I think it works

CovidAlertVaccinationModel/abm_timeseries.jl

@@ -29,7 +29,7 @@ function solve_and_plot_parameters()
     display(sum.(eachrow(ymo_vaccination_ts)) ./avg_populations) 
 
     l = length(p)
-    # savefig(plot(p...; layout = (l,1), size=(800,400*l),leftmargin = 12Plots.mm),"timeseries.pdf")
+    savefig(plot(p...; layout = (l,1), size=(800,400*l),leftmargin = 12Plots.mm),"timeseries.pdf")
     return out
 end
 

CovidAlertVaccinationModel/src/ABM/model_setup.jl

@@ -19,7 +19,7 @@
         π_base_o = -0.95,
         η = 0.5,
         κ = 0.0,
-        ω = 0.0053,
+        ω = 0.053,
         ω_en = 0.01,
         Γ = 1/7,
         ξ = 5.0,
@@ -74,7 +74,6 @@ mutable struct ModelSolution{T,InfNet,SocNet,WSMixingDist,RestMixingDist,Recorde
     index_vectors::Vector{Vector{Int}}
     demographics::Vector{AgentDemographic}
     is_app_user::Vector{Bool}
-    status_totals::Vector{Int}
     ws_matrix_tuple::WSMixingDist
     rest_matrix_tuple::RestMixingDist
     immunization_countdown::Vector{Int}
@@ -101,9 +100,9 @@ mutable struct ModelSolution{T,InfNet,SocNet,WSMixingDist,RestMixingDist,Recorde
         covid_alert_notifications = zeros(Bool,14,nodes) #two weeks worth of values
         time_of_last_alert = fill(-1,nodes) #time of last alert is negative if no alert has been recieved
 
-        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; record_degrees)
+        output_data.recorded_status_totals[:,1] = [count(==(AgentStatus(i)), u_0_inf) for i in 1:AgentStatus.size]
         
         return new{T,typeof(infected_mixing_graph),
         typeof(soc_mixing_graph),typeof(ws_matrix_tuple),
@@ -122,7 +121,6 @@ mutable struct ModelSolution{T,InfNet,SocNet,WSMixingDist,RestMixingDist,Recorde
             index_vectors,
             demographics,
             is_app_user,
-            status_totals,
             ws_matrix_tuple,
             rest_matrix_tuple,
             immunization_countdown,

CovidAlertVaccinationModel/src/ABM/output.jl

@@ -73,7 +73,6 @@ Initialize a Recorder filled with (copies) of val.
 
 function record!(t,modelsol)
     recorder = modelsol.output_data
-    recorder.recorded_status_totals[:,t] .= modelsol.status_totals
     alerts = filter(>(0),modelsol.time_of_last_alert)
     if !isempty(alerts)
         mean_alert_time = mean(t .- alerts) 

CovidAlertVaccinationModel/src/ABM/solve.jl

@@ -33,14 +33,16 @@ end
         time_of_last_alert[agent] = t
     end
 end
-function agent_transition!(modelsol,agent, from::AgentStatus,to::AgentStatus) 
-    @unpack u_next_inf,status_totals, immunization_countdown = modelsol
+function agent_transition!(t,modelsol,agent, from::AgentStatus,to::AgentStatus) 
+    @unpack u_next_inf,output_data, immunization_countdown = modelsol
     immunization_countdown[agent] = -1 
-    status_totals[Int(from)] -= 1
-    status_totals[Int(to)] += 1
+    if t<size(output_data.recorded_status_totals,2)-1
+        output_data.recorded_status_totals[Int(from), t+1] -= 1
+        output_data.recorded_status_totals[Int(to), t+1] += 1
+    end
     u_next_inf[agent] = to
 end
-function infect_susceptible_agent!(t, modelsol, agent,agent_inf_status,agent_demo,α_vec,β_vec)
+function is_susceptible_infected!(t, modelsol, agent,agent_inf_status,agent_demo,α_vec,β_vec)
     @unpack u_inf,inf_network, output_data = modelsol
 
     for mixing_graph in inf_network.graph_list[t], neighbor in neighbors(mixing_graph,agent)    
@@ -50,14 +52,14 @@ function infect_susceptible_agent!(t, modelsol, agent,agent_inf_status,agent_dem
             if rand(Random.default_rng(Threads.threadid())) < infection_threshold && agent_inf_status == Susceptible
                 output_data.daily_cases_by_age[Int(agent_demo),t]+=1
                 output_data.daily_unvac_cases_by_age[Int(agent_demo),t]+=1
-                agent_transition!(modelsol, agent, Susceptible,Infected)
+                agent_transition!(t,modelsol, agent, Susceptible,Infected)
                 return true
             end
         end
     end
     return false
 end
-function infect_immunized_agent!(t, modelsol, agent,agent_inf_status,agent_demo,α_vec,β_vec)
+function is_immunized_infected!(t, modelsol, agent,agent_inf_status,agent_demo,α_vec,β_vec)
     @unpack u_inf,inf_network, output_data = modelsol
     for mixing_graph in inf_network.graph_list[t], neighbor in neighbors(mixing_graph,agent)    
         if u_inf[neighbor] == Infected
@@ -65,7 +67,7 @@ function infect_immunized_agent!(t, modelsol, agent,agent_inf_status,agent_demo,
             if rand(Random.default_rng(Threads.threadid())) < infection_threshold && agent_inf_status == Immunized
                 immunization_ineffective = rand(Random.default_rng(Threads.threadid())) < 1- α_vec[Int(agent_demo)]
                 if immunization_ineffective
-                    agent_transition!(modelsol,agent, Immunized,Infected)
+                    agent_transition!(t,modelsol,agent, Immunized,Infected)
                     output_data.daily_cases_by_age[Int(agent_demo),t]+=1
                     return true
                 end
@@ -80,10 +82,9 @@ function vaccinate_agent!(t,agent,immunization_countdown)
     end
 end
 
