diff --git a/CovidAlertVaccinationModel/src/ABM/abm.jl b/CovidAlertVaccinationModel/src/ABM/abm.jl
index 084fd4d9b50bec979248cd63be3c53e967553a1a..563b084c61ea848ca058a99101025307595f01e2 100644
--- a/CovidAlertVaccinationModel/src/ABM/abm.jl
+++ b/CovidAlertVaccinationModel/src/ABM/abm.jl
@@ -10,8 +10,8 @@ default(framestyle = :box)
function bench()
steps = 100
model_sol = ModelSolution(steps,get_parameters(),5000);
- recording = recorder(steps)
- output = solve!(model_sol,DebugRecorder)
+ recording = DebugRecorder(steps)
+ output = solve!(model_sol,recording )
end
function abm()
diff --git a/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl b/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl
index c038e83329d3754a8e7f239ef4ecd647637ae913..30e0fea605dc521f80b4cd1218efe0882aa9dd67 100644
--- a/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl
+++ b/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl
@@ -31,11 +31,71 @@ function random_bipartite_graph_fast_CL!(g::SimpleGraph,anodes,bnodes,aseq,bseq,
return g
end
+struct TimeDepMixingGraph{N,G}
+ resampled_graphs::NTuple{N,G}
+ graph_list::Vector{Vector{G}}
+ function TimeDepMixingGraph(len,resampled_graphs::NTuple{N,G},base_graph_list::Vector{G}) where {G,N}
+ return new{N,G}(
+ resampled_graphs,
+ [copy(base_graph_list) for i in 1:len]
+ )
+ end
+end
+function time_dep_mixing_graphs(len,base_network,demographics,index_vectors,ws_matrix_tuple,rest_matrix_tuple)
+ home_static_edges = WeightedGraph(base_network,contact_time_distributions.hh) #network with households and LTC homes
+ ws_static_edges = WeightedGraph(demographics,index_vectors,ws_matrix_tuple.daily,contact_time_distributions.ws)
+ ws_weekly_edges = WeightedGraph(demographics,index_vectors,ws_matrix_tuple.twice_a_week,contact_time_distributions.ws)
+ ws_daily_edges = WeightedGraph(demographics,index_vectors,ws_matrix_tuple.otherwise,contact_time_distributions.ws)
+ rest_static_edges = WeightedGraph(demographics,index_vectors,rest_matrix_tuple.daily,contact_time_distributions.rest)
+ rest_weekly_edges = WeightedGraph(demographics,index_vectors,rest_matrix_tuple.twice_a_week,contact_time_distributions.rest)
+ rest_daily_edges = WeightedGraph(demographics,index_vectors,rest_matrix_tuple.otherwise,contact_time_distributions.rest)
+ inf_network_list = [home_static_edges,rest_static_edges]
+ soc_network_list = [home_static_edges,rest_static_edges,ws_static_edges]
+
+ infected_mixing_graph = TimeDepMixingGraph(len,(ws_daily_edges,rest_daily_edges),inf_network_list)
+ soc_mixing_graph = TimeDepMixingGraph(len,(ws_daily_edges,rest_daily_edges),soc_network_list)
+ # display(infected_mixing_graph.graph_list)
+ for (t,l) in enumerate(infected_mixing_graph.graph_list)
+ 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)
+ push!(l,rest_daily_edges)
+ end
+
+ return infected_mixing_graph,soc_mixing_graph
+end
+
+function remake!(time_dep_mixing_graph,demographic_index_vectors,mixing_matrix)
+ for weighted_graph in time_dep_mixing_graph.resampled_graphs
+ empty!.(weighted_graph.g.fadjlist) #empty all the vector edgelists
+ empty!(weighted_graph.weights_dict)
+ weighted_graph.g.ne = 0
+
+ contacts = MixingContacts(demographic_index_vectors,mixing_matrix)
+
+ for i in 1:length(demographic_index_vectors), j in 1:i #diagonal
+ random_bipartite_graph_fast_CL!(
+ weighted_graph.g,
+ demographic_index_vectors[i],
+ demographic_index_vectors[j],
+ contacts.contact_array[i,j],
+ contacts.contact_array[j,i],
+ weighted_graph.weights_dict
+ )
+ end
+ end
+end
