Post processing

PostProcessor

A type to provide data processing features supplying reports, plots, or other data analyses.

Internal Fields

• simulation::Simulation: Simulation object
• infectionsDF::DataFrame: Infections (joined with popuation to get information on infeter and infectee)
• populationDF::DataFrame: Population dataframe with one row per individual
• vaccinationsDF::DataFrame: Output of the vaccination logger
• deathsDF::DataFrame: Output of the death logger
• testsDF::DataFrame: Output of the test logger
• pooltestsDF::DataFrame: Output of the pool test logger
• serotestsDF::DataFrame: Output of the seroprevalence test logger
• quarantinesDF::DataFrame: Output of th quarantine logger
• cache::Dict{String, Any}: Internal cache to store and retrieve intermediate results

PostProcessor(simulation::Simulation)

Create a PostProcessor object for an associated Simulation. Post Processing requires a simulation to be done.

PostProcessor(simulations::Vector{Simulation})

Create a vector of PostProcessor objects for a vector of associated Simulation objects. Post Processing requires all simulations to be done.

age_incidence(postProcessor::PostProcessor, timespan::Int64, basesize::Int64)
age_incidence(postProcessor::PostProcessor; timespan::Int64 = 7, basesize::Int64 = 100_000)

Returns a DataFrame containing the infection incidence stratified by (10-year) age groups, per pathogen.

Parameters

• postProcessor::PostProcessor: Post processor instance
• timespan::Int64: Reference time window to calculate incidence
• basesize::Int64: Reference population size to calculate incidence

Returns

• DataFrame with the following columns:

age_incidence(rd::ResultData)

Returns a DataFrame with incidence over time stratified by age groups. 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(postProcessor::PostProcessor)

Divides the number of individuals who have been infected one (or multiple) time(s) by the total number of individuals, stratified by pathogen.

Returns

• DataFrame with the following columns:

compartment_fill(postProcessor::PostProcessor)

Returns a DataFrame containing the per-tick population state counts.

Returns

• DataFrame with the following columns:

compartment_periods(postProcessor::PostProcessor)

Calculates the durations of the disease compartments of all infections and returns a DataFrame containing all additional infectee-related information, per pathogen.

Returns

• DataFrame with the following columns:

Host-level care periods (hospital/ICU/ventilation) are no longer per-infection; see _hospital_df.

cumulative_cases(postProcessor::PostProcessor)

Calculates the cumulative counts of simulated infections (exposures), recoveries, and deaths over time and returns a DataFrame containing these cumulative counts for each tick, per pathogen.

If individuals can get reinfected, the cumulative counts will reflect the total number of events, including multiple infections, recoveries, and deaths for the same individual.

Returns

• DataFrame with the following columns:

cumulative_deaths(postProcessor::PostProcessor)

Returns a DataFrame containing the total count of individuals that died, per pathogen.

Returns

• DataFrame with the following columns:

cumulative_disease_progressions(postProcessor::PostProcessor)

Calculates the accumulated number of individuals in a certain disease state (latent, presymptomatic, symptomatic and asymptomatic) after the individual has been infected, per pathogen. Rows indicate the number of elapsed ticks since infections. Latent means infected but not yet infectious. Presymptomatic means infectious but not yet symptomatic. Symptomatic means infectious and symptomatic. Asymptomatic means infectious but not symptomatic and will never develop symptoms.

Example: Row 8 showing [20, 47, 290, 50] would mean that eight ticks after exposure, 20 individuals were latent, 47 were presymptomatic (no symptoms yet, but will be developing), 290 had symptoms and 50 are not experiencing symptoms and won't ever do.

Returns

• DataFrame with the following columns:

cumulative_quarantines(postProcessor::PostProcessor)

Returns a DataFrame containing cumulative information about days spent in isolation.

Columns

deathsDF(postProcessor::PostProcessor)

Returns the internal flat deaths DataFrame.

