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 ultimately susceptible to CoV

  • pop_infected = Number of initial infections

  • pop_type = What type of population data to use – random (fastest), synthpops (best), hybrid (compromise), or clustered (not recommended)

  • 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), 1 (default), 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

  • 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

  • beta = Beta per symptomatic contact; absolute

  • contacts = The number of contacts per layer; set below

  • dynam_layer = Which layers are dynamic; set below

  • beta_layer = Transmissibility per layer; set below

  • n_imports = Average daily number of imported cases (actual number is drawn from Poisson distribution)

  • beta_dist = Distribution to draw individual level transmissibility; see https://wellcomeopenresearch.org/articles/5-67

  • viral_dist = The time varying viral load (transmissibility); estimated from Lescure 2020, Lancet, https://doi.org/10.1016/S1473-3099(20)30200-0

Efficacy of protection measures

  • asymp_factor = Multiply beta by this factor for asymptomatic cases; no statistically significant difference in transmissibility: https://www.sciencedirect.com/science/article/pii/S1201971220302502

  • 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

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

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