Dynamics Model 4 Groups
Adapted from code by Cornell University students Mohammad Kamil (mk848), Carrie Rucker (cmr284), Jacob Shusko (jws383), Kevin Van Vorst (kpv23).
- or_suite.envs.vaccine_allotment.dynamics_model_4groups.dynamics_model(params, population)[source]
A function to run SIR disease dynamics for 4 groups.
- Parameters
params –
(dict) a dictionary containing the following keys and values:
contact_matrix: (np.array of floats) Contact rates between susceptible people in each class and the infected people.
P: (np.array of floats) P = [p1 p2 p3 p4] where pi = Prob(symptomatic | infected) for a person in class i.
H: (np.array of floats) H = [h1 h2 h3 h4] where hi = Prob(hospitalized | symptomatic) for a person in class i.
beta: (float) Recovery rate.
gamma: (int) Vaccination rate.
vaccines: The (int) number of vaccine available for this time period.
priority: The (list of chars) vaccination priority order of the four groups.
time_step: The (float) number of units of time you want the simulation to run for e.g. if all your rates are per day and you want to simulate 7 days, time_step = 7.
population – (np.array of ints) The starting state [S1 S2 S3 S4 A1 A2 A3 A4 I H R].
- Returns
newState: the final state [S1 S2 S3 S4 A1 A2 A3 A4 I H N]. Note that instead of returning the final number of recovered people R, we return N, the number of infections that occurred.
output_dictionary : A (dict) dictionary containing the following keys and values:
clock times: List of the times that each event happened.
c1 asymptomatic: List of counts of A1 for each time in clks.
c2 asymptomatic: List of counts of A2 for each time in clks.
c3 asymptomatic: List of counts of A3 for each time in clks.
c4 asymptomatic: List of counts of A4 for each time in clks.
mild symptomatic: List of counts of I for each time in clks.
hospitalized: List of counts of H for each time in clks.
c1 susceptible: List of counts of S1 for each time in clks.
c2 susceptible: List of counts of S2 for each time in clks.
c3 susceptible: List of counts of S3 for each time in clks.
c4 susceptible: List of counts of S4 for each time in clks.
recovered: List of counts of R for each time in clks.
total infected: int - Total number of infected (including those that were already infected).
total hospitalized: int - Total number of hospitalized individuals (including those that were already hospitalized).
vaccines: int - Total number of vaccines left.
event counts: np.array - Contains the counts for the number of times each of the 22 events occurred.
- Return type
np.array of ints, dict
- Typical usage example:
newState, info = dynamics_model(parameters, population)
- or_suite.envs.vaccine_allotment.dynamics_model_4groups.rand_vacc_update(state, changes, group, eligible, vaccines, count)[source]
Performs a random vaccination and updates the environment accordingly.
- Parameters
state – np.array - The state of the environment when the function is called.
changes – dict - Possible state changes [i,j] where the change is state[i]–, state[j]++
group – int - Current priority group.
eligible – List of ints, groups that are still eligible for vaccination.
vaccines – int - Current number of available vaccines.
count – np.array - Counts of all the events.
- Returns
state : Updated state.
count : Updated event count.
group : Updated priority group.
eligible : Updated eligible list.
vaccines : Updated vaccine count.
- Return type
np.array, int, int, list of ints, int
Notes
This function should only ever be called from inside dynamics model. The vaccination events correspond to indices 18, 19, 20 and 21 in count and changes. We increment the event counter even if we technically did not vaccinate anyone.
- or_suite.envs.vaccine_allotment.dynamics_model_4groups.vacc_update(state, changes, ind, count, flag, group, priority, vaccines)[source]
Performs a vaccination according to the priority order and updates the environment accordingly.
- Parameters
state – np.array - The state of the environment when the function is called.
changes – dict - Possible state changes [i,j] where the change is state[i]–, state[j]++
ind – int - The index corresponding the current vaccination event.
count – np.array - Counts of all the events.
flag – boolean - If true, we have vaccines and people to vaccinate if false, either there are no vaccines left or no people to vaccinate.
group – int - Current priority group; always either 0, 1, 2 or 3.
priority – List of strings, priority order list.
vaccines – int - Vaccine count.
- Returns
newState : Updated state.
count : Updated event counts.
flag : Updated flag for if we should continue to vaccinate.
group : Updated priority group.
priority : Updated priority list.
vaccines : Updated vaccine count.
- Return type
np.array, int, boolean, int, list of strings, int
Notes
This function should only ever be called from inside dynamics model. The vaccination events correspond to indices 18, 19, 20 and 21 in count and changes. We increment the event counter even if we technically did not vaccinate anyone.