Columns

detected_tick_cases(postProcessor::PostProcessor)

Returns the number of detected cases per tick and pathogen and the number of true new exposures. This analysis is based on the tick_test column of the infections-dataframe which indicates when an individual with an active infection was first tested positive. Thus, there might be a delay between exposure_cnt and detected_cnt.

Returns

• DataFrame with the following columns:

detection_rate(postProcessor::PostProcessor)

Returns a DataFrame with the fraction of detected cases per pathogen.

Returns

• DataFrame with the following columns:

effectiveR(postProcessor::PostProcessor)

Returns a DataFrame containing the effective R value for each tick, per pathogen.

For each infectee, this method looks ahead for secondary infections this individual might cause during the total span of the simulation. These infections are then counted towards the R-value of the initial infection. If individual A, for example, is infected at time 42 and causes four secondary infections during the next 14 ticks, these four infections are counted towards the R-value of time 42.

Note: This only works in scenarios without re-infection as the current implementation just evaluates the total infections caused by each individual in general. If an individual was infected multiple times, secondary infections will inflate the statistic.

Returns

• Dataframe with the following columns:

household_attack_rates(postProcessor::PostProcessor; hh_samples::Int64 = HOUSEHOLD_ATTACK_RATE_SAMPLES)

Returns a DataFrame containing data on the in-household attack rate, per pathogen. The in-household attack rate is defined as the fraction of individuals in a given household that got infected within the household (in-household infection chain) caused by the first introduction of the pathogen in this household. It does not reflect overall fraction of individuals that were infected in this household throughout the course of the simuation. As the attack rate calculation is very computationally intensive, it is not done for all household but rather for a subset of households. You can change the desired subset size through the optional hh_samples argument. Its default can be found in constants.jl

Returns

• DataFrame with the following columns:

infections(postProcessor::PostProcessor)

Returns the internal flat infections DataFrame.

Columns

infectionsDF(postProcessor::PostProcessor)

Returns the internal flat infections DataFrame. Lookup the docstring of infections(postProcessor::PostProcessor) for column definitions.

observed_R(postProcessor::PostProcessor)

Returns a DataFrame containing estimations for the effective (current) reproduction number R, based on detected infections, per pathogen.

Returns

• DataFrame with the following columns:

pooltestsDF(postProcessor::PostProcessor)

Returns the internal flat pool tests DataFrame.

Columns

populationDF(postProcessor)

Returns the internal flat population DataFrame.

Columns

population_pyramid(postProcessor::PostProcessor)

Returns a DataFrame containing data to generate a population pyramid. It provides the sum of all female and male individuals in all age groups in the population model. Sums for female are multiplied by -1 to facilitate visualization.

Returns

• DataFrame with the following columns:

r0(postProcessor::PostProcessor; sample_fraction = R0_CALCULATION_SAMPLE_FRACTION)

Returns a DataFrame with the R0 value per pathogen. Requires the infectionsDF to have consecutive infection IDs starting from 1 (which is the default in GEMS).

The R0 value is calculated as the number of infections caused by the first sample_fraction% of infections of each pathogen. partialsort is used to find the infection-ID threshold in O(N) average time.

Attention: This variant of the R0 calculation expects that the infections occur in a fully susceptible population, i.e. no immunity is present. If you have a scenario that includes vaccination or natural immunity, the R0 value will not be accurate.

Returns

• DataFrame with the following columns:

r0_per_county(postProcessor::PostProcessor; sample_fraction = R0_CALCULATION_SAMPLE_FRACTION)

Returns a dataframe with AGS on county level, pathogen id and a regional reproduction rate.

The R0 value is calculated as the number of infections that were caused by the first sample_fraction% of infections in each county and pathogen. The default value can be changed in the R0_CALCULATION_SAMPLE_FRACTION constant or just pass a different value as sample_fraction argument.

