From db98ecd1c21150a1a76d9ac555b70299de69d9bc Mon Sep 17 00:00:00 2001
From: pjentsch <pjentsch@uwaterloo.ca>
Date: Thu, 8 Apr 2021 01:22:34 -0400
Subject: [PATCH] finally contact network done
---
CovidAlertVaccinationModel/Manifest.toml | 183 +++++++++---------
CovidAlertVaccinationModel/Project.toml | 1 +
.../scratch_code/model_cuda.jl | 103 ++++++++++
.../other_model_implementation.jl | 83 ++++++++
.../src/CovidAlertVaccinationModel.jl | 26 +--
CovidAlertVaccinationModel/src/agents.jl | 63 +++---
.../src/contact_vectors.jl | 40 ++++
CovidAlertVaccinationModel/src/graphs.jl | 177 ++++++-----------
.../src/mixing_distributions.jl | 15 --
CovidAlertVaccinationModel/src/model.jl | 90 +++++----
CovidAlertVaccinationModel/test/runtests.jl | 25 ++-
IntervalsModel/src/IntervalsModel.jl | 3 -
.../src/ZeroWeightedDistributions.jl | 4 +
13 files changed, 486 insertions(+), 327 deletions(-)
create mode 100644 CovidAlertVaccinationModel/scratch_code/model_cuda.jl
create mode 100644 CovidAlertVaccinationModel/scratch_code/other_model_implementation.jl
create mode 100644 CovidAlertVaccinationModel/src/contact_vectors.jl
diff --git a/CovidAlertVaccinationModel/Manifest.toml b/CovidAlertVaccinationModel/Manifest.toml
index def003c..dd67016 100644
--- a/CovidAlertVaccinationModel/Manifest.toml
+++ b/CovidAlertVaccinationModel/Manifest.toml
@@ -21,10 +21,10 @@ uuid = "ec485272-7323-5ecc-a04f-4719b315124d"
version = "0.1.0"
[[ArrayInterface]]
-deps = ["IfElse", "LinearAlgebra", "Requires", "SparseArrays"]
-git-tree-sha1 = "ee07ae00e3cc277dcfa5507ce25be522313ecc3e"
+deps = ["IfElse", "LinearAlgebra", "Requires", "SparseArrays", "Static"]
+git-tree-sha1 = "2fbfa5f372352f92191b63976d070dc7195f47a4"
uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
-version = "3.1.1"
+version = "3.1.7"
[[Artifacts]]
uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
@@ -40,9 +40,9 @@ uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
[[BenchmarkTools]]
deps = ["JSON", "Logging", "Printf", "Statistics", "UUIDs"]
-git-tree-sha1 = "9e62e66db34540a0c919d72172cc2f642ac71260"
+git-tree-sha1 = "068fda9b756e41e6c75da7b771e6f89fa8a43d15"
uuid = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
-version = "0.5.0"
+version = "0.7.0"
[[Bzip2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -52,9 +52,9 @@ version = "1.0.6+5"
[[CSV]]
deps = ["Dates", "Mmap", "Parsers", "PooledArrays", "SentinelArrays", "Tables", "Unicode"]
-git-tree-sha1 = "1f79803452adf73e2d3fc84785adb7aaca14db36"
+git-tree-sha1 = "6d4242ef4cb1539e7ede8e01a47a32365e0a34cd"
uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
-version = "0.8.3"
+version = "0.8.4"
[[Cairo_jll]]
deps = ["Artifacts", "Bzip2_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"]
@@ -64,39 +64,39 @@ version = "1.16.0+6"
[[CategoricalArrays]]
deps = ["DataAPI", "Future", "JSON", "Missings", "Printf", "Statistics", "StructTypes", "Unicode"]
-git-tree-sha1 = "99809999c8ee01fa89498480b147f7394ea5450f"
+git-tree-sha1 = "f713d583d10fc036252fd826feebc6c173c522a8"
uuid = "324d7699-5711-5eae-9e2f-1d82baa6b597"
-version = "0.9.2"
+version = "0.9.5"
[[ChainRulesCore]]
deps = ["Compat", "LinearAlgebra", "SparseArrays"]
-git-tree-sha1 = "de4f08843c332d355852721adb1592bce7924da3"
+git-tree-sha1 = "44e9f638aa9ed1ad58885defc568c133010140aa"
uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-version = "0.9.29"
+version = "0.9.37"
[[ColorSchemes]]
deps = ["ColorTypes", "Colors", "FixedPointNumbers", "Random", "StaticArrays"]
-git-tree-sha1 = "3141757b5832ee7a0386db87997ee5a23ff20f4d"
+git-tree-sha1 = "d3cf83862f70d430d4b34e43ed65e74bd50ae0e0"
uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
-version = "3.10.2"
+version = "3.11.0"
[[ColorTypes]]
deps = ["FixedPointNumbers", "Random"]
-git-tree-sha1 = "4bffea7ed1a9f0f3d1a131bbcd4b925548d75288"
+git-tree-sha1 = "32a2b8af383f11cbb65803883837a149d10dfe8a"
uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
-version = "0.10.9"
+version = "0.10.12"
[[Colors]]
-deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Reexport"]
-git-tree-sha1 = "ac5f2213e56ed8a34a3dd2f681f4df1166b34929"
+deps = ["ColorTypes", "FixedPointNumbers", "Reexport"]
+git-tree-sha1 = "82f4e6ff9f847eca3e5ebc666ea2cd7b48e8b47e"
uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
-version = "0.12.6"
+version = "0.12.7"
[[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"
+git-tree-sha1 = "4fecfd5485d3c5de4003e19f00c6898cccd40667"
uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
-version = "3.25.0"
+version = "3.26.0"
[[CompilerSupportLibraries_jll]]
deps = ["Artifacts", "Libdl"]
@@ -131,9 +131,9 @@ version = "1.6.0"
[[DataFrames]]
deps = ["CategoricalArrays", "Compat", "DataAPI", "Future", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrettyTables", "Printf", "REPL", "Reexport", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"]
-git-tree-sha1 = "b0db5579803eabb33f1274ca7ca2f472fdfb7f2a"
+git-tree-sha1 = "d50972453ef464ddcebdf489d11885468b7b83a3"
uuid = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-version = "0.22.5"
+version = "0.22.7"
[[DataStructures]]
deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
@@ -165,9 +165,9 @@ uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
[[Distributions]]
deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SparseArrays", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "5a9a742ae30f13d6172c7ea245988d932134e25b"
+git-tree-sha1 = "e64debe8cd174cc52d7dd617ebc5492c6f8b698c"
uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.24.13"
+version = "0.24.15"
[[Downloads]]
deps = ["ArgTools", "LibCURL", "NetworkOptions"]
@@ -210,9 +210,9 @@ version = "4.3.1+4"
[[FillArrays]]
deps = ["LinearAlgebra", "Random", "SparseArrays"]
-git-tree-sha1 = "e384d3cff80ac79c7a541a817192841836e46331"
+git-tree-sha1 = "31939159aeb8ffad1d4d8ee44d07f8558273120a"
uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "0.11.2"
+version = "0.11.7"
[[FixedPointNumbers]]
deps = ["Statistics"]
@@ -250,27 +250,27 @@ uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