#defining my own weighted graph type cause we need to be able to resample the edge weights in a particular way
struct WeightedGraph{G, V,M}
g::G
- weights::V
+ weights_dict::V
weights_distribution_matrix::M
function WeightedGraph(demographics,demographic_index_vectors,mixing_matrix,weights_distribution_matrix)
@@ -43,29 +103,31 @@ struct WeightedGraph{G, V,M}
g = Graph(length(demographics))
- weights = RobinDict{Tuple{Int,Int},UInt8}()
+ weights_dict = RobinDict{Tuple{Int,Int},UInt8}()
for i in 1:length(demographic_index_vectors), j in 1:i #diagonal
- random_bipartite_graph_fast_CL!(g,demographic_index_vectors[i],demographic_index_vectors[j],contacts.contact_array[i,j],contacts.contact_array[j,i],weights)
+ random_bipartite_graph_fast_CL!(g,demographic_index_vectors[i],demographic_index_vectors[j],contacts.contact_array[i,j],contacts.contact_array[j,i],weights_dict)
end
covid_alert_time = zeros(nv(g))
- return new{typeof(g),typeof(weights),typeof(weights_distribution_matrix)}(
+ return new{typeof(g),typeof(weights_dict),typeof(weights_distribution_matrix)}(
g,
- weights,
+ weights_dict,
weights_distribution_matrix
)
end
function WeightedGraph(g::SimpleGraph,weights_distribution_matrix)
- weights = RobinDict{Tuple{Int,Int},UInt8}()
- return new{typeof(g),typeof(weights),typeof(weights_distribution_matrix)}(
+ weights_dict = RobinDict{Tuple{Int,Int},UInt8}()
+ return new{typeof(g),typeof(weights_dict),typeof(weights_distribution_matrix)}(
g,
- weights,
+ weights_dict,
weights_distribution_matrix
)
end
end
-
+function Base.show(io::IO, g::WeightedGraph)
+ print(io, "WG")
+end
function sample_mixing_graph!(mixing_graph,population_demographics)
for (k,e) in enumerate(edges(mixing_graph.g))
i = src(e)
@@ -74,8 +136,8 @@ function sample_mixing_graph!(mixing_graph,population_demographics)
demo_j = Int(population_demographics[j])
contact_time = rand(RNG, mixing_graph.weights_distribution_matrix[demo_i,demo_j])
- mixing_graph.weights[(i,j)] = contact_time
- mixing_graph.weights[(j,i)] = contact_time
+ mixing_graph.weights_dict[(i,j)] = contact_time
+ mixing_graph.weights_dict[(j,i)] = contact_time
end
end
@inline function reindex!(k,csum,index_list_i,index_list_j,j_to_i_contacts,i_to_j_contacts,sample_list_i,sample_list_j)
diff --git a/CovidAlertVaccinationModel/src/ABM/model_setup.jl b/CovidAlertVaccinationModel/src/ABM/model_setup.jl
index 6d623e82ac61196f3e620148b7b7513459732cc7..55ad33c8ff0ef06c34dbf1804c985f95dcc1359b 100644
--- a/CovidAlertVaccinationModel/src/ABM/model_setup.jl
+++ b/CovidAlertVaccinationModel/src/ABM/model_setup.jl
@@ -30,7 +30,7 @@ function get_u_0(nodes,I_0_fraction,vaccinator_prob)
return status,is_vaccinator
end
-struct ModelSolution{T,G}
+struct ModelSolution{T,InfNet,SocNet,WSMixingDist,RestMixingDist}
sim_length::Int
nodes::Int
params::T
@@ -40,8 +40,8 @@ struct ModelSolution{T,G}
u_vac::Vector{Bool}
covid_alert_times::Array{Int,2}
time_of_last_alert::Vector{Int}
- inf_network_lists::Vector{Vector{G}}
- soc_networks::Vector{G}
+ inf_network::InfNet
+ soc_network::SocNet
index_vectors::Vector{Vector{Int}}
demographics::Vector{AgentDemographic}
app_user::Vector{Bool}
@@ -49,62 +49,32 @@ struct ModelSolution{T,G}
app_user_index::Vector{Int}
status_totals::Vector{Int}
status_totals_next::Vector{Int}
+ ws_matrix_tuple::WSMixingDist
+ rest_matrix_tuple::RestMixingDist
function ModelSolution(sim_length,params::T,num_households) where T
demographics,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)
- ws_matrix_list = map_symmetrize(workschool_mixing)
- rest_matrix_list = map_symmetrize(rest_mixing)
+ ws_matrix_tuple = map_symmetrize(workschool_mixing)
