optimization in solve

CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl

 """
-A type that stores a pair of nodes representing an edge in the graph. 
+A type that stores a destination node and a weight representing an directed edge in the graph. 
 
-We need to define a custom type for these so we can define a hash function on graph edges, in order to more efficiently use them in hashmaps (dictionaries)
+We need to define a custom type for these so we can define custom equality on edges.
 """
 struct HalfWeightedEdge
     a::Int
@@ -10,7 +10,7 @@ end
 
 
 """
-Define symmetric edge equality, matches the hash function.
+Define edge equality to exclude the weight.
 """
 function Base.:(==)(e1::HalfWeightedEdge,e2::HalfWeightedEdge)
     return isequal(e1.a,e2.a)

CovidAlertVaccinationModel/src/ABM/solve.jl

@@ -44,7 +44,9 @@ end
 Base.@propagate_inbounds @views function update_infection_state!(t,modelsol; record_degrees = false)
     @unpack β_y,β_m,β_o,α_y,α_m,α_o,recovery_rate,immunizing,immunization_begin_day = modelsol.params
     @unpack u_inf,u_vac,u_next_inf,demographics,inf_network,status_totals, immunization_countdown, output_data = modelsol
-    
+    β_vec = @SVector [β_y,β_m,β_o]
+    α_vec = @SVector [α_y,α_m,α_o]
+
     function agent_transition!(node, from::AgentStatus,to::AgentStatus) 
         immunization_countdown[node] = -1 
         status_totals[Int(from)] -= 1
@@ -64,8 +66,6 @@ Base.@propagate_inbounds @views function update_infection_state!(t,modelsol; rec
                 for he in neighbors_and_weights(mixing_graph,i)
                     j = he.a   
                     if u_inf[j] == Infected && u_next_inf[i] != Infected
-                        β_vec = (β_y,β_m,β_o)
-                        α_vec = (α_y,α_m,α_o)
                         if rand(Random.default_rng(Threads.threadid())) < contact_weight(β_vec[Int(agent_demo)],he.weight[])
                             if agent_status == Immunized && rand(Random.default_rng(Threads.threadid())) < 1- α_vec[Int(agent_demo)]
                                 agent_transition!(i, Immunized,Infected)
@@ -105,11 +105,13 @@ end
 Base.@propagate_inbounds @views function update_vaccination_opinion_state!(t,modelsol,total_infections)
     @unpack infection_introduction_day, π_base_y,π_base_m,π_base_o, η,Γ,ζ, ω, ω_en,ξ = modelsol.params
     @unpack demographics,time_of_last_alert, nodes, soc_network,u_vac,u_next_vac,app_user,app_user_list,output_data = modelsol
-   
+    
+    if t<infection_introduction_day
+        π_base_vector = @SVector [π_base_y,π_base_m,π_base_o]
+    else
+        π_base_vector =  @SVector [π_base_y*ζ,π_base_m*ζ,π_base_o*ζ]
+    end
     for i in 1:nodes
-        π_base = t<infection_introduction_day ? 
-        (π_base_y,π_base_m,π_base_o) : 
-        (π_base_y*ζ,π_base_m*ζ,π_base_o*ζ)
         random_soc_network = sample(Random.default_rng(Threads.threadid()), soc_network.graph_list[t])
         if !isempty(neighbors_and_weights(random_soc_network,i))
                 random_neighbour = sample(Random.default_rng(Threads.threadid()), neighbors_and_weights(random_soc_network,i)).a
@@ -120,7 +122,7 @@ Base.@propagate_inbounds @views function update_vaccination_opinion_state!(t,mod
                     # display(t - time_of_last_alert[app_user_list[i]])
                 end
                 if u_vac[random_neighbour] == u_vac[i]
-                    vac_payoff = π_base[Int(demographics[i])] + total_infections*ω + app_vac_payoff
+                    vac_payoff = π_base_vector[Int(demographics[i])] + total_infections*ω + app_vac_payoff
                     if u_vac[i]
                         # display(1 - Φ(vac_payoff,ξ))
                         if rand(Random.default_rng(Threads.threadid())) < 1 - Φ(vac_payoff,ξ)