[[GLFW_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pkg", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"]
-git-tree-sha1 = "a1bbf700b5388bffc3d882f4f4d625cf1c714fd7"
+git-tree-sha1 = "bd1dbf065d7a4a0bdf7e74dd26cf932dda22b929"
uuid = "0656b61e-2033-5cc2-a64a-77c0f6c09b89"
-version = "3.3.2+1"
+version = "3.3.3+0"
[[GR]]
deps = ["Base64", "DelimitedFiles", "GR_jll", "HTTP", "JSON", "LinearAlgebra", "Pkg", "Printf", "Random", "Serialization", "Sockets", "Test", "UUIDs"]
-git-tree-sha1 = "aaebdf5588281c2902f499b49e67953f2b409c9c"
+git-tree-sha1 = "f74b42150042d11a1d94badf27c6125867c7e9bd"
uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
-version = "0.54.0"
+version = "0.57.1"
[[GR_jll]]
-deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Pkg", "Qt_jll", "Zlib_jll", "libpng_jll"]
-git-tree-sha1 = "8aee6fa096b0cbdb05e71750c978b96a08c78951"
+deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Pkg", "Qt5Base_jll", "Zlib_jll", "libpng_jll"]
+git-tree-sha1 = "578527027f2d6c29a8de3e2eb6887d8850ef755c"
uuid = "d2c73de3-f751-5644-a686-071e5b155ba9"
-version = "0.53.0+0"
+version = "0.57.1+0"
[[GeometryBasics]]
deps = ["EarCut_jll", "IterTools", "LinearAlgebra", "StaticArrays", "StructArrays", "Tables"]
-git-tree-sha1 = "4d4f72691933d5b6ee1ff20e27a102c3ae99d123"
+git-tree-sha1 = "4136b8a5668341e58398bb472754bff4ba0456ff"
uuid = "5c1252a2-5f33-56bf-86c9-59e7332b4326"
-version = "0.3.9"
+version = "0.3.12"
[[Gettext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "XML2_jll"]
@@ -290,10 +290,10 @@ uuid = "42e2da0e-8278-4e71-bc24-59509adca0fe"
version = "1.0.0"
[[HTTP]]
-deps = ["Base64", "Dates", "IniFile", "MbedTLS", "Sockets", "URIs"]
-git-tree-sha1 = "942c1a9c750bbe79912b7bd060a420932afd35b8"
+deps = ["Base64", "Dates", "IniFile", "MbedTLS", "NetworkOptions", "Sockets", "URIs"]
+git-tree-sha1 = "c9f380c76d8aaa1fa7ea9cf97bddbc0d5b15adc2"
uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
-version = "0.9.3"
+version = "0.9.5"
[[IfElse]]
git-tree-sha1 = "28e837ff3e7a6c3cdb252ce49fb412c8eb3caeef"
@@ -378,21 +378,21 @@ uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac"
version = "2.10.0+3"
[[LaTeXStrings]]
-git-tree-sha1 = "c7aebfecb1a60d59c0fe023a68ec947a208b1e6b"
+git-tree-sha1 = "c7f1c695e06c01b95a67f0cd1d34994f3e7db104"
uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
-version = "1.2.0"
+version = "1.2.1"
[[LabelledArrays]]
deps = ["ArrayInterface", "LinearAlgebra", "MacroTools", "StaticArrays"]
-git-tree-sha1 = "5e288800819c323de5897fa6d5a002bdad54baf7"
+git-tree-sha1 = "df09e970c816637191ef8794ef5c5c9f8950db88"
uuid = "2ee39098-c373-598a-b85f-a56591580800"
-version = "1.5.0"
+version = "1.6.0"
[[Latexify]]
deps = ["Formatting", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "Printf", "Requires"]
-git-tree-sha1 = "3a0084cec7bf157edcb45a67fac0647f88fe5eaf"
+git-tree-sha1 = "7c72983c6daf61393ee8a0b29a419c709a06cede"
uuid = "23fbe1c1-3f47-55db-b15f-69d7ec21a316"
-version = "0.14.7"
+version = "0.14.12"
[[LibCURL]]
deps = ["LibCURL_jll", "MozillaCACerts_jll"]
@@ -573,9 +573,9 @@ uuid = "91d4177d-7536-5919-b921-800302f37372"
version = "1.3.1+3"
[[OrderedCollections]]
-git-tree-sha1 = "d45739abcfc03b51f6a42712894a593f74c80a23"
+git-tree-sha1 = "4fa2ba51070ec13fcc7517db714445b4ab986bdf"
uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.3.3"
+version = "1.4.0"
[[PCRE_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -584,16 +584,16 @@ uuid = "2f80f16e-611a-54ab-bc61-aa92de5b98fc"
version = "8.42.0+4"
[[PDMats]]
-deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse", "Test"]
-git-tree-sha1 = "95a4038d1011dfdbde7cecd2ad0ac411e53ab1bc"
+deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"]
+git-tree-sha1 = "f82a0e71f222199de8e9eb9a09977bd0767d52a0"
uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
-version = "0.10.1"
+version = "0.11.0"
[[Parsers]]
deps = ["Dates"]
-git-tree-sha1 = "50c9a9ed8c714945e01cd53a21007ed3865ed714"
+git-tree-sha1 = "c8abc88faa3f7a3950832ac5d6e690881590d6dc"
uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "1.0.15"
+version = "1.1.0"
[[Pipe]]
git-tree-sha1 = "6842804e7867b115ca9de748a0cf6b364523c16d"
@@ -624,15 +624,15 @@ version = "1.0.10"
[[Plots]]
deps = ["Base64", "Contour", "Dates", "FFMPEG", "FixedPointNumbers", "GR", "GeometryBasics", "JSON", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "PlotThemes", "PlotUtils", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs"]
-git-tree-sha1 = "cab13323a50caf17432793269677b289234f02ca"
+git-tree-sha1 = "cc4eb1be2576984d7a0f7f51478827dee816138b"
uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
-version = "1.10.4"
+version = "1.11.2"
[[PooledArrays]]
-deps = ["DataAPI"]
-git-tree-sha1 = "0e8f5c428a41a81cd71f76d76f2fc3415fe5a676"
+deps = ["DataAPI", "Future"]
+git-tree-sha1 = "cde4ce9d6f33219465b55162811d8de8139c0414"
uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720"
-version = "1.1.0"
+version = "1.2.1"
[[PrettyTables]]
deps = ["Crayons", "Formatting", "Markdown", "Reexport", "Tables"]
@@ -644,11 +644,11 @@ version = "0.11.1"
deps = ["Unicode"]
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
-[[Qt_jll]]
+[[Qt5Base_jll]]
deps = ["Artifacts", "CompilerSupportLibraries_jll", "Fontconfig_jll", "Glib_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "OpenSSL_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libxcb_jll", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_keysyms_jll", "Xorg_xcb_util_renderutil_jll", "Xorg_xcb_util_wm_jll", "Zlib_jll", "xkbcommon_jll"]
-git-tree-sha1 = "588b799506f0b956a7e6e18f767dfa03cf333f26"
-uuid = "ede63266-ebff-546c-83e0-1c6fb6d0efc8"
-version = "5.15.2+2"
+git-tree-sha1 = "16626cfabbf7206d60d84f2bf4725af7b37d4a77"
+uuid = "ea2cea3b-5b76-57ae-a6ef-0a8af62496e1"
+version = "5.15.2+0"
[[QuadGK]]
deps = ["DataStructures", "LinearAlgebra"]
@@ -677,9 +677,9 @@ version = "1.1.1"
[[RecipesPipeline]]
deps = ["Dates", "NaNMath", "PlotUtils", "RecipesBase"]
-git-tree-sha1 = "c4d54a78e287de7ec73bbc928ce5eb3c60f80b24"
+git-tree-sha1 = "7a5026a6741c14147d1cb6daf2528a77ca28eb51"