+ rest_matrix_tuple = map_symmetrize(rest_mixing)
app_user_list = zeros(length(demographics))
is_app_user = rand(RNG,length(demographics)) .< params.app_user_fraction
app_user_index = findall(==(true),is_app_user)
app_user_list[is_app_user] .= collect(1:length(app_user_index))
- # display(app)
nodes = length(demographics)
u_0_inf,u_0_vac = get_u_0(nodes,params.I_0_fraction,params.vaccinator_prob)
- home_static_edges = WeightedGraph(base_network,contact_time_distributions.hh) #network with households and LTC homes
- ws_static_edges = WeightedGraph(demographics,index_vectors,ws_matrix_list.daily,contact_time_distributions.ws)
- ws_weekly_edges = WeightedGraph(demographics,index_vectors,ws_matrix_list.twice_a_week,contact_time_distributions.ws)
- ws_daily_edges_vector = [WeightedGraph(demographics,index_vectors,ws_matrix_list.otherwise,contact_time_distributions.ws) for i in 1:sim_length]
-
- rest_static_edges = WeightedGraph(demographics,index_vectors,rest_matrix_list.daily,contact_time_distributions.rest)
- rest_weekly_edges = WeightedGraph(demographics,index_vectors,rest_matrix_list.twice_a_week,contact_time_distributions.rest)
- rest_daily_edges_vector = [WeightedGraph(demographics,index_vectors,rest_matrix_list.otherwise,contact_time_distributions.rest) for i in 1:sim_length]
-
- inf_network_lists = [
- [home_static_edges,rest_static_edges] for i in 1:sim_length
- ]
- soc_network_list = [home_static_edges,rest_static_edges,ws_static_edges]
-
- for (t,l) in enumerate(inf_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
-
+ infected_mixing_graph,soc_mixing_graph = time_dep_mixing_graphs(sim_length,base_network,demographics,index_vectors,ws_matrix_tuple,rest_matrix_tuple)
+
covid_alert_times = zeros(Int,length(app_user_index),14) #two weeks worth of values
- time_of_last_alert = fill(-1,length(app_user_index)) #two weeks worth of values
-
- status_totals = zeros(Int, AgentStatus.size)
+ time_of_last_alert = fill(-1,length(app_user_index)) #time of last alert is negative if no alert has been recieved
- status_totals[1] = count(==(Susceptible), u_0_inf)
- status_totals[2] = count(==(Infected), u_0_inf)
- status_totals[3] = count(==(Recovered), u_0_inf)
- status_totals[4] = count(==(Immunized), u_0_inf)
+ status_totals = [count(==(i), u_0_inf) for i in 1:AgentStatus.size]
-
- return new{T,typeof(home_static_edges)}(
+ return new{T,typeof(infected_mixing_graph),typeof(soc_mixing_graph),typeof(ws_matrix_tuple),typeof(rest_matrix_tuple)}(
sim_length,
nodes,
params,
@@ -114,15 +84,17 @@ struct ModelSolution{T,G}
copy(u_0_vac),
covid_alert_times,
time_of_last_alert,
- inf_network_lists,
- soc_network_list,
+ infected_mixing_graph,
+ soc_mixing_graph,
index_vectors,
demographics,
is_app_user,
app_user_list,
app_user_index,
status_totals,
- copy(status_totals)
+ copy(status_totals),
+ ws_matrix_tuple,
+ rest_matrix_tuple
)
end
end
diff --git a/CovidAlertVaccinationModel/src/ABM/solve.jl b/CovidAlertVaccinationModel/src/ABM/solve.jl
index 817aa6a9c4ed9f1f7edb84d143e2409436975345..fc8684de1af695e54171381de3ca475d3d434712 100644
--- a/CovidAlertVaccinationModel/src/ABM/solve.jl
+++ b/CovidAlertVaccinationModel/src/ABM/solve.jl
@@ -4,16 +4,16 @@ function contact_weight(p, contact_time)
end
function update_alert_durations!(t,modelsol)
@unpack notification_parameter = modelsol.params
- @unpack time_of_last_alert, app_user_index,inf_network_lists,covid_alert_times,app_user = modelsol
+ @unpack time_of_last_alert, app_user_index,inf_network,covid_alert_times,app_user = modelsol
