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config_sample_2pop_inference.yml
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config_sample_2pop_inference.yml
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name: sample_2pop
setup_name: minimal
start_date: 2020-02-01
end_date: 2020-08-31
nslots: 1
subpop_setup:
geodata: model_input/geodata_sample_2pop.csv
mobility: model_input/mobility_sample_2pop.csv
initial_conditions:
method: SetInitialConditions
initial_conditions_file: model_input/ic_2pop.csv
allow_missing_subpops: TRUE
allow_missing_compartments: TRUE
compartments:
infection_stage: ["S", "E", "I", "R"]
seir:
integration:
method: rk4
dt: 1
parameters:
sigma:
value: 1 / 4
gamma:
value: 1 / 5
Ro:
value: 2.5
transitions:
- source: ["S"]
destination: ["E"]
rate: ["Ro * gamma"]
proportional_to: [["S"],["I"]]
proportion_exponent: ["1","1"]
- source: ["E"]
destination: ["I"]
rate: ["sigma"]
proportional_to: ["E"]
proportion_exponent: ["1"]
- source: ["I"]
destination: ["R"]
rate: ["gamma"]
proportional_to: ["I"]
proportion_exponent: ["1"]
seir_modifiers:
scenarios:
- Ro_all
modifiers:
Ro_mod: # assume same for all subpopulations, as a method to fit baseline R0
method: SinglePeriodModifier
parameter: Ro
period_start_date: 2020-02-01
period_end_date: 2020-08-31
subpop: "all"
subpop_groups: "all"
value:
distribution: truncnorm
mean: 1.0
sd: 0.001
a: 0
b: 1
perturbation:
distribution: truncnorm
mean: 0
sd: 0.025
a: -0.1
b: 0.1
Ro_lockdown: # allow to vary by subpopulation
method: SinglePeriodModifier
parameter: Ro
period_start_date: 2020-03-15
period_end_date: 2020-05-01
subpop: "all"
value:
distribution: truncnorm
mean: 0.5
sd: 0.001
a: 0
b: 1
perturbation:
distribution: truncnorm
mean: 0
sd: 0.025
a: -0.1
b: 0.1
Ro_relax: # allow to vary by subpopulation
method: SinglePeriodModifier
parameter: Ro
period_start_date: 2020-05-01
period_end_date: 2020-07-01
subpop: "all"
value:
distribution: truncnorm
mean: 0.8
sd: 0.001
a: 0
b: 1
perturbation:
distribution: truncnorm
mean: 0
sd: 0.025
a: -0.1
b: 0.1
Ro_all:
method: StackedModifier
modifiers: ["Ro_mod","Ro_lockdown","Ro_relax"]
outcomes:
method: delayframe
outcomes:
incidCase: #incidence of detected cases
source:
incidence:
infection_stage: "I"
probability:
value: 0.5
delay:
value: 5
incidHosp: #incidence of hospitalizations
source:
incidence:
infection_stage: "I"
probability:
value: 0.05
delay:
value: 7
duration:
value: 10
name: currHosp # will track number of current hospitalizations (ie prevalence)
incidDeath: #incidence of deaths
source: incidHosp
probability:
value: 0.2
delay:
value: 14
outcome_modifiers:
scenarios:
- test_limits
modifiers:
# assume that due to limitations in testing, initially the case detection probability was lower
test_limits:
method: SinglePeriodModifier
parameter: incidCase
subpop: "all"
period_start_date: 2020-02-01
period_end_date: 2020-06-01
value:
distribution: truncnorm
mean: 0.5
sd: 0.001
a: 0
b: 1
perturbation:
distribution: truncnorm
mean: 0
sd: 0.025
a: -0.1
b: 0.1
inference:
iterations_per_slot: 1
do_inference: TRUE
gt_data_path: data/sample_2pop_cases.csv
statistics:
sum_hosp:
name: "Weekly incident hospitalizations"
aggregator: sum
period: "1 weeks"
#gt_start_date:
#gt_end_date:
sim_var: incidHosp
data_var: incidH
remove_na: TRUE
add_one: TRUE
likelihood:
dist: norm_cov
param: 0.3