uuid = "01d81517-befc-4cb6-b9ec-a95719d0359c"
-version = "0.3.1"
+version = "0.3.2"
[[Reexport]]
git-tree-sha1 = "57d8440b0c7d98fc4f889e478e80f268d534c9d5"
@@ -687,27 +687,28 @@ uuid = "189a3867-3050-52da-a836-e630ba90ab69"
version = "1.0.0"
[[Referenceables]]
-git-tree-sha1 = "4a32b1dd124a846580608eb347d4337f873c2499"
+deps = ["Adapt"]
+git-tree-sha1 = "e681d3bfa49cd46c3c161505caddf20f0e62aaa9"
uuid = "42d2dcc6-99eb-4e98-b66c-637b7d73030e"
-version = "0.1.0"
+version = "0.1.2"
[[Requires]]
deps = ["UUIDs"]
-git-tree-sha1 = "cfbac6c1ed70c002ec6361e7fd334f02820d6419"
+git-tree-sha1 = "4036a3bd08ac7e968e27c203d45f5fff15020621"
uuid = "ae029012-a4dd-5104-9daa-d747884805df"
-version = "1.1.2"
+version = "1.1.3"
[[Rmath]]
deps = ["Random", "Rmath_jll"]
-git-tree-sha1 = "86c5647b565873641538d8f812c04e4c9dbeb370"
+git-tree-sha1 = "bf3188feca147ce108c76ad82c2792c57abe7b1f"
uuid = "79098fc4-a85e-5d69-aa6a-4863f24498fa"
-version = "0.6.1"
+version = "0.7.0"
[[Rmath_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "1b7bf41258f6c5c9c31df8c1ba34c1fc88674957"
+git-tree-sha1 = "68db32dff12bb6127bac73c209881191bf0efbb7"
uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f"
-version = "0.2.2+2"
+version = "0.3.0+0"
[[SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
@@ -739,9 +740,9 @@ uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
[[Showoff]]
deps = ["Dates", "Grisu"]
-git-tree-sha1 = "ee010d8f103468309b8afac4abb9be2e18ff1182"
+git-tree-sha1 = "236dd0ddad6e3764cce8d8b09c0bbba6df2e194f"
uuid = "992d4aef-0814-514b-bc4d-f2e9a6c4116f"
-version = "0.3.2"
+version = "1.0.2"
[[SimpleTraits]]
deps = ["InteractiveUtils", "MacroTools"]
@@ -764,9 +765,9 @@ uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
[[SpecialFunctions]]
deps = ["ChainRulesCore", "OpenSpecFun_jll"]
-git-tree-sha1 = "75394dbe2bd346beeed750fb02baa6445487b862"
+git-tree-sha1 = "5919936c0e92cff40e57d0ddf0ceb667d42e5902"
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "1.2.1"
+version = "1.3.0"
[[SplittablesBase]]
deps = ["Setfield", "Test"]
@@ -774,11 +775,17 @@ git-tree-sha1 = "edef25a158db82f4940720ebada14a60ef6c4232"
uuid = "171d559e-b47b-412a-8079-5efa626c420e"
version = "0.1.13"
+[[Static]]
+deps = ["IfElse"]
+git-tree-sha1 = "ddec5466a1d2d7e58adf9a427ba69763661aacf6"
+uuid = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
+version = "0.2.4"
+
[[StaticArrays]]
deps = ["LinearAlgebra", "Random", "Statistics"]
-git-tree-sha1 = "9da72ed50e94dbff92036da395275ed114e04d49"
+git-tree-sha1 = "2f01a51c23eed210ff4a1be102c4cc8236b66e5b"
uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "1.0.1"
+version = "1.1.0"
[[Statistics]]
deps = ["LinearAlgebra", "SparseArrays"]
@@ -786,15 +793,15 @@ uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
[[StatsBase]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
-git-tree-sha1 = "7bab7d4eb46b225b35179632852b595a3162cb61"
+git-tree-sha1 = "a83fa3021ac4c5a918582ec4721bc0cf70b495a9"
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-version = "0.33.2"
+version = "0.33.4"
[[StatsFuns]]
deps = ["Rmath", "SpecialFunctions"]
-git-tree-sha1 = "3b9f665c70712af3264b61c27a7e1d62055dafd1"
+git-tree-sha1 = "ced55fd4bae008a8ea12508314e725df61f0ba45"
uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
-version = "0.9.6"
+version = "0.9.7"
[[StructArrays]]
deps = ["Adapt", "DataAPI", "Tables"]
@@ -804,9 +811,9 @@ version = "0.5.0"
[[StructTypes]]
deps = ["Dates", "UUIDs"]
-git-tree-sha1 = "65a43f5218197bc7091b76bc273a5e323a1d7b0d"
+git-tree-sha1 = "5eaf731e88587bb72a6c1262c0a014cd1859a08d"
uuid = "856f2bd8-1eba-4b0a-8007-ebc267875bd4"
-version = "1.2.3"
+version = "1.5.2"
[[SuiteSparse]]
deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
@@ -818,15 +825,15 @@ uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
[[TableTraits]]
deps = ["IteratorInterfaceExtensions"]
-git-tree-sha1 = "b1ad568ba658d8cbb3b892ed5380a6f3e781a81e"
+git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
-version = "1.0.0"
+version = "1.0.1"
[[Tables]]
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"]
-git-tree-sha1 = "a716dde43d57fa537a19058d044b495301ba6565"
+git-tree-sha1 = "a9ff3dfec713c6677af435d6a6d65f9744feef67"
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
-version = "1.3.2"
+version = "1.4.1"
[[Tar]]
deps = ["ArgTools", "SHA"]
@@ -850,9 +857,9 @@ version = "1.5.3"
[[Transducers]]
deps = ["ArgCheck", "BangBang", "CompositionsBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"]
-git-tree-sha1 = "9550eba57ebc2f7677c4c946aaca56e149ca73ff"
+git-tree-sha1 = "721dc0b917a43e6d191f07a6ea095ced164f08ce"
uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999"
-version = "0.4.59"
+version = "0.4.62"
[[URIs]]
git-tree-sha1 = "7855809b88d7b16e9b029afd17880930626f54a2"
diff --git a/CovidAlertVaccinationModel/Project.toml b/CovidAlertVaccinationModel/Project.toml
index cdbf10a..41bd14f 100644
--- a/CovidAlertVaccinationModel/Project.toml
+++ b/CovidAlertVaccinationModel/Project.toml
@@ -14,6 +14,7 @@ ImportAll = "c65182e5-40f4-518f-8165-175b85689199"
Intervals = "d8418881-c3e1-53bb-8760-2df7ec849ed5"
LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800"
LightGraphs = "093fc24a-ae57-5d10-9952-331d41423f4d"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
NamedTupleTools = "d9ec5142-1e00-5aa0-9d6a-321866360f50"
NetworkLayout = "46757867-2c16-5918-afeb-47bfcb05e46a"
Pipe = "b98c9c47-44ae-5843-9183-064241ee97a0"
diff --git a/CovidAlertVaccinationModel/scratch_code/model_cuda.jl b/CovidAlertVaccinationModel/scratch_code/model_cuda.jl
new file mode 100644
index 0000000..0d41be4
--- /dev/null
+++ b/CovidAlertVaccinationModel/scratch_code/model_cuda.jl
@@ -0,0 +1,103 @@
+#testing some GPU implementations of the ABM model
+
+
+
+function vaccinate_uniformly!(num_vaccines,u_next,u,graph,population_list,index_vectors) #vaccination_algorithm
+ vaccination_indices = rand(RNG,1:length(u),num_vaccines)
+ u_next[vaccination_indices] .= Immunized
+end
+function agents_step_cuda!(t,u_next,u,population_list,graph,params,index_vectors,vaccination_algorithm!)
