object
    • word_size::Int64: System word size
    • threads::Int64: Number of threads this Julia instance was started with
    • cpu_data::Markdown.MD: Information on the processor (not available for ARM Macs)
    • total_mem_size::Float64: Total system memory
    • free_mem_size::Float64: Available system memory
    • git_repo::SubString{String}: Current Git repository
    • git_branch::SubString{String}: Current Git branch
    • git_commit::SubString{String}: Current Git commit ID
    • model_size::Int64: Size of the simulation model in memory [CURRENTLY DACTIVATED]
    • population_size::Int64: Size of the population model in memory [CURRENTLY DACTIVATED]

  • aggregated_setting_age_contacts::Dict{String, Any}

    • Household::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for Households based on sampled data
    • SchoolClass::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for SchoolClass based on sampled data
    • School::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for School based on sampled data
    • SchoolComplex::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for SchoolComplex based on sampled data
    • Office::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for Office based on sampled data
    • Department::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for Department based on sampled data
    • Workplace::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for Workplace based on sampled data
    • WorkplaceSite::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for WorkplaceSite based on sampled data
    • Municipality::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for Municipality based on sampled data
    • GlobalSetting::ContactMatrix{Float64}: age group x age group "ContactMatrix" object for GlobalSetting based on sampled data

  • dataframes::Dict{String, Any}

    • effectiveR::DataFrame: Effective R value over time
    • tick_cases::DataFrame: Infections per tick
    • tick_deaths::DataFrame: Deaths per tick
    • tick_serial_intervals::DataFrame: Aggregated data on serial intervals per tick
    • tick_generation_times::DataFrame: Aggregated data on generation timess per tick
    • tick_tests::DataFrame: Number of tests performed per tick
    • tick_pooltests::DataFrame: Number of (pooled) tests per tick
    • tick_serotests::DataFrame: Number of seroprevalence-tests performed per tick
    • tick_cases_per_setting::DataFrame: Tick cases aggregated by settingtype,
    • detected_tick_cases::DataFrame: Number of detected infections per tick
    • compartment_fill::DataFrame: Number of individuals currently in any of the disease compartments
    • aggregated_compartment_periods::DataFrame: Statistics on time individuals spend in each disease compartment
    • cumulative_cases::DataFrame: Cumulative infections over time
    • cumulative_deaths::DataFrame: Cumulative deaths over time
    • cumulative_disease_progressions::DataFrame: Cumulative information on disease states N ticks after exposure
    • cumulative_quarantines::DataFrame: Number of quarantined individuals per tick
    • age_incidence::DataFrame: Incidence over time stratified by age groups
    • population_pyramid::DataFrame: Data required to plot population pyramid (age, sex, count)
    • rolling_observed_SI::DataFrame: Serial interval estimation based on the last 14 days of detected cases
    • observed_R::DataFrame: Reproduction number estimation based on detected cases and the SI estimation
    • tick_hosptitalizations::DataFrame: DataFrame containing the daily hospitalizations etc.
    • time_to_detection::DataFrame: Statistics on the time between exposure and first detection of an infection through a test
    • household_attack_rates::DataFrame: Statistics on the seconary infections in households
    • household_secondary_attack_rates::DataFrame: Household secondary attack rate counts per household size, defined like a household study
    • customlogger::DataFrame: Dataframe obtained from any custom logger that might have been set