parameter fits

CovidAlertVaccinationModel/src/ABM/parameter_optimization.jl

@@ -15,9 +15,16 @@ function solve_w_parameters(default_p, p_names, new_p_list)
 end
 
 function fit_distribution_parameters(p_tuple)
-    p_names = (:ω,:π_base_y,:π_base_m,:π_base_o,:ζ)
+    p_names = (:ω,:β_y,:β_m,:β_o,:π_base_y,:π_base_m,:π_base_o,:ζ)        
+    
+    β_y_val = 0.00077
+    β_m_val = 0.00065
+    β_o_val = 0.75
     priors = Factored(
         Uniform(0.0,0.01),
+        TriangularDist(β_y_val-0.0005,β_y_val+0.0005),
+        TriangularDist(β_m_val-0.0005,β_m_val+0.0005),
+        TriangularDist(β_o_val-0.0005,β_o_val+0.0005), 
         Uniform(-5.0,3.0),
         Uniform(-5.0,3.0),
         Uniform(-5.0,3.0), 
@@ -26,18 +33,8 @@ function fit_distribution_parameters(p_tuple)
     #simulation begins in july
     #60 days for opinion dynamics to stabilize, then immunization begins in september,
     #infection introduced at beginning of december
-    sim_length = 400
-    p_tuple_adjust = merge(p_tuple,
-        (
-            sim_length = sim_length,
-            I_0_fraction = 0.005,
-            immunization_begin_day = 60, 
-            infection_introduction_day = 180,
-            immunizing = true,
-        )
-    )
     function cost(p)
-        output,model = solve_w_parameters(p_tuple_adjust, p_names,p)
+        output,model = solve_w_parameters(p_tuple, p_names,p)
         ymo_vaccination_ts = output.daily_immunized_by_age
 
         total_postinf_vaccination = sum.(eachrow(ymo_vaccination_ts[:,181:end]))
@@ -57,17 +54,17 @@ function fit_distribution_parameters(p_tuple)
         + sum((total_postinf_vaccination .- total_postinf_vaccination).^2)  
         + sum((final_size .- target_final_size).^2)
     end 
-    #    display(cost((0.005,0.00075, 0.00063,0.75,-1.4,-1.4,-0.95)))
+    #    display(cost((0.005,0.00075, 0.00063,0.75,-1.4,-1.4,-0.95,1.25)))
     
-    out = smc(priors,cost; verbose = true, nparticles = 250, parallel = true)
+    out = smc(priors,cost; verbose = true, nparticles = 1000, parallel = true)
     return NamedTuple{p_names}(ntuple(i -> out.P[i].particles,length(p_names)))
 end
 function fit_parameters(default_parameters)
     # pre_inf_behaviour_parameters_path =joinpath(PACKAGE_FOLDER,"abm_parameter_fits","pre_inf_behaviour_parameters.dat")
     # post_inf_behaviour_parameters_path = joinpath(PACKAGE_FOLDER,"abm_parameter_fits","post_inf_behaviour_parameters.dat")
     fit_all_parameters_path = joinpath(PACKAGE_FOLDER,"abm_parameter_fits","fit_all_parameters.dat")
-    # output = fit_distribution_parameters(default_parameters)
-    # serialize(fit_all_parameters_path,output)
+    output = fit_distribution_parameters(default_parameters)
+    serialize(fit_all_parameters_path,output)
 
     
     fitted_parameter_tuple = deserialize(fit_all_parameters_path)

CovidAlertVaccinationModel/src/ABM/solve.jl

@@ -176,7 +176,7 @@ end
 
 function solve!(modelsol,recordings...)
     init_indices = rand(Random.default_rng(Threads.threadid()), 1:modelsol.nodes, round(Int,modelsol.nodes*modelsol.params.I_0_fraction))
-    @show length(init_indices)
+
     for t in 1:modelsol.sim_length
         agents_step!(t,modelsol,init_indices) 
         #advance agent states based on the new network

CovidAlertVaccinationModel/test/ABM/mixing_test.jl

 
-using CovidAlertVaccinationModel:generate_contact_vectors!
+using CovidAlertVaccinationModel:abm,generate_contact_vectors!
 using CovidAlertVaccinationModel:ModelSolution,AgentDemographic,mean,get_weight
 using CovidAlertVaccinationModel:AgentStatus,get_u_0,get_parameters,solve!, DebugRecorder, WeightedGraph, contact_time_distributions,GraphEdge
 using LightGraphs
@@ -135,8 +135,5 @@ end
         for i in eachindex(mixing_dist)
             @test mean(mixing_dist[i]) ≈ mean(dist[i])  atol = 0.2
         end
-        display(var.(mixing_dist))
-        display(var.(dist))
-        display("====")
     end
 end
\ No newline at end of file