+ @unpack p, recovery_rate, vaccines_per_day,vaccination_start_day = params
+ u_next .= u#keep state the same if nothing else happens
+
+ if t >= vaccination_start_day
+ vaccination_algorithm!(Int(floor(vaccines_per_day*length(u))),u_next,u,graph,population_list,index_vectors)
+ end
+ @inbounds for i in 1:length(u)
+ agent_status = u[i]
+ agent_demo = population_list[i]
+ if agent_status == Susceptible
+ @inbounds for j in 1:length(u)
+ if graph[i,j] != 0 && u[j] == Infected
+ if rand(RNG) < contact_weight(p,graph[i,j])
+ (u_next[i] = Infected)
+ end
+ end
+ end
+ elseif agent_status == Infected
+ if rand(RNG) < recovery_rate
+ u_next[i] = Recovered
+ end
+ end
+ end
+end
+
+function agents_step_gpu!(t,u_next,u,graph,params)
+ u_next .= u
+ function kernel(state, _, (t,u_next,u,g,params,randstate))
+ @unpack p, recovery_rate = params
+ i = linear_index(state)
+ if i <= length(u)
+ agent_status = u[i]
+ if agent_status == Susceptible
+ for j in 1:length(u)
+ if g[i,j] == 1 && u[j] == Infected && GPUArrays.gpu_rand(Float64, state, randstate) < p
+ u_next[i] = Infected
+ end
+ end
+ elseif agent_status == Infected
+ if GPUArrays.gpu_rand(Float64, state, randstate) < recovery_rate
+ u_next[i] = Recovered
+ end
+ end
+ end
+ return nothing
+ end
+ gpu_call(kernel, u, (t,u_next,u,graph,params, GPUArrays.default_rng(typeof(u)).state))
+end
+
+function solve_cuda!(u_0,params,steps,agent_model,vaccination_algorithm!; record = false)
+ solution = vcat([u_0], [fill(Susceptible,length(u_0)) for i in 1:steps])
+
+ population_list = cu(agent_model.demographics) #list of demographic status for each agent
+ index_vectors = cu.(agent_model.demographic_index_vectors) #lists of indices of each agent with a given demographic
+ base_network = cu(agent_model.base_network) #network with households and LTC homes
+ ws_contacts = cu(agent_model.ws_matrix_list)
+ rest_contacts = cu(agent_model.rest_matrix_list)
+
+ ws_daily_graph = MixingGraph(index_vectors,ws_contacts.daily,contact_time_distribution_matrix)
+ rest_daily_graph = MixingGraph(index_vectors,rest_contacts.daily,contact_time_distribution_matrix)
+
+ ws_weekly_graph = MixingGraph(index_vectors,ws_contacts.twice_a_week,contact_time_distribution_matrix)
+ rest_weekly_graph = MixingGraph(index_vectors,rest_contacts.twice_a_week,contact_time_distribution_matrix)
+
+
+ graph = similar(base_network)
+ graphlist = typeof(graph)[]
+ for t in 1:steps
+ day_of_week = mod(t,7)
+ graph .= 0
+ graph .= base_network
+ if !(day_of_week == 3 || day_of_week == 4)
+ apply_mixing_graph(graph, ws_daily_graph)
+ end
+ apply_mixing_graph(graph, rest_daily_graph)
+ if rand(RNG)<5/7
+ apply_mixing_graph(graph, ws_weekly_graph)
+ apply_mixing_graph(graph, rest_weekly_graph)
+ end
+
+ ws_once_graph = MixingGraph(index_vectors,ws_contacts.otherwise,contact_time_distribution_matrix)
+ rest_once_graph = MixingGraph(index_vectors,rest_contacts.otherwise,contact_time_distribution_matrix)
+
+ apply_mixing_graph(graph, ws_once_graph)
+ apply_mixing_graph(graph, rest_once_graph)
+
+ if record
+ push!(graphlist, deepcopy(graph))
+ end
+ agents_step!(t,solution[t+1],solution[t],population_list,graph,params,index_vectors,vaccination_algorithm!)
+ # advance agent states based on the new network, vaccination process given by vaccination_algorithm!, which is just a function defined as above
+ end
+ return solution, graphlist
+end
\ No newline at end of file
diff --git a/CovidAlertVaccinationModel/scratch_code/other_model_implementation.jl b/CovidAlertVaccinationModel/scratch_code/other_model_implementation.jl
new file mode 100644
index 0000000..f67028b
--- /dev/null
+++ b/CovidAlertVaccinationModel/scratch_code/other_model_implementation.jl
@@ -0,0 +1,83 @@
+
+function agents_step_2!(t,S_indicator,R_indicator, I_indicies,I_indicies_next,population_list,graph,params,index_vectors,vaccination_algorithm!)
+ @unpack p, recovery_rate, vaccines_per_day,vaccination_start_day = params
+ # if t >= vaccination_start_day
+ # vaccination_algorithm!(Int(floor(vaccines_per_day*length(u))),u_next,u,graph,population_list,index_vectors)
+ # end
+
+ while !isempty(I_indicies)
+ u = dequeue!(I_indicies)
+ for v in 1:length(population_list)
+ if graph[u,v] != 0 && S_indicator[v] == true
+ if rand(RNG) < contact_weight(p,graph[u,v])
+ # display(v)
+ enqueue!(I_indicies_next,v)
+ S_indicator[v] = false
+ end
+ end
+ end
+ if rand(RNG) < recovery_rate
+ R_indicator[u] = true
+ else
+ enqueue!(I_indicies_next,u)
+ end
+ end
+end
+using BenchmarkTools
+using DataStructures
+function solve_2!(u_0,params,steps,agent_model,vaccination_algorithm!; record = false)
+ S_indicator = u_0 .== Susceptible
+ R_indicator = u_0 .== Recovered
+ I_indicies_next = Queue{Int}()
+ I_indicies = Queue{Int}()
+
+ for ind in findall(==(Infected), u_0)
+ enqueue!(I_indicies, ind)
+ end
+
+
+ population_list = agent_model.demographics #list of demographic status for each agent
+ index_vectors = agent_model.demographic_index_vectors #lists of indices of each agent with a given demographic
+ base_network = agent_model.base_network #network with households and LTC homes
+ ws_contacts = agent_model.ws_matrix_list
+ rest_contacts = agent_model.rest_matrix_list
+
+ ws_daily_graph = MixingGraph(index_vectors,ws_contacts.daily,contact_time_distribution_matrix)
+ rest_daily_graph = MixingGraph(index_vectors,rest_contacts.daily,contact_time_distribution_matrix)
+
+ ws_weekly_graph = MixingGraph(index_vectors,ws_contacts.twice_a_week,contact_time_distribution_matrix)
+ rest_weekly_graph = MixingGraph(index_vectors,rest_contacts.twice_a_week,contact_time_distribution_matrix)
+
+
+ graph = similar(base_network)
+ graphlist = typeof(graph)[]
+ for t in 1:steps
+ day_of_week = mod(t,7)
+ graph .= 0
+ graph .= base_network
+ if !(day_of_week == 3 || day_of_week == 4)
+ apply_mixing_graph(graph, ws_daily_graph)
+ end
+ apply_mixing_graph(graph, rest_daily_graph)
+ if rand(RNG)<5/7
+ apply_mixing_graph(graph, ws_weekly_graph)
+ apply_mixing_graph(graph, rest_weekly_graph)
+ end
+
+ ws_once_graph = MixingGraph(index_vectors,ws_contacts.otherwise,contact_time_distribution_matrix)
+ rest_once_graph = MixingGraph(index_vectors,rest_contacts.otherwise,contact_time_distribution_matrix)
+
+ apply_mixing_graph(graph, ws_once_graph)
+ apply_mixing_graph(graph, rest_once_graph)
+
+ if record
+ push!(graphlist, deepcopy(graph))
+ end
+ agents_step_2!(t,S_indicator,R_indicator,I_indicies, I_indicies_next,population_list,graph,params,index_vectors,vaccination_algorithm!)
