Output from the simulator

Events tracked during the simulation

This simulator simulations individuals in a population using an event-based model. Briefly speaking,

The simulator creates an initial population with a set of initial events (see later section for details)
The events will happen at a pre-specified time and will trigger further events. For example,

an INFECTION event will cause someone to be infected with the virus. The individual will be marked as infected and according to individualized parameters of the infectivity of the infected individual, the INFECTION event may trigger a series of events such as INFECTION to other individuals, SHOW_SYMPTIOM if he or she is symptomatic and will show symptom after an incubation period, REMOVAL or QUARANTINE after symptoms are detected.

The simulator processes and logs the events and stops after pre-specified exit condition.

Currently the following events are tracked

Name	Event
`ABORT`	If the first carrier show sympton during quarantine.
`END`	Simulation ends.
`ERROR`	An error was raised.
`INFECION_FAILED`	No one left to infect
`INFECTION_AVOIDED`	An infection happended during quarantine. The individual might not have showed sympton.
`INFECTION_IGNORED`	Infect an infected individual, which does not change anything.
`INFECTION`	Infect an non-quarantined individual, who might already been infected.
`QUARANTINE`	Quarantine someone till specified time.
`RECOVER`	Recovered, no longer infectious
`REINTEGRATION`	Reintroduce the quarantined individual to group.
`REMOVAL`	Remove from population.
`SHOW_SYMPTOM`	Show symptom.
`VACCINATION`	Vaccination
`REPLACEMENT`	A special event that replace the existing individual with a new uninfected one.
`START`	Start up simulation.
`STAT`	Report population stats at specified intervals.

The log file contains all events that happens during the simulations. For example, for command

outbreak_simulator --repeat 100 --popsize 64 --logfile result_remove_symptomatic.txt

You will get an output file result_remove_symptomatic.txt with the following columns:

column	content
`id`	id of the simulation.
`time`	time of the event in days, accurate to hour.
`event`	type of event
`target`	subject of the event, for example the ID of the individual that has been quarantined.
`params`	Additional parameters, mostly for the `INFECTION` event where simulated $R_0$ and incubation period will be displayed.

and would look like the following:

id      time    event   target  params
     0.00    START   .       id=2,time=06/23/2020-17:51:39,args=--infector 0
     0.00    INFECTION       0       r0=2.10,r=0,r_presym=0,r_sym=0,incu=4.99
     4.99    SHOW_SYMPTOM    0       .
     4.99    REMOVAL 0       popsize=63
     11.33   RECOVER 0       recovered=0,infected=0,popsize=63,True=True
     11.33   END     63      popsize=63,prop_asym=0.123,time=06/23/2020-17:51:39
     0.00    START   .       id=3,time=06/23/2020-17:51:39,args=--infector 0
     0.00    INFECTION       0       r0=1.49,r=0,r_presym=0,r_sym=0,incu=5.47
     5.47    SHOW_SYMPTOM    0       .
     5.47    REMOVAL 0       popsize=63
     11.64   RECOVER 0       recovered=0,infected=0,popsize=63,True=True
     11.64   END     63      popsize=63,prop_asym=0.220,time=06/23/2020-17:51:39
     0.00    START   .       id=7,time=06/23/2020-17:51:39,args=--infector 0
     0.00    INFECTION       0       r0=0.43,r=0,r_asym=0

which I assume would be pretty self-explanatory. Note that the simulation IDs are not ordered because the they are run in parallel but you can expect all events belong to the same simulation are recorded together..

Summary report from multiple replicates

At the end of each command, a report will be given to summarize key statistics from multiple replicated simulations. The output contains the following keys and their values