- for (i,node) in enumerate(modelsol.app_user_index), mixing_graph in modelsol.inf_network_lists[t]
+ for (i,node) in enumerate(modelsol.app_user_index), mixing_graph in modelsol.inf_network.graph_list[t]
for j in 2:14
covid_alert_times[i,j-1] = covid_alert_times[i,j] #shift them all back
end
for j in neighbors(mixing_graph.g,node)
if app_user[j]
- covid_alert_times[i,end] += mixing_graph.weights[(node,j)] #add the contact times for today to the back
+ covid_alert_times[i,end] += mixing_graph.weights_dict[(node,j)] #add the contact times for today to the back
end
end
if rand(RNG) < 1 - (1- notification_parameter)^sum(covid_alert_times[i,:])
@@ -23,7 +23,7 @@ function update_alert_durations!(t,modelsol)
end
function update_infection_state!(t,modelsol)
@unpack base_transmission_probability,immunization_loss_prob,recovery_rate = modelsol.params
- @unpack u_inf,u_vac,u_next_inf,u_next_vac,demographics,inf_network_lists,status_totals,status_totals_next = modelsol
+ @unpack u_inf,u_vac,u_next_inf,u_next_vac,demographics,inf_network,status_totals,status_totals_next = modelsol
function agent_transition!(node, from::AgentStatus,to::AgentStatus)
status_totals_next[Int(from)] -= 1
@@ -41,9 +41,9 @@ function update_infection_state!(t,modelsol)
if is_vaccinator
agent_transition!(i, Susceptible,Immunized)
else
- for mixing_graph in inf_network_lists[t]
+ for mixing_graph in inf_network.graph_list[t]
for j in neighbors(mixing_graph.g,i)
- if u_inf[j] == Infected && rand(RNG) < contact_weight(base_transmission_probability,mixing_graph.weights[(i,j)])
+ if u_inf[j] == Infected && rand(RNG) < contact_weight(base_transmission_probability,mixing_graph.weights_dict[(i,j)])
agent_transition!(i, Susceptible,Infected)
end
end
@@ -63,7 +63,7 @@ end
function update_vaccination_opinion_state!(t,modelsol,total_infections)
@unpack Ï€_base, η,γ, κ, ω, Ï, ω_en,Ï_en,γ,β = modelsol.params
- @unpack demographics,time_of_last_alert, nodes, soc_networks,u_vac,u_next_vac,app_user,app_user_list = modelsol
+ @unpack demographics,time_of_last_alert, nodes, soc_network,u_vac,u_next_vac,app_user,app_user_list = modelsol
app_user_pointer = 0
@@ -72,8 +72,8 @@ function update_vaccination_opinion_state!(t,modelsol,total_infections)
soc_nbrs_vac = [0,0,0]
soc_nbrs_nonvac = 0
num_soc_nbrs = 0
- for soc_network in soc_networks
- soc_nbrs = neighbors(soc_network.g,i)
+ for sc_g in soc_network.graph_list[t]
+ soc_nbrs = neighbors(sc_g.g,i)
num_soc_nbrs += length(soc_nbrs)
for nbr in soc_nbrs
if u_vac[nbr]
@@ -108,7 +108,10 @@ end
function agents_step!(t,modelsol)
- for network in modelsol.inf_network_lists[t]
+
+ remake!(modelsol.inf_network,modelsol.index_vectors,modelsol.ws_matrix_tuple.daily)
+ remake!(modelsol.inf_network,modelsol.index_vectors,modelsol.rest_matrix_tuple.daily)
+ for network in modelsol.inf_network.graph_list[t]
sample_mixing_graph!(network,modelsol.demographics) #get new contact weights
end
diff --git a/CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl b/CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl
index 6ef2ce693d8f2443d7add25c39352c0b7a0e54e1..4cc5eb67090e496582ec07a3b9adda0046a778d8 100644
--- a/CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl
+++ b/CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl
@@ -33,9 +33,9 @@ const RNG = Xoroshiro128Star(1)
const color_palette = palette(:seaborn_pastel) #color theme for the plots
include("utils.jl")
include("data.jl")
-include("ABM/agents.jl")
include("ABM/mixing_distributions.jl")
include("ABM/mixing_graphs.jl")
+include("ABM/agents.jl")
include("ABM/plotting.jl")
include("ABM/model_setup.jl")
include("ABM/output.jl")