+ I_indicies, I_indicies_next = I_indicies_next, I_indicies
+ empty!(I_indicies_next)
+
+ # advance agent states based on the new network, vaccination process given by vaccination_algorithm!, which is just a function defined as above
+ end
+ return R_indicator, graphlist
+end
\ No newline at end of file
diff --git a/CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl b/CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl
index 8562317..76eb7c8 100644
--- a/CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl
+++ b/CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl
@@ -1,5 +1,4 @@
module CovidAlertVaccinationModel
-using ZeroWeightedDistributions
using LightGraphs
using RandomNumbers.Xorshifts
using Random
@@ -8,7 +7,6 @@ using Plots
using DataFrames
using Distributions
using StatsBase
-import StatsBase:mean
using Dates
using ThreadsX
using DelimitedFiles
@@ -17,7 +15,6 @@ using NamedTupleTools
using NetworkLayout:Stress
using NetworkLayout:SFDP
-import LightGraphs.add_edge!
const PACKAGE_FOLDER = dirname(dirname(pathof(CovidAlertVaccinationModel)))
@@ -25,6 +22,7 @@ const RNG = Xoroshiro128Star()
include("agents.jl")
include("data.jl")
+include("contact_vectors.jl")
include("mixing_distributions.jl")
include("model.jl")
include("plotting.jl")
@@ -33,31 +31,23 @@ include("graphs.jl")
const population = 14.57e6 #population of ontario
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 infection_data = parse_cases_data()
-const demographic_distribution = get_canada_demographic_distribution()
-
+const age_bins = [(0.0, 25.0),(25.0,65.0),(65.0,Inf)]
default(dpi = 300)
default(framestyle = :box)
using BenchmarkTools
function main()
- # display(size.(agent_model.demographic_index_vectors))
-
- # Random.seed!(RNG,1)
Random.seed!(RNG,1)
agent_model = AgentModel(5000)
- steps = 300
- # @btime solve!($u_0,$get_parameters(),$steps,$agent_model,$vaccinate_uniformly!);
- # Random.seed!(RNG,1)
- # agent_model = AgentModel(1_00)
- u_0 = get_u_0(length(agent_model.demographics))
- @show length(agent_model.demographics)
- @btime solve!($u_0,$get_parameters(),$steps,$agent_model,$vaccinate_uniformly!);
+ steps = 100
+ # return agent_model
+ sol,graphs = solve!(get_parameters(),steps,agent_model,vaccinate_uniformly!);
+ b1 = @benchmark solve!($get_parameters(),$steps,$agent_model,$vaccinate_uniformly!)
+ # display(b1)
# println("done")
# sol,graphs = solve_record!(u_0,get_parameters(),steps,agent_model,vaccinate_uniformly!);
# return aggregate_timeseries(sol)
- # plot_model_spatial_gif(agent_model.base_network,graphs,sol)
+ # plot_model_spatial_gif(agent_model.base_network,gddddddddraphs,sol)
end
end
diff --git a/CovidAlertVaccinationModel/src/agents.jl b/CovidAlertVaccinationModel/src/agents.jl
index 7e0a1df..e77e0ae 100644
--- a/CovidAlertVaccinationModel/src/agents.jl
+++ b/CovidAlertVaccinationModel/src/agents.jl
@@ -11,52 +11,57 @@ end
Immunized
end
-struct AgentModel{GraphType,DistMatrixList1,DistMatrixList2,DistMatrixList3}
+struct AgentModel{GraphType,L1,L2}
demographics::Vector{AgentDemographic}
- # is_app_user::Vector{Boolean}
- # is_vaccinator::Vector{Boolean}
- # app_user_indices::Vector{Int}
- # vaccinator_indices::Vector{Int}
demographic_index_vectors::Vector{Vector{Int}}
base_network::GraphType
- workschool_contacts_mean_adjusted_list::DistMatrixList1
- rest_contacts_mean_adjusted_list::DistMatrixList2
- home_contacts_duration_list::DistMatrixList3
+ ws_matrix_list::L1
+ rest_matrix_list::L2
function AgentModel(num_households)
pop_list,base_network,index_vectors = generate_population(num_households)
pop_sizes = length.(index_vectors)
+ map_symmetrize(m_tuple) = map(md -> symmetrize_means(pop_sizes,md), m_tuple)
- workschool_contacts_mean_adjusted_list = [symmetrize_means(pop_sizes,initial_workschool_mixing_matrix)]
- rest_contacts_mean_adjusted_list = [symmetrize_means(pop_sizes,initial_rest_mixing_matrix)]
- home_contacts_duration_list = [symmetrize_means(pop_sizes,contact_time_distribution_matrix)]
-
-
- workplace_data, distancing_data,residential_data = get_distancing_data_sets()
- @assert length(workplace_data) == length(distancing_data) == length(residential_data) "uh oh your data is different lengths!"
-
- for t in 1:length(workplace_data)
- push!(workschool_contacts_mean_adjusted_list, adjust_distributions_mean(workschool_contacts_mean_adjusted_list[1], workplace_data[t]))
- push!(rest_contacts_mean_adjusted_list, adjust_distributions_mean(rest_contacts_mean_adjusted_list[1], distancing_data[t]))
- push!(home_contacts_duration_list, adjust_distributions_mean(home_contacts_duration_list[1], residential_data[t]))
- end
-
+ workschool_contacts_mean_adjusted = map_symmetrize(workschool_mixing)
+ rest_contacts_mean_adjusted = map_symmetrize(rest_mixing)
+ # vaccinator_indices = sample(RNG,1:length(pop_list),length(pop_list) // 2)
return new{
typeof(base_network),
- typeof(workschool_contacts_mean_adjusted_list),
- typeof(rest_contacts_mean_adjusted_list),
- typeof(home_contacts_duration_list)
+ typeof(workschool_contacts_mean_adjusted),
+ typeof(rest_contacts_mean_adjusted)
}(
pop_list,
index_vectors,
base_network,
- workschool_contacts_mean_adjusted_list,
- rest_contacts_mean_adjusted_list,
- home_contacts_duration_list
- )
+ workschool_contacts_mean_adjusted,
+ rest_contacts_mean_adjusted
+ )
end
end
+#generate population with households distributed according to household_size_distribution
+function generate_population(num_households)
+ households_composition = sample_household_data(num_households)
+ households = map( l -> [fill(AgentDemographic(i),n) for (i,n) in enumerate(l)],households_composition) |>
+ l -> reduce(vcat,l) |>
+ l -> reduce(vcat,l)
+
+ total_household_pop = sum(sum.(households_composition))
+ household_networks = map(LightGraphs.complete_graph, sum.(households_composition))
+
+ population_list = reduce(vcat,households)
+ network = reduce(blockdiag,household_networks; init = SimpleGraph())
+ index_vectors = [findall(x -> x == AgentDemographic(i), population_list) for i in 1:(AgentDemographic.size-1)]
+ return (;
+ population_list,
+ network,
+ index_vectors
+ )
+end
+
+
+
# func(a) = println("agent")
function Base.show(io::IO, status::AgentStatus)
if status == Susceptible
diff --git a/CovidAlertVaccinationModel/src/contact_vectors.jl b/CovidAlertVaccinationModel/src/contact_vectors.jl
new file mode 100644
index 0000000..64350cb
--- /dev/null
+++ b/CovidAlertVaccinationModel/src/contact_vectors.jl
@@ -0,0 +1,40 @@
+function reindex!(k,csum,index_list_i,index_list_j,j_to_i_contacts,i_to_j_contacts,sample_list_i,sample_list_j)
+ i_index = index_list_i[k]
+ j_index = index_list_j[k]
+ csum += j_to_i_contacts[j_index] - i_to_j_contacts[i_index]
+
+ i_to_j_contacts[i_index] = sample_list_i[k]
+ j_to_i_contacts[j_index] = sample_list_j[k]
+ csum += i_to_j_contacts[i_index] - j_to_i_contacts[j_index]
+
+ return csum
+end
+
+function generate_contact_vectors!(ij_dist,ji_dist,i_to_j_contacts, j_to_i_contacts)
+ rand!(RNG,ij_dist,i_to_j_contacts)
+ rand!(RNG,ji_dist,j_to_i_contacts)
+ l_i = length(i_to_j_contacts)
+ l_j = length(j_to_i_contacts)
+
+ csum = sum(i_to_j_contacts) - sum(j_to_i_contacts)
+
+ inner_iter = 10
+ index_list_i = zeros(Int,inner_iter)
+ index_list_j = similar(index_list_i)
+ sample_list_i = zeros(eltype(i_to_j_contacts),inner_iter)
+ sample_list_j = similar(sample_list_i)
+
+ while csum != 0
+ index_list_i = sample!(RNG,1:l_i,index_list_i)
+ index_list_j = sample!(RNG,1:l_j,index_list_j)
+ sample_list_i = rand!(RNG,ij_dist,sample_list_i)
+ sample_list_j = rand!(RNG,ji_dist,sample_list_j)
+ for i = 1:inner_iter
+ if csum != 0
+ csum = reindex!(i,csum,index_list_i,index_list_j,j_to_i_contacts,i_to_j_contacts,sample_list_i,sample_list_j)
+ end
+ end
+ end
+
+ return nothing
+end
\ No newline at end of file
diff --git a/CovidAlertVaccinationModel/src/graphs.jl b/CovidAlertVaccinationModel/src/graphs.jl
index 339c446..56f490f 100644
--- a/CovidAlertVaccinationModel/src/graphs.jl
+++ b/CovidAlertVaccinationModel/src/graphs.jl
@@ -1,104 +1,26 @@
-#This defines a type for the time-dependent-graph
-#The tradeoff is (partially) memory-usage vs speed, so it tries to preallocate as much as possible
-#As result, it stores a lot of redundant information
-#Julia's LightGraphs.jl package uses edgelists to store their graphs, but here I am experimenting with bitarrays, which seem to be 4-5x faster, even for huge problems.
