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seir_model.py

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+import numpy as np
+
+def p_exposed(params, substep, state_history, previous_state):
+  signal = previous_state["S"]*params["infection_rate"]*previous_state["I"]/N
+  return {"delta_s":np.ceil(signal)}
+  
+def p_infected(params, substep, state_history, previous_state):
+  signal = params["exposure_rate"]*previous_state["E"]
+  return {"delta_i":np.ceil(signal)}
+  
+def p_recovered(params, substep, state_history, previous_state):
+  signal = params["recovery_rate"]*previous_state["I"]
+  return {"delta_r":np.ceil(signal)}
+
+def s_susceptible(params, substep, state_history, previous_state, policy_input):
+  new_S = previous_state["S"] - policy_input["delta_s"]
+  return ("S", max(new_S,0))
+  
+def s_exposed(params, substep, state_history, previous_state, policy_input):
+  new_E = previous_state["E"] + policy_input["delta_s"] - policy_input["delta_i"]
+  return ("E", max(new_E,0))
+
+def s_infected(params, substep, state_history, previous_state, policy_input):
+  new_I = previous_state["I"] + policy_input["delta_i"] - policy_input["delta_r"]
+  return ("I", max(new_I,0))
+  
+def s_recovered(params, substep, state_history, previous_state, policy_input):
+  new_R = previous_state["R"] + policy_input["delta_r"]
+  return ("R", max(new_R, 0))