more realistic scenarios
@@ -28,6 +28,12 @@ def test_interventions(do_plot=False, do_show=True, do_save=False, fig_path=None
n_people = base_sim['n']
npts = base_sim.npts
+ # Define overall testing assumptions
+ # As the most optimistic case, we assume countries could get to South Korea's testing levels. S Korea has tested
+ # an average of 10000 people/day over March, or 270,000 in total. This is ~200 people per million every day (0.02%).
+ max_optimistic_testing = 0.0002
+ optimistic_daily_tests = [max_optimistic_testing*n_people]*npts # Very best-case scenario
+
# Define the scenarios
scenarios = {
'baseline': {
@@ -36,29 +42,16 @@ def test_interventions(do_plot=False, do_show=True, do_save=False, fig_path=None
'interventions': None,
}
},
- 'test1pc': {
- 'name':'Test 1% (untargeted); isolate positives',
- 'pars': {
- 'interventions': cv.TestNum(npts, daily_tests=[0.01*n_people]*npts),
- }
- },
- 'test10pc': {
- 'name':'Test 10% (untargeted); isolate positives',
+ 'test_skorea': {
+ 'name':'Assuming South Korea testing levels of 0.02% daily (untargeted); isolate positives',
'pars': {
- 'interventions': cv.TestNum(npts, daily_tests=[0.10*n_people]*npts),
+ 'interventions': cv.TestNum(npts, daily_tests=optimistic_daily_tests)
}
},
'tracing1pc': {
- 'name':'Test 1% (contact tracing); isolate positives',
- 'pars': {
- 'interventions': cv.TestNum(npts, daily_tests=[0.01*n_people]*npts),
- 'cont_factor': 0.1, # This means that people who've been in contact with known positives isolate with 90% effectiveness
- }
- },
- 'tracing10pc': {
- 'name':'Test 10% (contact tracing); isolate positives',
+ 'name':'Assuming South Korea testing levels of 0.02% daily (with contact tracing); isolate positives',
'pars': {
- 'interventions': cv.TestNum(npts, daily_tests=[0.10*n_people]*npts),
+ 'interventions': cv.TestNum(npts, daily_tests=optimistic_daily_tests),
'cont_factor': 0.1, # This means that people who've been in contact with known positives isolate with 90% effectiveness
}
},