-#Also completely decouple the generation of the contact vectors (the number of contacts each node has in each WorkSchool/Rest layer) from the
-#generation of the corresponding graphs
-#bitmatrices are much faster at the graph generation but we lose access to neighbors in O(1)
-struct MixingGraphs{GraphVector,A}
- graph::GraphVector
- base_graph::GraphVector
- contact_vector_ws::A
- contact_vector_rest::A
- function MixingGraphs(static_contacts, ws_mixing_matrices, rest_mixing_matrices, index_vectors)
- (length(ws_mixing_matrices) == length(rest_mixing_matrices)) || throw(ArgumentError("mixing matrix lists must be of equal length"))
- ts_length = length(ws_mixing_matrices)
- contact_vector_ws = map(mm -> MixingContacts(index_vectors,mm), ws_mixing_matrices)
- contact_vector_rest = map(mm -> MixingContacts(index_vectors,mm), rest_mixing_matrices)
- base_graph = convert(BitArray,adjacency_matrix(static_contacts))
- return new{typeof(base_graph),typeof(contact_vector_ws)}(deepcopy(base_graph),base_graph,contact_vector_ws,contact_vector_rest)
- end
-end
-
-
+using LinearAlgebra
+using LightGraphs
#A type that defines a matrix of vectors, such that the sum of the ijth vector is equal to the sum of the jith vector
struct MixingContacts{V}
+ total_edges::Int
contact_array::V
function MixingContacts(index_vectors,mixing_matrix)
contacts = map(CartesianIndices(mixing_matrix)) do ind
zeros(length(index_vectors[ind[1]]))
end
+ tot = 0
for i in 1:length(mixing_matrix[:,1]), j in 1:i #diagonal
generate_contact_vectors!(mixing_matrix[i,j],mixing_matrix[j,i],contacts[i,j],contacts[j,i] )
+ tot += sum(contacts[i,j])
end
- new{typeof(contacts)}(contacts)
- end
-end
-
-#generate a new mixing graph for the current timestep t
-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],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
-
-
-function update_contacts!(mixing_contacts,mixing_matrix)
- for i in 1:length(mixing_matrix[:,1]), j in 1:i #diagonal
- generate_contact_vectors!(mixing_matrix[i,j],mixing_matrix[j,i],mixing_contacts.contact_array[i,j],mixing_contacts.contact_array[j,i] )
- end
-end
-function generate_contact_vectors!(ij_dist,ji_dist,i_to_j_contacts, j_to_i_contacts)
- rand!(RNG,ij_dist,i_to_j_contacts)
- rand!(RNG,ji_dist,j_to_i_contacts)
- l_i = length(i_to_j_contacts)
- l_j = length(j_to_i_contacts)
- contacts_sums = sum(i_to_j_contacts) - sum(j_to_i_contacts)
- sample_list_length = max(l_i,l_j) #better heuristic for this based on stddev of dist?
- index_list_i = sample(RNG,1:l_i,sample_list_length)
- index_list_j = sample(RNG,1:l_j,sample_list_length)
- sample_list_i = rand(RNG,ij_dist,sample_list_length)
- sample_list_j = rand(RNG,ji_dist,sample_list_length)
- for k = 1:sample_list_length
- if (contacts_sums != 0)
- i_index = index_list_i[k]
- j_index = index_list_j[k]
- contacts_sums += j_to_i_contacts[j_index] - i_to_j_contacts[i_index]
-
- i_to_j_contacts[i_index] = sample_list_i[k]
- j_to_i_contacts[j_index] = sample_list_j[k]
- contacts_sums += i_to_j_contacts[i_index] - j_to_i_contacts[j_index]
- else
- break
- end
+ new{typeof(contacts)}(tot,contacts)
end
- while contacts_sums != 0
- i_index = sample(RNG,1:l_i)
- j_index = sample(RNG,1:l_j)
- contacts_sums += j_to_i_contacts[j_index] - i_to_j_contacts[i_index]
- i_to_j_contacts[i_index] = rand(RNG,ij_dist)
- j_to_i_contacts[j_index] = rand(RNG,ji_dist)
- contacts_sums += i_to_j_contacts[i_index] - j_to_i_contacts[j_index]
- end
- return nothing
end
-#add a bipartite graph derived from mixing matrices onto g
-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
- return g
-end
-
-using StatsBase
#modify g so that nodes specified in anodes and bnodes are connected by a bipartite graph with expected degrees given by aseq and bseq
#implemented from Aksoy, S. G., Kolda, T. G., & Pinar, A. (2017). Measuring and modeling bipartite graphs with community structure
-#simple algorithm, might produce parallel edges for small graphs
-function random_bipartite_graph_fast_CL!(g::SimpleGraph,anodes,bnodes,aseq,bseq)
+#simple algorithm, lightgraphs does not allow parallel edges
+function random_bipartite_graph_fast_CL!(g::SimpleGraph,anodes,bnodes,aseq,bseq, weights_dict)
lena = length(aseq)
lenb = length(bseq)
m = Int(sum(aseq))
@@ -108,40 +30,65 @@ function random_bipartite_graph_fast_CL!(g::SimpleGraph,anodes,bnodes,aseq,bseq)
for k in 1:m
add_edge!(g,astubs[k],bstubs[k])
end
- return g
+ return g
end
-#same algorithm but with a bitarray, this cannot produce parallel edges
-function random_bipartite_graph_fast_CL!(g::T,anodes,bnodes,aseq,bseq) where T<:AbstractArray
- lena = length(aseq)
- lenb = length(bseq)
- m = Int(sum(aseq))
- @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 i in 1:m
- g[astubs[i],bstubs[i]] = 1
- g[bstubs[i],astubs[i]] = 1
+
+struct MixingGraph{G, V, M}
+ g::G
+ weights::V
+ covid_alert_time::M
+ function MixingGraph(population_list,index_vectors,mixing_matrix)
+
+ contacts = MixingContacts(index_vectors,mixing_matrix)
+
+ g = Graph(length(population_list))
+
+ weights = Dict{Tuple{Int,Int},UInt8}()
+ for i in 1:length(index_vectors), j in 1:i #diagonal
+ random_bipartite_graph_fast_CL!(g,index_vectors[i],index_vectors[j],contacts.contact_array[i,j],contacts.contact_array[j,i],weights)
+ end
+
+ covid_alert_time = zeros(nv(g))
+ return new{typeof(g),typeof(weights),typeof(covid_alert_time)}(
+ g,
+ weights,
+ covid_alert_time
+ )
+ end
+ function MixingGraph(g::SimpleGraph)
+ weights = Dict{Tuple{Int,Int},UInt8}()
+ covid_alert_time = zeros(nv(g))
+ return new{typeof(g),typeof(weights),typeof(covid_alert_time)}(
+ g,
+ weights,
+ covid_alert_time
+ )
end
- return g
end
-#generate population with households distributed according to household_size_distribution
-function generate_population(num_households)
- households_composition = sample_household_data(num_households)
- households = map( l -> [fill(AgentDemographic(i),n) for (i,n) in enumerate(l)],households_composition) |>
