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