first heatmaps

CovidAlertVaccinationModel/Project.toml

@@ -7,6 +7,7 @@ version = "0.1.0"
 AxisKeys = "94b1ba4f-4ee9-5380-92f1-94cde586c3c5"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
 CurveFit = "5a033b19-8c74-5913-a970-47c3779ef25c"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"

CovidAlertVaccinationModel/abm_timeseries.jl

@@ -6,7 +6,7 @@ using CovidAlertVaccinationModel:vaccination_data,ymo_vac,ymo_attack_rate
 const samples = 10
 
 function solve_and_plot_parameters()
-    p =  CovidAlertVaccinationModel.get_parameters()
+    p =  CovidAlertVaccinationModel.get_app_parameters()
     display(p)
     out,avg_populations = mean_solve(samples, p,DebugRecorder)
     p = plot_model(nothing,[nothing],[out],p.infection_introduction_day,p.immunization_begin_day)
@@ -14,7 +14,7 @@ function solve_and_plot_parameters()
     ymo_vaccination_ts = mean.(out.daily_immunized_by_age)
 
     total_postinf_vaccination = mean.(out.total_postinf_vaccination)#sum.(eachrow(ymo_vaccination_ts[:,180:end]))
-    final_size = mean.(out.final_size_by_age)#sum.(eachrow(mean.(out.daily_unvac_cases_by_age)))
+    final_size = mean.(out.unvac_final_size_by_age)#sum.(eachrow(mean.(out.daily_unvac_cases_by_age)))
     total_preinf_vaccination = mean.(out.total_preinf_vaccination)#sum.(eachrow(ymo_vaccination_ts[:,1:180]))
     target_final_size = ymo_attack_rate .*avg_populations
     target_preinf_vac = ymo_vac .* sum(vaccination_data[1:4]) .* avg_populations

CovidAlertVaccinationModel/prem_mixing_approx.jl

-using CovidAlertVaccinationModel:abm,ModelSolution, get_parameters, neighbors, GraphEdge, get_weight, contact_time_distributions
-using LightGraphs
-function approximate_mixing_matricies_1(sol)
-    mean_mixing_degree = zeros(3,3)
-    mean_mixing_weighted_degree = zeros(3,3)
-    display(p.O_distribution_shift)
-    te_missed = zeros(8)
-    te = zeros(8)
-        edgelist = []
-        for g_list in sol.inf_network.graph_list
-            for (k,g) in enumerate(g_list)
-                # display(g.mixing_edges.weights_dict)
-                for e in keys(g.mixing_edges.weights_dict)
-                    demo_i = sol.demographics[e.a]
-                    demo_j = sol.demographics[e.b]
-                    if(has_edge(g.g,e.a,e.b))
-                        mean_mixing_degree[Int(demo_i), Int(demo_j)] += 1
-                        mean_mixing_degree[Int(demo_j), Int(demo_i)] += 1
-                        mean_mixing_weighted_degree[Int(demo_i), Int(demo_j)] += get_weight(g,GraphEdge(e.a,e.b))
-                        mean_mixing_weighted_degree[Int(demo_j), Int(demo_i)] += get_weight(g,GraphEdge(e.a,e.b))
-                        te[k] += 1
-                    else 
-                        push!(edgelist,e)
-                        te_missed[k] += 1
-                    end
-                end 
-             end
-    end
-    mean_mixing_degree ./= (500 .* length.(sol.index_vectors) * 2)
-    mean_mixing_weighted_degree ./= (500 .* length.(sol.index_vectors) * 2)
-    display(mean_mixing_degree)
-    display(mean_mixing_weighted_degree)
-    display(te_missed)
-    return sol
-end
-function approximate_mixing_matricies_2(sol)
-mean_mixing_degree = zeros(3,3)
-mean_mixing_weighted_degree = zeros(3,3)
-te_missed = zeros(8)
-    display(p.O_distribution_shift)
-        for g_list in sol.inf_network.graph_list
-            for g in g_list
-                # display(g.mixing_edges.weights_dict)
-                for e in edges(g.g)  
-                    demo_i = sol.demographics[src(e)]
-                    demo_j = sol.demographics[dst(e)]
-                    mean_mixing_degree[Int(demo_i), Int(demo_j)] += 1#get_weight(g,GraphEdge(node_i,node_j)) /2
-                    mean_mixing_degree[Int(demo_j), Int(demo_i)] += 1#get_weight(g,GraphEdge(node_i,node_j)) /2
-                    mean_mixing_weighted_degree[Int(demo_i), Int(demo_j)] += get_weight(g,GraphEdge(src(e),dst(e)))
-                    mean_mixing_weighted_degree[Int(demo_j), Int(demo_i)] += get_weight(g,GraphEdge(src(e),dst(e)))
-                end 
-             end
-    end
-    mean_mixing_degree ./= (500 .* length.(sol.index_vectors) * 2)
-    mean_mixing_weighted_degree ./= (500 .* length.(sol.index_vectors) * 2)
-    return sol
-end
-
-
-p = get_parameters()
-sol = abm(p,nothing)
-approximate_mixing_matricies_1(sol);
-approximate_mixing_matricies_2(sol);
-println("dasdlkjasdas")
-
-
-
-#rerun with weights
-#
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CovidAlertVaccinationModel/src/ABM/model_setup.jl

@@ -15,8 +15,8 @@ function get_parameters()#(0.0000,0.00048,0.0005,0.16,-1.30,-1.24,-0.8,0.35,0.35
         π_base_o = -0.95,
         η = 0.0,
         κ = 0.0,
-        ω = 0.0055,
-        ω_en = 0.00,
+        ω = 0.0,
+        ω_en = 0.0005,
         Γ = 1/7,
         ξ = 5.0,
         notification_parameter = 0.001,

CovidAlertVaccinationModel/src/ABM/parameter_planes.jl

@@ -17,7 +17,7 @@ if !ispath(univariate_path)
     mkdir(univariate_path)
 end
 function univariate_simulations()
-    len = 10
+    len = 20
     univarate_test_list = (
         # (:I_0_fraction, range(0.0, 0.05; length = len)), 
         # (:base_transmission_probability, range(0.0002, 0.002; length = len)),
@@ -48,14 +48,14 @@ end
 
 using AxisKeys 
 function multivariate_simulations()
-    len = 10
+    len = 15
     samples = 10
     app_simulations = (
         (:η, range(0.0, 0.01; length = len)),
-        (:ω_en, range(0.0, 0.0005; length = len)),
+        (:ω_en, range(0.0, 0.05; length = len)),
         # (:notification_threshold, (1:len)),
     )
-    run_multivariate_sims(app_simulations,1)
+    run_multivariate_sims(app_simulations,samples)
 
 
     # for ((varname,_),p) in zip(univarate_test_list,plt_list)
@@ -65,26 +65,52 @@ end
 using ProgressMeter
 function run_multivariate_sims(sims,samples)
     varnames, sim_ranges = zip(sims...)
+
+    without_app_future = @spawn mean_solve(samples,get_parameters(),HeatmapRecorder)
+
     default_parameters = get_app_parameters() 
     simvars = Iterators.product(sim_ranges...)
     progmeter = Progress(length(simvars))
-    output = ThreadsX.map(simvars) do vars
+    app_output = ThreadsX.map(simvars) do vars
         vars_with_names = NamedTuple{varnames}(vars)
         parameters = merge(default_parameters,vars_with_names)
-        out,_ = mean_solve(samples, parameters,DebugRecorder) 
+        out,_ = mean_solve(1, parameters,HeatmapRecorder) 
         next!(progmeter)
         return out
     end
     display(length(simvars))
     fname = join(string.(varnames),"_")
-    keyed_output = KeyedArray(output;NamedTuple{varnames}(sim_ranges)...)
+    keyed_output = KeyedArray(app_output;NamedTuple{varnames}(sim_ranges)...)
     path = joinpath(PACKAGE_FOLDER,"abm_output","$fname.dat")
-    serialize(path,keyed_output)
+    serialize(path,(fetch(without_app_future)[1],keyed_output))
     return fname
 end
-
+using ColorSchemes
+using LaTeXStrings
 function plot_parameter_plane(input_fname)
-    map(1: length(axiskeys(output)[end])) do i
-        
+    path = joinpath(PACKAGE_FOLDER,"abm_output","$input_fname.dat")
+    output_no_app, output = deserialize(path)
+    var_ranges = axiskeys(output)
+    vars = (L"\eta",L"\omega_{en}")
+
+
+
+    mean_final_size(p) = mean(reduce(merge!,p.final_size_by_age))
+    base_outcome = mean_final_size(output_no_app)
+    final_size_map = map(x-> (mean_final_size(x) - base_outcome),output)
+    
+    mean_weighted_degree_change(p) = mean(p.avg_weighted_degree_of_vaccinators)-mean(p.avg_weighted_degree)
+    weighted_degree_map = map(mean_weighted_degree_change,output)
+    cs = cgrad([:blue,:orange])
+
+    datamaps = [weighted_degree_map, final_size_map]
+    fnames = ["wdg_change.pdf", "final_size_change.pdf"]
+    titles = [
+        "Average w. deg. of vaccinators minus average w. deg.",
+        "Change in average final size of infection outbreak",
+    ]
+    for (fname,title,datamap) in zip(fnames,titles,datamaps)
+        p = heatmap(var_ranges[1],var_ranges[2],datamap; title, xlabel = vars[1], ylabel = vars[2], seriescolor=cs, size = (800,600))
+        savefig(p,joinpath(PACKAGE_FOLDER,"plots","app_heatmaps","$fname"))
     end
 end
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