Model Configuration

The model is configured on a global-level to define parameters for the entire population. Node-specific parameters can be defined for specific geographic regions such as states, districts, and wards separately. Node-wise configuration overrides global configuration.

Global Configuration:

Parameters

pop_frac: population fractions of different age groups: [0-19,20-39,40-59,60+]

rate_frac: rate multiplier of different age groups (default value of 1 corresponds to a rate of ~2.3)

rates: basic reproduction number R0 is the number of secondary infections each infected individual produces (largest eigenvalue chosen from the 4×4 matrix used to model the four age categories)

param: global intervention is a time event that impacts the parameters of the simulation (for eg: lockdown)

intervention_date: date when intervention was put in place

intervention_type: distinguishes between global and nodal interventions

intervention_day: number of days after which the intervention was put in place (void from latest version)

CFR: case fatality rate of different age groups (percentage of infected people who pass away)

P_SEVERE: hospitalization rate of different age groups (percentage of infected people who need to be hospitalized)

I0: initial number of infectious persons at the start of the simulation

E0: initial number of exposed persons at the start of the simulation

D_death: average time duration an individual takes to move from infection to death

D_hospital_lag: average time duration for an infected person to be hospitalised (assuming the individual is hospitalised)

D_incubation: time duration an exposed person incubates for before getting infected

D_infectious: time duration an infectious individual (symptomatic and asymptomatic) is shedding the virus

D_recovery_mild: recovery time for mild cases (non-hospitalised individuals)

D_recovery_severe: average length of stay in a hospital for severe cases

Fatal0: initial number of fatal persons at the start of the simulation

Mild0: initial number of recovering mild cases at the start of the simulation

R0: initial number of removed persons (recovered or dead) at the start of the simulation

R_Fatal0: initial number of removed fatal cases at the start of the simulation

R_Mild0: initial number of removed mild cases at the start of the simulation

R_Severe0: initial number of removed hospitalized cases at the start of the simulation

S0: initial number of susceptible persons (number of people who are not immune to the disease at the start of the simulation, typically encompasses the entire population)

Severe0: initial number of severe cases recovering at home at the start of the simulation

Severe_H0: initial number of severe cases recovering at hospitals at the start of the simulation

{
"pop_frac": [0.44, 0.35, 0.15, 0.06], "rate_frac": [1, 1, 1, 1], "rates": 2.3,
"param": [
{
   "intervention_date": "04-02-2020",
   "rate_frac": [0.2, 0.2, 0.2, 0.2],
   "intervention_type": "global"
   }
],
"CFR": [0.01,0.065,0.125,0.25],
"P_SEVERE": [0.15, 0.35, 0.6, 0.9],
"I0": 50,
"E0": 75,
"D_death": 14,
"D_hospital_lag": 5,
"D_incubation": 5.2,
"D_infectious": 2.9,
"D_recovery_mild": 11.1,
"D_recovery_severe": 28.6,
"Fatal0": 0,
"Mild0": 0,
"R0": 0,
"R_Fatal0": 0,
"R_Mild0": 0,
"R_Severe0": 0,
"S0": -1,
"Severe0": 0,
"Severe_H0": 0
}

Nodal Configuration (State, District, etc):

Parameters

node: name of the node ie the geographical boundary for which the simulation is executed

t0: offset value ie the starting day for the specific simulation in comparison to the simulation’s reference

pop: total population within the node’s geographic boundary

nodal_param_change: node-wise interventions ie time events that impacts only the geographical area for which the node is defined

intervention_date: date when intervention was put in place

intervention_type: distinguishes between global and nodal interventions

intervention_day: number of days after which the intervention was put in place

rate_frac: rate multiplier of different age groups (default value of 1 corresponds to a rate of ~2.3)

[
{
"node": "Andhra Pradesh",
"pop": 57013668,
"nodal_param_change": [
{
"intervention_date": "04-03-2020",
"intervention_type": "local",
"delI": 20,
"rate_frac": [0.4, 0.4, 0.4, 0.4]
}
]
},
{ "node": "Arunachal Pradesh", "pop": 1591286 },
{ "node": "Assam", "pop": 35886412 },
{ "node": "Bihar", "pop": 119714369 },
{ "node": "Chandigarh", "pop": 1213767 },
{ "node": "Chhattisgarh", "pop": 29376977 },
{
"node": "Delhi",
"pop": 19306132,
"nodal_param_change": [
{
"intervention_date": "04-03-2020",
"intervention_type": "local",
"delI": 50,
"rate_frac": [0.6, 0.6, 0.6, 0.6]
}
]
},

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