optionally record degrees

CovidAlertVaccinationModel/src/ABM/abm.jl

@@ -34,7 +34,7 @@ end
 Run the model with given parameter tuple and output recorder. See `get_parameters` for list of parameters. See `output.jl` for the list of recorders. Currently just 'DebugRecorder`.
 """
 function abm(parameters)
-    model_sol = ModelSolution(parameters.sim_length,parameters,parameters.num_households)
+    model_sol = ModelSolution(parameters.sim_length,parameters,parameters.num_households; record_degrees = false)
     solve!(model_sol)
     return model_sol
 end

CovidAlertVaccinationModel/src/ABM/model_setup.jl

@@ -78,7 +78,7 @@ mutable struct ModelSolution{T,InfNet,SocNet,WSMixingDist,RestMixingDist,Recorde
     rest_matrix_tuple::RestMixingDist
     immunization_countdown::Vector{Int}
     output_data::RecorderType
-    @views function ModelSolution(sim_length,params::T,num_households) where T
+    @views function ModelSolution(sim_length,params::T,num_households; record_degrees = false) where T
         demographics,base_network,index_vectors = generate_population(num_households)
         nodes = length(demographics)
         pop_sizes = length.(index_vectors)
@@ -105,7 +105,8 @@ mutable struct ModelSolution{T,InfNet,SocNet,WSMixingDist,RestMixingDist,Recorde
 
         status_totals = [count(==(AgentStatus(i)), u_0_inf) for i in 1:AgentStatus.size]
         immunization_countdown = fill(-1, nodes) #immunization countdown is negative if not counting down
-        output_data = Recorder(0,sim_length)
+        output_data = Recorder(0,sim_length; record_degrees)
+        
         return new{T,typeof(infected_mixing_graph),
         typeof(soc_mixing_graph),typeof(ws_matrix_tuple),
         typeof(rest_matrix_tuple), typeof(output_data)}(

CovidAlertVaccinationModel/src/ABM/output.jl

@@ -6,6 +6,7 @@ import OnlineStats.fit!
 Recorder should store everything we might want to know about the model output. 
 """
 struct Recorder{ElType,ArrT1,ArrT2,ArrT3,StatAccumulator}
+
     recorded_status_totals::ArrT1
     daily_cases_by_age::ArrT3
     total_vaccinators::ArrT2
@@ -18,11 +19,13 @@ struct Recorder{ElType,ArrT1,ArrT2,ArrT3,StatAccumulator}
     mean_time_since_last_notification::ArrT2
     daily_immunized_by_age::ArrT3
     daily_unvac_cases_by_age::ArrT3
+    record_degrees_flag::Bool
     avg_weighted_degree_of_vaccinators::Vector{StatAccumulator}
     avg_weighted_degree::Vector{StatAccumulator}
 
-    function Recorder(val::T,sim_length) where T
+    function Recorder(val::T,sim_length; record_degrees = false) where T
     
+
         totals = [copy(val) for i in 1:4, j in 1:sim_length]
         state_totals = @LArray totals (S = (1,:),I = (2,:),R = (3,:), V = (4,:))
         total_vaccinators = [copy(val) for j in 1:sim_length]
@@ -41,7 +44,7 @@ struct Recorder{ElType,ArrT1,ArrT2,ArrT3,StatAccumulator}
         final_size_by_age =  [copy(val) for i in 1:3]
     
         avg_weighted_degree_of_vaccinators = [Variance() for _ in 1:3]
-        avg_weighted_degree = Variance()
+        avg_weighted_degree = [Variance() for _ in 1:3]
     
         return new{T,typeof(state_totals),typeof(total_vaccinators),typeof(daily_immunized_by_age),eltype(avg_weighted_degree)}(
             state_totals,
@@ -56,6 +59,7 @@ struct Recorder{ElType,ArrT1,ArrT2,ArrT3,StatAccumulator}
             mean_time_since_last_notification,
             daily_immunized_by_age,
             daily_unvac_by_age,
+            record_degrees,
             avg_weighted_degree_of_vaccinators,
             avg_weighted_degree
         )

CovidAlertVaccinationModel/src/ABM/solve.jl

@@ -84,17 +84,18 @@ Base.@propagate_inbounds @views function update_infection_state!(t,modelsol; rec
                agent_transition!(i, Infected,Recovered)
             end
         end
-        weighted_degree_of_i::Int = record_degrees ? weighted_degree(t,i,inf_network) : 0
+
+        weighted_degree_of_i::Int = output_data.record_degrees_flag ? weighted_degree(t,i,inf_network) : 0
         if immunization_countdown[i] == 0 
             output_data.daily_immunized_by_age[Int(agent_demo),t] += 1
             fit!(output_data.avg_weighted_degree_of_vaccinators[Int(agent_demo)],weighted_degree_of_i)
             agent_transition!(i, Susceptible,Immunized)
 
         elseif immunization_countdown[i]>0
-            fit!(output_data.avg_weighted_degree,weighted_degree_of_i)
+            fit!(output_data.avg_weighted_degree[Int(agent_demo)],weighted_degree_of_i)
             immunization_countdown[i] -= 1
         else
-            fit!(output_data.avg_weighted_degree,weighted_degree_of_i)
+            fit!(output_data.avg_weighted_degree[Int(agent_demo)],weighted_degree_of_i)
         end
     end
 end