shift durations for weekly and daily edges, change gamma to reasonable value

CovidAlertVaccinationModel/src/ABM/mixing_distributions.jl

@@ -36,6 +36,12 @@ See `load_contact_time_distributions()`.
 """
 const contact_time_distributions = load_contact_time_distributions()
 
+function shift_contact_distributions(sampler_matrix::AbstractMatrix{T},proportion) where T<:Distributions.PoissonADSampler
+    return map(sampler_matrix) do sampler
+        μ_old = sampler.μ
+        return Distributions.PoissonADSampler(μ_old*proportion)
+    end
+end
 
 ```
 Given alphas given in `unemployment_matrix` computes the zero weight for each pair of demographic contacts given individual zero weights.

CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl

@@ -81,19 +81,24 @@ Assumes the simulation begins on Thursday arbitrarily.
 """
 function time_dep_mixing_graphs(len,base_network,demographics,index_vectors,ws_matrix_tuple,rest_matrix_tuple)
 
+    #weekly multiply durations by 1/5
+    daily_shift(dur_dists) = shift_contact_distributions(dur_dists,1/5)
+    #daily multiply by 1/2
+    weekly_shift(dur_dists) = shift_contact_distributions(dur_dists,1/2)
+
     home_static_edges = WeightedGraph(base_network,demographics,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_justonce_edges = WeightedGraph(demographics,index_vectors,ws_matrix_tuple.otherwise,contact_time_distributions.ws)
+    ws_weekly_edges = WeightedGraph(demographics,index_vectors,ws_matrix_tuple.twice_a_week,weekly_shift(contact_time_distributions.ws))
+    ws_justonce_edges = WeightedGraph(demographics,index_vectors,ws_matrix_tuple.otherwise,daily_shift(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_justonce_edges = WeightedGraph(demographics,index_vectors,rest_matrix_tuple.otherwise,contact_time_distributions.rest)
+    rest_weekly_edges = WeightedGraph(demographics,index_vectors,rest_matrix_tuple.twice_a_week,weekly_shift(contact_time_distributions.rest))
+    rest_justonce_edges = WeightedGraph(demographics,index_vectors,rest_matrix_tuple.otherwise,daily_shift(contact_time_distributions.rest))
    
-    #weekly multiply durations by 1/5
-    #daily multiply by 1/2
+ 
     
+
     inf_network_list = [home_static_edges,rest_static_edges,ws_justonce_edges,rest_justonce_edges] 
     soc_network_list = [home_static_edges,rest_static_edges,ws_static_edges]
 

CovidAlertVaccinationModel/src/ABM/model_setup.jl

@@ -21,7 +21,7 @@
         κ = 0.0,
         ω = 0.0061,
         ω_en = 0.05,
-        Γ = 1/7, #make this .906
+        Γ = 0.906,
         ξ = 5.0,
         notification_parameter = 0.0005,
         vaccinator_prob = 0.6,

CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl

 module CovidAlertVaccinationModel
+using Distributions: PoissonADSampler
 using Intervals: Ending
 using Base: Float64, NamedTuple
 using LightGraphs

CovidAlertVaccinationModel/src/IntervalsModel/ws_durations_model.jl

@@ -38,7 +38,7 @@ function err_ws(p,dist)
 
             for age_j in YOUNG:OLD #for a given age_sample loop over possible contact ages        
                 if neighourhoods[age_j] > 0
-                    #get durations from our candidate distribtions for each of the contacts in neighbourhood
+                    #get durations from our candidate distributions for each of the contacts in neighbourhood
                     durs = trunc.(Int,rand(Random.default_rng(Threads.threadid()),age_dists[age_sample,age_j],neighourhoods[age_j])) .% durmax
 
                     #this MODIFIES sample_list to contain samples from the distribution of total_durations, given intervals of length dur.

CovidAlertVaccinationModel/src/data.jl

@@ -101,6 +101,9 @@ function load_mixing_matrices()
     return map(t -> from_mean.(Geometric{Float64},t),workschool_mixing), map(t -> from_mean.(Geometric{Float64}, t),rest_mixing)
 end
 
+function make_sampler(λ)
+    return Distributions.PoissonADSampler(λ)#Distributions.DiscreteNonParametricSampler(0:durmax,[pdf(Poisson(λ),x) for x in 0:durmax])
+end
 function load_contact_time_distributions()
     distkey = "Distributions.Poisson"
     fnames = (
@@ -108,9 +111,6 @@ function load_contact_time_distributions()
         ws = "ws",
         rest = "rest"
     )
-    function make_sampler(λ)
-        return Distributions.PoissonADSampler(λ)#Distributions.DiscreteNonParametricSampler(0:durmax,[pdf(Poisson(λ),x) for x in 0:durmax])
-    end
     contact_distributions_tuple = map(fnames) do fname
         dat = deserialize(joinpath(PACKAGE_FOLDER,"intervals_model_output","simulation_output","$fname.dat"))
         return map(p -> make_sampler(mode(p.particles)), as_symmetric_matrix(dat[distkey].P))