Update cova_app.py
comment old intervention param
@@ -47,35 +47,32 @@ def get_defaults(region=None, merge=False):
# 'Seattle': 10000,
# 'Wuhan': 1,
},
- 'n_days': {
- 'Example': 60,
- # 'Seattle': 45,
- # 'Wuhan': 90,
- },
+ # 'n_days': {
+ # 'Example': 60,
+ # # 'Seattle': 45,
+ # # 'Wuhan': 90,
+ # },
'pop_infected': {
'Example': 100,
# 'Seattle': 4,
# 'Wuhan': 10,
},
- 'web_int_day': {
- 'Example': 25,
- # 'Seattle': 0,
- # 'Wuhan': 1,
- },
- 'web_int_eff': {
- 'Example': 0.8,
- # 'Seattle': 0.0,
- # 'Wuhan': 0.9,
- },
+ # 'web_int_day': {
+ # 'Example': 25,
+ # # 'Seattle': 0,
+ # # 'Wuhan': 1,
+ # },
+ # 'web_int_eff': {
+ # 'Example': 0.8,
+ # # 'Seattle': 0.0,
+ # # 'Wuhan': 0.9,
+ # },
}
sim_pars = {}
sim_pars['pop_scale'] = dict(best=1, min=1, max=1e6, name='Population scale factor', tip='Multiplier for results (to approximate large populations)')
sim_pars['pop_size'] = dict(best=5000, min=1, max=max_pop, name='Population size', tip='Number of agents simulated in the model')
sim_pars['pop_infected'] = dict(best=10, min=1, max=max_pop, name='Initial infections', tip='Number of initial seed infections in the model')
- sim_pars['n_days'] = dict(best=90, min=1, max=max_days, name='Number of days to simulate', tip='Number of days to run the simulation for')
- sim_pars['web_int_day'] = dict(best=20, min=0, max=max_days, name='Intervention start day', tip='Start day of the intervention (for no intervention, set start day to 0 and effectiveness to 0)')
- sim_pars['web_int_eff'] = dict(best=0.9, min=0, max=1.0, name='Intervention effectiveness', tip='Fractional reduction in infectiousness due to intervention')
sim_pars['rand_seed'] = dict(best=0, min=0, max=100, name='Random seed', tip='Random number seed (set to 0 for different results each time)')
epi_pars = {}
@@ -211,8 +208,6 @@ def run_sim(sim_pars=None, epi_pars=None, intervention_pars=None, show_animation
# Add the intervention
web_pars['interventions'] = []
- if web_pars['web_int_day'] is not None:
- web_pars['interventions'] = cv.change_beta(days=web_pars.pop('web_int_day'), changes=(1-web_pars.pop('web_int_eff')))
switcher = {
'social_distance': map_social_distance,