name	value
`logfile`	Log file of the simulation with all the events
`popsize`	Initial population size
`handle_symptomatic`	If asymptomatic infectees are kept, removed, or quarantined, at what percentage.
`prop_asym_carriers`	Proportion of asymptomatic carriers, also the probability of infectee who do not show any symptom
`interval`	Interval of time events (1/24 for hours)
`n_simulation`	Total number of simulations, which is the number of `END` events
`total_infection`	Number of `INFECTION` events
`total_infection_failed`	Number of `INFECTION_FAILED` events
`total_infection_avoided`	Number of `INFECTION_AVOIDED` events
`total_infection_ignored`	Number of `INFECTION_IGNORED` events
`total_show_symptom`	Number of `SHOW_SYMPTOM` events
`total_removal`	Number of `REMOVAL` events
`total_quarantine`	Number of `QUARANTINE` events
`total_reintegration`	Number of `REINTEGRATION` events
`total_abort`	Number of `ABORT` events
`total_asym_infection`	Number of asymptomatic infections
`total_presym_infection`	Number of presymptomatic infections
`total_sym_infection`	Number of symptomatic infections
`n_remaining_popsize_XXX`	Number of simulations with `XXX` remaining population size
`n_no_outbreak`	Number of simulations with no outbreak (no symptom from anyone, or mission canceled)
`n_outbreak_duration_XXX`	Number of simulations with outbreak ends in day `XXX` . Pre-quarantine days are not counted as outbreak. Outbreak can end at day 0 if the infectee will not show symtom or infect others.
`n_no_infected_by_seed`	Number of simulations when the introduced carrier does not infect anyone
`n_num_infected_by_seed_XXX`	Number of simulations with `XXX` people affected by the introduced virus carrier, `XXX > 0` .
`n_first_infected_by_seed_on_day_XXX`	Number of simulations when the introduced carrier infect the first infectee on day `XXX` , `XXX<1` is rounded to 1, and so on. Pre-quarantine time is deducted.
`n_seed_show_no_symptom`	Number of simulations when the seed show no symptom
`n_seed_show_symptom_on_day_XXX`	Number of simulations when the carrier show symptom at day `XXX` , `XXX < 1` is rounded to 1, and so on.
`n_no_first_infection`	Number of simualations with no infection at all.
`n_first_infection_on_day_XXX`	Number of simualations with the first infection event happens at day `XXX` . It is the same as `XXX_n_first_infected_by_seed_on_day` but is reserved when multiple seeds are introduced.
`n_first_symptom`	Number of simulations when with at least one symptomatic case
`n_first_symptom_on_day_XXX`	Number of simulations when the first symptom appear at day `XXX` , `XXX < 1` is rounded to 1, and so on. Symptom during quarantine is not considered and pre-quarantine days are deducted.
`n_second_symptom`	Number of simulations when there are a second symptomatic case symptom.
`n_second_symptom_on_day_XXX`	Number of simulations when the second symptom appear at day `XXX` after the first symptom
`n_third_symptom`	Number of simulations when there are a third symptomatic case symtom
`n_third_symptom_on_day_XXX`	Number of simulations when the first symptom appear at day `XXX` after the second symptom
`n_popsize_XXX`	Population size at time `XXX` , a list if multiple replicates
`n_YYY_popsize_XXX`	Population size of group YYY at time `XXX` a list if multiple replicates
`n_infected_XXX`	Number of infected individuals at time `XXX` a list if multiple replicates
`n_YYY_infected_XXX`	Number of infected individuals in group YYY at time `XXX` a list if multiple replicates
`n_recovered_XXX`	Number of recovered individuals at time `XXX` a list if multiple replicates
`n_YYY_recovered_XXX`	Number of recovered individuals in group YYY at time `XXX` a list if multiple replicates
`incidence_rate_XXX`	Incidence rate (infected/popsize) at time `XXX` a list if multiple replicates
`YYY_incidence_rate_XXX`	Incidence rate in group YYY at time `XXX` a list if multiple replicates
`seroprevalence_XXX`	Seroprevalence (infected + recovered)/popsize) at time `XXX` a list if multiple replicates
`YYY_seroprevalence_XXX`	Seroprevalence in group YYY at time `XXX` a list if multiple replicates
`avg_n_popsize_XXX`	Average population size at time `XXX` if there are multiple replicates
`avg_n_YYY_popsize_XXX`	Average population size of group YYY at time `XXX` if there are multiple replicates
`avg_n_infected_XXX`	Average number of infected individuals at time `XXX` if there are multiple replicates
`avg_n_YYY_infected_XXX`	Average number of infected individuals in group YYY at time `XXX` if there are multiple replicates
`avg_n_recovered_XXX`	Average number of recovered individuals at time `XXX` if there are multiple replicates
`avg_n_YYY_recovered_XXX`	Average number of recovered individuals in group YYY at time `XXX` if there are multiple replicates
`avg_incidence_rate_XXX`	Average incidence rate (infected/popsize) at time `XXX` if there are multiple replicates
`avg_YYY_incidence_rate_XXX`	Average incidence rate in group YYY at time `XXX` if there are multiple replicates
`avg_seroprevalence_XXX`	Average neroprevalence (infected + recovered)/popsize) at time `XXX` if there are multiple replicates
`avg_YYY_seroprevalence_XXX`	Average seroprevalence in group YYY at time `XXX` if there are multiple replicates
`EVENT_STAT_XXX`	Reported statistics `STAT` for customized event `EVENT` at time `XXX` a list if multiple replicates
`avg_EVENT_STAT_XXX`	Average reported statistics `STAT` for customized event `EVENT` at time `XXX` if there are multiple replicates

NOTE: All avg_ fields have average over existing replicates, and all replicates. For example, if at time 100, 8 of the replicate simulations have completed and the rest 2 have population size 5, and 10, the average population size will be reported as 7.5 ( 5+10/2 ) and 0.15 ( 5+10/10 ).

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