Attention: This variant of the R0 calculation expects that the infections occur in a fully susceptible population, i.e. no immunity is present. If you have a scenario that includes vaccination or natural immunity, the R0 value will not be accurate. Since the infector-AGS must be known, this calculation is based on the sample_fraction% of simulated infections and excludes the seeding infection (as they have no infector ags).

rolling_observed_SI(postProcessor::PostProcessor)

Returns a DataFrame containing aggregated estimations on the serial interval based on true detected cases, per pathogen. The estimations are based on all true detected cases in a 14-past-days time window. If fewer than 50 infections were recorded in that time window, detections prior to that are added until the sample is complete.

Returns

• DataFrame with the following columns:

setting_sizes(postProcessor::PostProcessor)

Returns a Dictionary containing information about size of the settings. The keys are equal to the settingtypes and the values correspond to a countmap of the setting sizes.

Returns

• Dict{String, Dict{Int64, Int64}}: Nested dictionary where the first key is the name of the setting type (e.g., "Household") and the innter dictionary is a countmap with the key being a setting size (e.g., 5) and the value the number of occurences.

sim_infectionsDF(postProcessor::PostProcessor)

Returns a DataFrame containing all infections that happened during the simulation run. As it is a direct filter on the PostProcessors internal infectionsDF, the column structure is identical to the output of infectionsDF(postProcessor)

simulation(postProcessor::PostProcessor)

Returns the associated Simulation object.

testsDF(postProcessor::PostProcessor)

Returns the internal flat tests DataFrame. It was joined with the population dataframe to also obtain personal characteristics about the testees.

Columns

tick_cases(postProcessor::PostProcessor)

Returns a DataFrame containing the count of individuals currently entering in the respective disease states exposed, infectious, recovered, and deceased, per pathogen.

Returns

• DataFrame with the following columns:

A host death is credited to exactly one pathogen (the one the HealthProgression drew it from), so dead_cnt summed over pathogens is the total number of deaths.

tick_cases_per_setting(postProcessor::PostProcessor)

Returns a DataFrame containing information about the infections in different setting types, per pathogen.

Returns

• DataFrame with the following columns:

tick_deaths(postProcessor::PostProcessor)

Returns a DataFrame containing the count of individuals that died per tick and pathogen.

Returns

• DataFrame with the following columns:

 tick_pooltests(postProcessor::PostProcessor)

Returns a Dict for each employed testtype containing their name as a key and a DataFrame containing the number of applied pool tests (positive/negative/total) per tick with a `pathogenid` column.

Returns

• Dict{String, DataFrame}: The key is the TestType's name. The values are DataFrames with the following columns:

tick_serotests(postProcessor::PostProcessor)

Returns a Dict for each seroprevalence test_type, keyed by its name and containing a DataFrame with counts per simulation tick and pathogen.

Returns

• Dict{String, DataFrame}: The key is the test_type name. The values are DataFrames with the following columns:

tick_tests(postProcessor::PostProcessor)

Returns a Dict for each employed testtype containing their name as a key and a DataFrame containing the count of individuals that got tested (positive/negative) as well as the people reported positive for the first time per tick, with a `pathogenid` column.

Returns

• Dict{String, DataFrame}: The key is the TestType's name. The values are DataFrames with the following columns:

time_to_detection(postProcessor::PostProcessor)

Returns the mean, standard deviation, minimum, maximum, upper- and lower 95% confidence intervals of the time to detection for all detected cases, per pathogen. The time to detection is defined as the number of ticks between the time of exposure and time of detection.

Returns

• DataFrame with the following columns:

total_detected_cases(postProcessor::PostProcessor)

Returns a DataFrame with the total number of detected cases per pathogen.

Returns

• DataFrame with the following columns:

total_quarantines(postProcessor::PostProcessor)

Returns the total number of total ticks spent in quarantine. E.g., if 10 indiviuals were in quarantine for 5 ticks each, this function will return 50.

total_tests(postProcessor::PostProcessor)

Sums up the total number of tests per test type and pathogen.

Returns

• DataFrame with the following columns: