posteriors added, ZWDist pdf bugfix

CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl

@@ -12,7 +12,6 @@ import StatsBase:mean
 using Dates
 using ThreadsX
 using DelimitedFiles
-
 using NamedTupleTools
 # using CUDA
 using NetworkLayout:Stress
@@ -30,7 +29,7 @@ include("graphs.jl")
 const RNG = Xoroshiro128Star()
 const population = 14.57e6 #population of ontario
 
-const color_palette = palette(:seaborn_bright) #color theme for the plots
+const color_palette = palette(:seaborn_pastel) #color theme for the plots
 const age_bins = [(0.0, 25.0),(25.0,65.0),(65.0,Inf)] 
 const PACKAGE_FOLDER = dirname(dirname(pathof(CovidAlertVaccinationModel)))
 const infection_data = parse_cases_data()
@@ -43,18 +42,20 @@ using BenchmarkTools
 function main()
     # display(size.(agent_model.demographic_index_vectors))
 
-    agent_model = AgentModel(5000)
+    # Random.seed!(RNG,1)
+    VectorizedRNG.seed!(1)
+    agent_model = AgentModel(1000)
     u_0 = get_u_0(length(agent_model.demographics))
     steps = 300
-    Random.seed!(RNG,1)
 
     @btime solve!($u_0,$get_parameters(),$steps,$agent_model,$vaccinate_uniformly!);    
     # agent_model = AgentModel(10_000)
-    @btime solve!($u_0,$get_parameters(),$steps,$agent_model,$vaccinate_uniformly!);
+    # @btime solve!($u_0,$get_parameters(),$steps,$agent_model,$vaccinate_uniformly!);
+    println("done")
     # sol,graphs = solve!(u_0,get_parameters(),steps,agent_model,vaccinate_uniformly!);
     # return aggregate_timeseries(sol)
     # plot_model_spatial_gif(agent_model.base_network,graphs,sol1)
 end
 
 
-end
\ No newline at end of file
+end

CovidAlertVaccinationModel/src/graphs.jl

@@ -39,8 +39,8 @@ end
 function advance_mixing_graph!(t,mixing_graph,index_vectors)
     mixing_graph.graph .= 0
     mixing_graph.graph .= mixing_graph.base_graph
-    generate_mixing_graph!(mixing_graph.graph, index_vectors,mixing_graph.contact_vector_rest[t])
-    generate_mixing_graph!(mixing_graph.graph, index_vectors,mixing_graph.contact_vector_ws[t])
+    generate_mixing_graph!(mixing_graph.graph, index_vectors,mixing_graph.contact_vector_rest[t],contact_time_distribution_matrix)
+    generate_mixing_graph!(mixing_graph.graph, index_vectors,mixing_graph.contact_vector_ws[t],contact_time_distribution_matrix)
     return nothing
 end
 
@@ -85,7 +85,7 @@ function generate_contact_vectors!(ij_dist,ji_dist,i_to_j_contacts, j_to_i_conta
     return nothing
 end
 #add a bipartite graph derived from mixing matrices onto g
-function generate_mixing_graph!(g,index_vectors,contacts)
+function generate_mixing_graph!(g,index_vectors,contacts,edge_weight_dists)
     for i in 1:length(index_vectors), j in 1:length(index_vectors)  
         random_bipartite_graph_fast_CL!(g,index_vectors[i],index_vectors[j],contacts.contact_array[i,j],contacts.contact_array[j,i])
     end
@@ -117,8 +117,8 @@ function random_bipartite_graph_fast_CL!(g::T,anodes,bnodes,aseq,bseq) where T<:
     @assert sum(aseq) == sum(bseq) "degree sequences must have equal sum"
     astubs = sample(RNG,anodes,StatsBase.weights(aseq./m), m)
     bstubs = sample(RNG,bnodes,StatsBase.weights(bseq./m), m)
-    for k in 1:m
-        g[astubs[k],bstubs[k]] = 1
+    for i in 1:m
+        g[astubs[i],bstubs[i]] = 1
     end
     return g
 end

CovidAlertVaccinationModel/src/model.jl

@@ -25,13 +25,13 @@ function agents_step!(t,u_next,u,population_list,graph,params,index_vectors,vacc
     if t >= vaccination_start_day
         vaccination_algorithm!(Int(floor(vaccines_per_day*length(u))),u_next,u,graph,population_list,index_vectors) 
     end
-    for i in 1:length(u)
+    @inbounds for i in 1:length(u)
         agent_status = u[i]
         agent_demo = population_list[i]
         if agent_status == Susceptible
-            for j in 1:length(u) #LightGraphs.neighbors(graph,i)
-                if graph[i,j] == 1 && u[j] == Infected && rand(RNG) < contact_weight(p, agent_demo,population_list[j])
-                    u_next[i] = Infected
+            @inbounds for j in 1:length(u) #LightGraphs.neighbors(graph,i)
+                if graph[i,j] == 1 && u[j] == Infected && rand(RNG) < contact_weight(p, agent_demo,population_list[j]) #only need contact weights for infected subnetwork
+                    (u_next[i] = Infected)
                 end
             end
         elseif agent_status == Infected

IntervalsModel/Manifest.toml

@@ -115,6 +115,11 @@ git-tree-sha1 = "ac5f2213e56ed8a34a3dd2f681f4df1166b34929"
 uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
 version = "0.12.6"
 
+[[Combinatorics]]
+git-tree-sha1 = "08c8b6831dc00bfea825826be0bc8336fc369860"
+uuid = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
+version = "1.0.2"
+
 [[Compat]]
 deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "SHA", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"]
 git-tree-sha1 = "919c7f3151e79ff196add81d7f4e45d91bbf420b"
@@ -359,6 +364,12 @@ git-tree-sha1 = "1179250d910c99810d8a7ff55c50c4ed68c77a58"
 uuid = "e33a78d0-f292-5ffc-b300-72abe9b543c8"
 version = "2.4.0+0"
 
+[[HypothesisTests]]
+deps = ["Combinatorics", "Distributions", "LinearAlgebra", "Random", "Rmath", "Roots", "Statistics", "StatsBase"]
+git-tree-sha1 = "552892528991c3e17eb60e623f1ac94c0663eb7d"
+uuid = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
+version = "0.10.2"
+
 [[IfElse]]
 git-tree-sha1 = "28e837ff3e7a6c3cdb252ce49fb412c8eb3caeef"
 uuid = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173"
@@ -833,6 +844,12 @@ git-tree-sha1 = "d76185aa1f421306dec73c057aa384bad74188f0"
 uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f"
 version = "0.2.2+1"
 
+[[Roots]]
+deps = ["Printf"]
+git-tree-sha1 = "369e25546984dff5df351bc056fccc30de615080"
+uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
+version = "1.0.8"
+
 [[SHA]]
 uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 

IntervalsModel/Project.toml

@@ -10,6 +10,7 @@ CovidAlertVaccinationModel = "9260c4ec-b5cf-4bc2-ad29-e1d23bf2bd6f"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
 Intervals = "d8418881-c3e1-53bb-8760-2df7ec849ed5"
 JLSO = "9da8a3cd-07a3-59c0-a743-3fdc52c30d11"
 KissABC = "9c9dad79-530a-4643-a18b-2704674d4108"