Ctrl+K
On this page

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

Basic disease transmission#

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

Time for disease progression#

Time for disease recovery#

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