- l -> reduce(vcat,l) |>
- l -> reduce(vcat,l)
+function MixingGraph(g::SimpleGraph,population_list,contact_time_distribution_matrix)
+ mg = MixingGraph(g)
+ sample_mixing_graph!(mg, population_list, contact_time_distribution_matrix)
+ return mg
+end
+function MixingGraph(population_list,index_vectors,mixing_matrix,contact_time_distribution_matrix)
+ mg = MixingGraph(population_list,index_vectors,mixing_matrix)
+ sample_mixing_graph!(mg, population_list, contact_time_distribution_matrix)
+ return mg
+end
- total_household_pop = sum(sum.(households_composition))
- household_networks = map(LightGraphs.complete_graph, sum.(households_composition))
+function sample_mixing_graph!(mixing_graph,population_demographics, contact_time_distributions)
+ for (k,e) in enumerate(edges(mixing_graph.g))
+ i = src(e)
+ j = dst(e)
+ demo_i = Int(population_demographics[i])
+ demo_j = Int(population_demographics[j])
- population_list = reduce(vcat,households)
- network = reduce(blockdiag,household_networks; init = SimpleGraph())
- index_vectors = [findall(x -> x == AgentDemographic(i), population_list) for i in 1:(AgentDemographic.size-1)]
- return (;
- population_list,
- network,
- index_vectors
- )
+ contact_time = rand(RNG, contact_time_distributions[demo_i,demo_j])
+ mixing_graph.covid_alert_time[i] += contact_time
+ mixing_graph.covid_alert_time[j] += contact_time
+ mixing_graph.weights[(i,j)] = contact_time
+ mixing_graph.weights[(j,i)] = contact_time
+ end
end
\ No newline at end of file
diff --git a/CovidAlertVaccinationModel/src/mixing_distributions.jl b/CovidAlertVaccinationModel/src/mixing_distributions.jl
index 60ad4a0..9002c01 100644
--- a/CovidAlertVaccinationModel/src/mixing_distributions.jl
+++ b/CovidAlertVaccinationModel/src/mixing_distributions.jl
@@ -1,18 +1,9 @@
-
-function adjust_distributions_mean(distribution_matrix::AbstractArray{T},mean_shift_percentage) where T<:ZWDist
- return map(distribution_matrix) do dist
- new_mean = mean(dist)*(1 + mean_shift_percentage)
- return from_mean(typeof(dist),dist.α, new_mean)
- end
-end
-
function adjust_distributions_mean(distribution_matrix::AbstractArray{T},mean_shift_percentage) where T<:Distribution
return map(distribution_matrix) do dist
new_mean = mean(dist)*(1 + mean_shift_percentage)
return from_mean(typeof(dist), new_mean)
end
end
-
function symmetrize_means(population_sizes,mixing_matrix::Matrix{<:Distribution})
mixing_means = mean.(mixing_matrix)
symmetrized_means = zeros((length(population_sizes),length(population_sizes)))
@@ -30,10 +21,6 @@ end
function from_mean(::Type{Poisson{T}},μ) where T
return Poisson(μ)
end
-function from_mean(::Type{ZWDist{DistType,T}},α,μ) where {DistType <: Distribution{Univariate,W} where W, T}
- return ZWDist(α,from_mean(DistType,μ/(1-α)))
-end
-StatsBase.mean(d::ZWDist{Dist,T}) where {Dist,T} = (1 - d.α)*StatsBase.mean(d.base_dist)
function from_mean_workschool(_,μ)
return Poisson(μ)
end
@@ -59,5 +46,3 @@ const workschool_mixing, rest_mixing = load_mixing_matrices()
const contact_time_distribution_matrix = [Geometric() for i in 1:(AgentDemographic.size-1), j in 1:(AgentDemographic.size-1)]
-
-
diff --git a/CovidAlertVaccinationModel/src/model.jl b/CovidAlertVaccinationModel/src/model.jl
index d2ef5a9..3ff6785 100644
--- a/CovidAlertVaccinationModel/src/model.jl
+++ b/CovidAlertVaccinationModel/src/model.jl
@@ -5,8 +5,7 @@ function get_u_0(nodes)
return u_0
end
-function contact_weight(p, agent_v,agent_w)
- contact_time = rand(RNG,contact_time_distribution_matrix[Int(agent_v),Int(agent_w)])
+function contact_weight(p, contact_time)
return 1 - (1-p)^contact_time
end
@@ -18,20 +17,18 @@ function hotspotting!(num_vaccines,u_next,u,graph,population_list,index_vectors)
# vaccination_indices = rand(RNG,1:length(u),num_vaccines)
# u_next[vaccination_indices] .= Immunized
end
-function agents_step!(t,u_next,u,population_list,graph,params,index_vectors,vaccination_algorithm!)
+function agents_step!(t,u_next,u,population_list,network_list,params,index_vectors,vaccination_algorithm!)
@unpack p, recovery_rate, vaccines_per_day,vaccination_start_day = params
- u_next .= u#keep state the same if nothing else happens
-
- if t >= vaccination_start_day
- vaccination_algorithm!(Int(floor(vaccines_per_day*length(u))),u_next,u,graph,population_list,index_vectors)
- end
+ u_next .= u
@inbounds for i in 1:length(u)
agent_status = u[i]
agent_demo = population_list[i]
if agent_status == Susceptible
- @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)
+ for mixing_graph in network_list
+ for j in neighbors(mixing_graph.g,i)
+ if u[j] == Infected && rand(RNG) < contact_weight(p,mixing_graph.weights[(i,j)])
+ u_next[i] = Infected
+ end
end
end
elseif agent_status == Infected
@@ -43,60 +40,61 @@ function agents_step!(t,u_next,u,population_list,graph,params,index_vectors,vacc
end
-function solve!(u_0,params,steps,agent_model,vaccination_algorithm!)
- solution = vcat([u_0], [fill(Susceptible,length(u_0)) for i in 1:steps])
+function solve!(params,steps,agent_model,vaccination_algorithm!; record = false)
population_list = agent_model.demographics #list of demographic status for each agent
+ popsize = length(population_list)
index_vectors = agent_model.demographic_index_vectors #lists of indices of each agent with a given demographic
- base_network = agent_model.base_network #static network, households and LTC homes
- ws_contacts = agent_model.workschool_contacts_mean_adjusted_list
- rest_contacts = agent_model.rest_contacts_mean_adjusted_list
+ ws_contacts = agent_model.ws_matrix_list
+ rest_contacts = agent_model.rest_matrix_list
+ p = params.p
+ u_0 = get_u_0(length(agent_model.demographics))
- @show length(ws_contacts)
- mixing_graphs = MixingGraphs(base_network, ws_contacts, rest_contacts, index_vectors)
- for t in 1:steps
- advance_mixing_graph!(t,mixing_graphs,index_vectors)
- agents_step!(t,solution[t+1],solution[t],population_list,mixing_graphs.graph,params,index_vectors,vaccination_algorithm!)