-function update_vaccination_opinion_state!(t,agent,modelsol,total_infections,π_base_vec)
-    @unpack immunization_intervals, η,Γ,ζ, ω, ω_en,ξ = modelsol.params
-    @unpack demographics,time_of_last_alert, soc_network,u_vac,u_next_vac,is_app_user,output_data = modelsol
-
+function update_vaccination_opinion_state!(t,agent,modelsol,vaccinate_today,total_infections,π_base_vec)
+    @unpack infection_introduction_day,immunization_intervals, η,Γ,ζ, ω, ω_en,ξ = modelsol.params
+    @unpack immunization_countdown,demographics,time_of_last_alert, soc_network,u_vac,u_next_vac,is_app_user,output_data = modelsol
     random_soc_network = sample(Random.default_rng(Threads.threadid()), soc_network.graph_list[t])
     if !isempty(neighbors(random_soc_network,agent))
         random_neighbour = sample(Random.default_rng(Threads.threadid()), neighbors(random_soc_network.g,agent))
@@ -101,6 +102,9 @@ function update_vaccination_opinion_state!(t,agent,modelsol,total_infections,π_
             else
                 if rand(Random.default_rng(Threads.threadid())) < Φ(vac_payoff,ξ)
                     u_next_vac[agent] = true
+                    if vaccinate_today && t>infection_introduction_day
+                        vaccinate_agent!(t, agent, immunization_countdown)
+                    end
                 end
             end
             return vac_payoff
@@ -136,7 +140,7 @@ function sample_initial_nodes(nodes,graphs,I_0_fraction)
 end    
 
 function agents_step!(t,modelsol,vaccinate_today)
-    @unpack recovery_rate,β_y,β_m,β_o,α_y,α_m,α_o,π_base_y,π_base_m,π_base_o,ζ,immunization_intervals = modelsol.params
+    @unpack infection_introduction_day,recovery_rate,β_y,β_m,β_o,α_y,α_m,α_o,π_base_y,π_base_m,π_base_o,ζ,immunization_intervals = modelsol.params
     @unpack u_inf,u_vac,u_next_vac,u_next_inf,demographics,inf_network, is_app_user, immunization_countdown, output_data = modelsol
 
     u_next_inf .= u_inf
@@ -153,16 +157,16 @@ function agents_step!(t,modelsol,vaccinate_today)
     for (agent,(agent_inf_status,agent_is_vaccinator,agent_demo)) in enumerate(zip(u_inf,u_vac,demographics))
 
         if agent_inf_status == Susceptible
-            infect_susceptible_agent!(t, modelsol, agent,agent_inf_status,agent_demo,α_vec,β_vec)
-            if vaccinate_today && agent_is_vaccinator
+            is_susceptible_infected!(t, modelsol, agent,agent_inf_status,agent_demo,α_vec,β_vec)
+            if vaccinate_today && agent_is_vaccinator && t<(infection_introduction_day-1)
                 vaccinate_agent!(t, agent, immunization_countdown)
             end
 
         elseif agent_inf_status == Immunized
-            infect_immunized_agent!(t, modelsol, agent,agent_inf_status,agent_demo,α_vec,β_vec)
+            is_immunized_infected!(t, modelsol, agent,agent_inf_status,agent_demo,α_vec,β_vec)
         elseif agent_inf_status == Infected
             if rand(Random.default_rng(Threads.threadid())) < recovery_rate
-                agent_transition!(modelsol, agent, Infected,Recovered)
+                agent_transition!(t,modelsol, agent, Infected,Recovered)
             end
         end
         if t>first(minimum(modelsol.params.immunization_intervals))
@@ -174,16 +178,15 @@ function agents_step!(t,modelsol,vaccinate_today)
         if immunization_countdown[agent] == 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!(modelsol, agent, Susceptible,Immunized)
+            agent_transition!(t,modelsol, agent, Susceptible,Immunized)
         elseif immunization_countdown[agent]>0
             fit!(output_data.avg_weighted_degree[Int(agent_demo)],weighted_degree_of_i)
             immunization_countdown[agent] -= 1
         else
             fit!(output_data.avg_weighted_degree[Int(agent_demo)],weighted_degree_of_i)
         end
-
-        update_vaccination_opinion_state!(t, agent, modelsol, modelsol.status_totals[Int(Infected)],π_base_vec) #very bad pls change
-
+        total_infected = modelsol.output_data.recorded_status_totals[Int(Infected), t]
+        update_vaccination_opinion_state!(t, agent, modelsol,vaccinate_today, total_infected,π_base_vec)
     end
 
     output_data.total_vaccinators[t] = count(==(true),u_vac) 
@@ -209,7 +212,7 @@ function solve!(modelsol)
             if !isempty(init_indices)
                 inf_index = pop!(init_indices)
                 modelsol.u_inf[inf_index] = Infected
-                modelsol.status_totals[Int(Infected)] += 1
+                modelsol.output_data.recorded_status_totals[1,Int(Infected)] += 1
             end
            
         end
@@ -224,7 +227,7 @@ function solve!(modelsol)
             vaccinate_today = false
         end
 
-        total_weight += agents_step!(t,modelsol,vaccinate_today)
+        agents_step!(t,modelsol,vaccinate_today)
         #advance agent states based on the new network
         
         record!(t,modelsol)