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.
pop_size= Number of agents, i.e., people susceptible to SARS-CoV-2pop_infected= Number of initial infectionspop_type= What type of population data to use -- 'random' (fastest), 'synthpops' (best), 'hybrid' (compromise)location= What location to load data from -- default Seattle
start_day= Start day of the simulationend_day= End day of the simulationn_days= Number of days to run, if end_day isn't specifiedrand_seed= Random seed, if None, don't resetverbose= Whether or not to display information during the run -- options are 0 (silent), 0.1 (some; default), 1 (more), 2 (everything)
pop_scale= Factor by which to scale the population -- e.g. 1000 with pop_size = 10e3 means a population of 10mscaled_pop= The total scaled population, i.e. the number of agents times the scale factor; alternative to pop_scalerescale= Enable dynamic rescaling of the populationrescale_threshold= Fraction susceptible population that will trigger rescaling if rescalingrescale_factor= Factor by which we rescale the population
beta= Beta per symptomatic contact; absoluten_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/5-67viral_dist= The time varying viral load (transmissibility); estimated from Lescure 2020, Lancet, https://doi.org/10.1016/S1473-3099(20)30200-0asymp_factor= Multiply beta by this factor for asymptomatic cases; no statistically significant difference in transmissibility: https://www.sciencedirect.com/science/article/pii/S1201971220302502
contacts= The number of contacts per layerdynam_layer= Which layers are dynamicbeta_layer= Transmissibility per layer
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
use_waning= Whether to use dynamically calculated immunitynab_init= Parameters for the distribution of the initial level of log2(nab) following natural infection, taken from fig1b of https://doi.org/10.1101/2021.03.09.21252641nab_decay= Parameters describing the kinetics of decay of nabs over time, taken from fig3b of https://doi.org/10.1101/2021.03.09.21252641nab_kin= Constructed during sim initialization using the nab_decay parametersnab_boost= Multiplicative factor applied to a person's nab levels if they get reinfected. # TODO: add sourcenab_eff= Parameters to map nabs to efficacyrel_imm_symp= Relative immunity from natural infection varies by symptomsimmunity= Matrix of immunity and cross-immunity factors, set by init_immunity() in immunity.py
rel_beta= Relative transmissibility varies by strainrel_imm_strain= Relative own-immunity varies by strain
exp2inf= Duration from exposed to infectious; see Lauer et al., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081172/, subtracting inf2sym durationinf2sym= Duration from infectious to symptomatic; see Linton et al., https://doi.org/10.3390/jcm9020538sym2sev= Duration from symptomatic to severe symptoms; see Linton et al., https://doi.org/10.3390/jcm9020538Duration from severe symptoms to requiring ICU; see Wang et al., https://jamanetwork.com/journals/jama/fullarticle/2761044Duration from severe symptoms to requiring ICU
asym2rec= Duration for asymptomatic people to recover; see Wölfel et al., https://www.nature.com/articles/s41586-020-2196-xmild2rec= Duration for people with mild symptoms to recover; see Wölfel et al., https://www.nature.com/articles/s41586-020-2196-xsev2rec= Duration for people with severe symptoms to recover, 22.6 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdfcrit2rec= Duration for people with critical symptoms to recover, 22.6 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdfcrit2die= Duration from critical symptoms to death, 17.8 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdf
rel_symp_prob= Scale factor for proportion of symptomatic casesrel_severe_prob= Scale factor for proportion of symptomatic cases that become severerel_crit_prob= Scale factor for proportion of severe cases that become criticalrel_death_prob= Scale factor for proportion of critical cases that result in deathprog_by_age= Whether to set disease progression based on the person's ageprognoses= The actual arrays of prognoses by age; this is populated later
iso_factor= Multiply beta by this factor for diganosed cases to represent isolation; set belowquar_factor= Quarantine multiplier on transmissibility and susceptibility; set belowquar_period= Number of days to quarantine for; assumption based on standard policies
interventions= The interventions present in this simulation; populated by the useranalyzers= Custom analysis functions; populated by the usertimelimit= Time limit for the simulation (seconds)stopping_func= A function to call to stop the sim partway through
n_beds_hospThe number of hospital (adult acute care) beds available for severely ill patients (default is no constraint)n_beds_icuThe number of ICU beds available for critically ill patients (default is no constraint)no_hosp_factorMultiplier for how much more likely severely ill people are to become critical if no hospital beds are availableno_icu_factorMultiplier for how much more likely critically ill people are to die if no ICU beds are available