- #advance agent states based on the new network, vaccination process given by vaccination_algorithm!, which is just a function defined as above
- end
-
- return solution #mixing_graphs.graph_list
-end
+ home_static_edges = MixingGraph(agent_model.base_network,population_list,contact_time_distribution_matrix) #network with households and LTC homes
+ ws_static_edges = MixingGraph(population_list,index_vectors,ws_contacts.daily,contact_time_distribution_matrix)
+ ws_weekly_edges = MixingGraph(population_list,index_vectors,ws_contacts.twice_a_week,contact_time_distribution_matrix)
+ ws_daily_edges_vector = [MixingGraph(population_list,index_vectors,ws_contacts.otherwise,contact_time_distribution_matrix) for i in 1:steps]
+
+ rest_static_edges = MixingGraph(population_list,index_vectors,rest_contacts.daily,contact_time_distribution_matrix)
+ rest_weekly_edges = MixingGraph(population_list,index_vectors,rest_contacts.twice_a_week,contact_time_distribution_matrix)
+ rest_daily_edges_vector = [MixingGraph(population_list,index_vectors,rest_contacts.otherwise,contact_time_distribution_matrix) for i in 1:steps]
+
-function solve_record!(u_0,params,steps,agent_model,vaccination_algorithm!)
solution = vcat([u_0], [fill(Susceptible,length(u_0)) for i in 1:steps])
- population_list = agent_model.demographics #list of demographic status for each agent
- index_vectors = agent_model.demographic_index_vectors #lists of indices of each agent with a given demographic
- base_network = agent_model.base_network #static network, households and LTC homes
- ws_contacts = agent_model.workschool_contacts_mean_adjusted_list
- rest_contacts = agent_model.rest_contacts_mean_adjusted_list
- mixing_graphs = MixingGraphs(base_network, ws_contacts, rest_contacts, index_vectors)
- graphs = SimpleGraph[]
+ network_lists = [
+ [home_static_edges,rest_static_edges] for i in 1:steps
+ ]
+
+ for (t,l) in enumerate(network_lists)
+ day_of_week = mod(t,7)
+ if !(day_of_week == 3 || day_of_week == 4) #simulation begins on thursday I guess
+ push!(l, ws_static_edges)
+ end
+ if rand(RNG)<5/7
+ push!(l, ws_weekly_edges)
+ push!(l, rest_weekly_edges)
+ end
+ push!(l,ws_daily_edges_vector[t])
+ push!(l,rest_daily_edges_vector[t])
+ end
+
for t in 1:steps
- advance_mixing_graph!(t,mixing_graphs,index_vectors)
- g_matrix_graph = SimpleGraph(deepcopy(mixing_graphs.graph))
- push!(graphs,g_matrix_graph)
- agents_step!(t,solution[t+1],solution[t],population_list,mixing_graphs.graph,params,index_vectors,vaccination_algorithm!)
+ network_list = network_lists[t]
#advance agent states based on the new network, vaccination process given by vaccination_algorithm!, which is just a function defined as above
+ agents_step!(t,solution[t+1],solution[t],population_list,network_list,params,index_vectors,vaccination_algorithm!)
end
-
- return solution,graphs
+ return solution, network_lists
end
function get_parameters()
params = (
p = 0.05,
- recovery_rate = 0.05,
+ recovery_rate = 0.1,
vaccines_per_day = 0.0, #percentage of population vaccinated per day
vaccination_start_day = 60
)
return params
end
-function fit_parameters()
-
-
-end
-
diff --git a/CovidAlertVaccinationModel/test/runtests.jl b/CovidAlertVaccinationModel/test/runtests.jl
index ae7b6c5..b8ec529 100644
--- a/CovidAlertVaccinationModel/test/runtests.jl
+++ b/CovidAlertVaccinationModel/test/runtests.jl
@@ -15,8 +15,8 @@ dem_cat = AgentDemographic.size -1
#covidalert
@testset "mixing matrices, size: $sz" for (m,sz) in zip(agent_models,model_sizes)
- ws_dist = m.workschool_contacts_mean_adjusted_list
- r_dist = m.rest_contacts_mean_adjusted_list
+ ws_dist = m.ws_matrix_list
+ r_dist = m.rest_matrix_list
index_vec =m.demographic_index_vectors
@testset "workschool" for i in dem_cat, j in dem_cat
for t in 1:length(ws_dist)
@@ -35,7 +35,7 @@ function vac_rate_test(model,vac_strategy, vac_rate; rng = Xoroshiro128Plus())
u_0 = get_u_0(length(model.demographics))
params = merge(get_parameters(),(vaccines_per_day = vac_rate,))
steps = 300
- sol1 = solve!(u_0,params,steps,model,vac_strategy);
+ sol1,_ = solve!(u_0,params,steps,model,vac_strategy);
total_infections = count(x->x == AgentStatus(3),sol1[end])
display(total_infections)
return total_infections
@@ -43,14 +43,13 @@ end
function infection_rate_test(model, inf_parameter; rng = Xoroshiro128Plus())
- u_0 = get_u_0(length(model.demographics))
params = merge(get_parameters(),(p = inf_parameter,))
steps = 300
- display(params)
- sol1 = solve!(u_0,params,steps,model,vaccinate_uniformly!);
+ # display(params)
+ sol1,_ = solve!(params,steps,model,vaccinate_uniformly!);
total_infections = count(x->x == AgentStatus(3),sol1[end])
- display(total_infections)
+ # display(total_infections)
return total_infections
end
@@ -60,12 +59,12 @@ function test_comparison(f,xpts,comparison)
return all(comparison(ypts[i],ypts[i+1]) for i in 1:length(ypts)-1)
end
-@testset "vaccination efficacy $sz" for (m,sz) in zip(deepcopy(agent_models),model_sizes)
- @show vac_rate_test(m,vaccination_strategies[1],vaccination_rates[1])
- @testset "strategy" for strat in vaccination_strategies
- @test test_comparison(x->vac_rate_test(m,strat,x),vaccination_rates,>)
- end
-end
+# @testset "vaccination efficacy $sz" for (m,sz) in zip(deepcopy(agent_models),model_sizes)
+# @show vac_rate_test(m,vaccination_strategies[1],vaccination_rates[1])
+# @testset "strategy" for strat in vaccination_strategies
+# @test test_comparison(x->vac_rate_test(m,strat,x),vaccination_rates,>)
+# end
+# end
@testset "infection efficacy $sz" for (m,sz) in zip(deepcopy(agent_models),model_sizes)
@test test_comparison(x->infection_rate_test(m,x),infection_rates,<)
diff --git a/IntervalsModel/src/IntervalsModel.jl b/IntervalsModel/src/IntervalsModel.jl
index 936a9d8..1edbc94 100644
--- a/IntervalsModel/src/IntervalsModel.jl
+++ b/IntervalsModel/src/IntervalsModel.jl
@@ -1,7 +1,5 @@
module IntervalsModel
export main, do_hh, do_ws, do_rest
-
-
using Intervals
using CSV
using DataFrames
@@ -10,7 +8,6 @@ using Random
using KernelDensity
using StatsBase
using Distributions
-using ZeroWeightedDistributions
const PACKAGE_FOLDER = dirname(dirname(pathof(IntervalsModel)))
using DataStructures:OrderedDict
using Serialization
diff --git a/ZeroWeightedDistributions/src/ZeroWeightedDistributions.jl b/ZeroWeightedDistributions/src/ZeroWeightedDistributions.jl
index 4d2c1ab..3eda876 100644
--- a/ZeroWeightedDistributions/src/ZeroWeightedDistributions.jl
+++ b/ZeroWeightedDistributions/src/ZeroWeightedDistributions.jl
@@ -22,6 +22,10 @@ struct ZWDist{BaseDistType <: Sampleable,T} <: Sampleable{Univariate,T}
end
end
+# function from_mean(::Type{ZWDist{DistType,T}},α,μ) where {DistType <: Distribution{Univariate,W} where W, T}
+# return ZWDist(α,from_mean(DistType,μ/(1-α)))
+# end
+StatsBase.mean(d::ZWDist{Dist,T}) where {Dist,T} = (1 - d.α)*StatsBase.mean(d.base_dist)
function Distributions.pdf(d::ZWDist, x)
--
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