# Parameters¶

This file describes each of the input parameters in Covasim. Note: the overall infection rate can be explored using sim.results['doubling_time'] and sim.results['r_eff'] (a higher infection rate means lower doubling times and higher R_eff), as well as by simply looking at the epidemic curves.

## Population parameters¶

• pop_size = Number of agents, i.e., people susceptible to SARS-CoV-2
• pop_infected = Number of initial infections
• pop_type = What type of population data to use – ‘random’ (fastest), ‘synthpops’ (best), ‘hybrid’ (compromise)
• location = What location to load data from – default Seattle

## Simulation parameters¶

• start_day = Start day of the simulation
• end_day = End day of the simulation
• n_days = Number of days to run, if end_day isn’t specified
• rand_seed = Random seed, if None, don’t reset
• verbose = Whether or not to display information during the run – options are 0 (silent), 0.1 (some; default), 1 (more), 2 (everything)

## Rescaling parameters¶

• pop_scale = Factor by which to scale the population – e.g. 1000 with pop_size = 10e3 means a population of 10m
• scaled_pop = The total scaled population, i.e. the number of agents times the scale factor; alternative to pop_scale
• rescale = Enable dynamic rescaling of the population
• rescale_threshold = Fraction susceptible population that will trigger rescaling if rescaling
• rescale_factor = Factor by which we rescale the population

## Network parameters¶

• contacts = The number of contacts per layer
• dynam_layer = Which layers are dynamic
• beta_layer = Transmissibility per layer

## Multi-strain parameters¶

• n_imports = Average daily number of imported cases (actual number is drawn from Poisson distribution)
• n_strains = The number of strains circulating in the population

## Immunity parameters¶

• use_waning = Whether to use dynamically calculated immunity
• nab_init = Parameters for the distribution of the initial level of log2(nab) following natural infection, taken from fig1b of https://doi.org/10.1101/2021.03.09.21252641
• nab_decay = Parameters describing the kinetics of decay of nabs over time, taken from fig3b of https://doi.org/10.1101/2021.03.09.21252641
• nab_kin = Constructed during sim initialization using the nab_decay parameters
• nab_boost = Multiplicative factor applied to a person’s nab levels if they get reinfected. # TODO: add source
• nab_eff = Parameters to map nabs to efficacy
• rel_imm_symp = Relative immunity from natural infection varies by symptoms
• immunity = Matrix of immunity and cross-immunity factors, set by init_immunity() in immunity.py

## Strain-specific parameters¶

• rel_beta = Relative transmissibility varies by strain
• rel_imm_strain = Relative own-immunity varies by strain

## Severity parameters¶

• rel_symp_prob = Scale factor for proportion of symptomatic cases
• rel_severe_prob = Scale factor for proportion of symptomatic cases that become severe
• rel_crit_prob = Scale factor for proportion of severe cases that become critical
• rel_death_prob = Scale factor for proportion of critical cases that result in death
• prog_by_age = Whether to set disease progression based on the person’s age
• prognoses = The actual arrays of prognoses by age; this is populated later

## Efficacy of protection measures¶

• iso_factor = Multiply beta by this factor for diganosed cases to represent isolation; set below
• quar_factor = Quarantine multiplier on transmissibility and susceptibility; set below
• quar_period = Number of days to quarantine for; assumption based on standard policies

## Events and interventions¶

• interventions = The interventions present in this simulation; populated by the user
• analyzers = Custom analysis functions; populated by the user
• timelimit = Time limit for the simulation (seconds)
• stopping_func = A function to call to stop the sim partway through

## Health system parameters¶

• n_beds_hosp The number of hospital (adult acute care) beds available for severely ill patients (default is no constraint)
• n_beds_icu The number of ICU beds available for critically ill patients (default is no constraint)
• no_hosp_factor Multiplier for how much more likely severely ill people are to become critical if no hospital beds are available
• no_icu_factor Multiplier for how much more likely critically ill people are to die if no ICU beds are available