initial opinion dynamics, just need to do app notifications now..

CovidAlertVaccinationModel/src/ABM/model.jl

@@ -16,11 +16,32 @@ function contact_weight(p, contact_time)
     return 1 - (1-p)^contact_time
 end
 
-function vac_switch_probability()
-    return 0.1
+function EN_payoff()
+    return 0.0
 end
 
-function agents_step!(t,u_next,u,population_list,network_list,params,covid_alert_times,app_user_index)
+function vac_switch_probability(u, population_list,vac, params,total_infected,soc_nbrs)
+    @unpack π_base, η, κ, ω, ρ, ω_en,ρ_en,γ,β = params 
+    if vac
+        soc_nbrs_vac = [0,0,0]
+        for nbr in soc_nbrs
+            if u.is_vaccinator[nbr]
+                soc_nbrs_vac[Int(population_list[nbr])] += 1
+            end
+        end
+        return Φ(π_base + κ * (sum(soc_nbrs_vac)/length(soc_nbrs)) + dot(ρ,soc_nbrs_vac) + 0,β)
+    else
+        soc_nbrs_nonvac = 0
+        for nbr in soc_nbrs
+            if u.is_vaccinator[nbr]
+                soc_nbrs_nonvac += 1
+            end
+        end
+        return  Φ(κ * soc_nbrs_nonvac/length(soc_nbrs),β)
+    end
+end
+
+function agents_step!(t,u_next,u,population_list,soc_network,network_list,params,covid_alert_times,app_user_index)
     @unpack p, recovery_rate, immunization_loss_prob  = params  
     
     u_next.status .= u.status
@@ -36,7 +57,7 @@ function agents_step!(t,u_next,u,population_list,network_list,params,covid_alert
     end
 
     for i in 1:length(population_list)
-        if rand(RNG)<vac_switch_probability()
+        if rand(RNG)<vac_switch_probability(u, population_list,u.is_vaccinator[i],params,count(==(Infected),u),neighbors(soc_network.g,i))
             u_next.is_vaccinator[i] = !u.is_vaccinator[i]
         end
     end
@@ -120,7 +141,7 @@ function solve!(params,steps,agent_model; record = false)
             sample_mixing_graph!(network,population_list, contact_time_distribution_matrix) #get new contact weights
         end
         #advance agent states based on the new network
-        agents_step!(t,solution[t+1],solution[t],population_list,network_list,params,covid_alert_times,app_user_index) 
+        agents_step!(t,solution[t+1],solution[t],population_list,home_static_edges,network_list,params,covid_alert_times,app_user_index) 
     end
     return solution, network_lists
 end
@@ -128,11 +149,22 @@ function get_parameters()
     params = (
         p = 0.05,
         recovery_rate = 0.1,
-        vaccines_per_day = 0.0, #percentage of population vaccinated per day
-        vaccination_start_day = 60,
-        immunization_loss_prob = 0.01
+        immunization_loss_prob = 0.01,
+        π_base = 0.1,
+        η = 0.0,
+        κ = 0.0,
+        ω = 0.0,
+        ρ = [0.0,0.0,0.0],
+        ω_en = 0.0,
+        ρ_en = 0.0,
+        γ = 0.0,
+        β = 0.1
     )
     return params
 end
 
+function Φ(payoff,β)
+    return 1 / (exp(-1*β*payoff))
+end
+
 

CovidAlertVaccinationModel/src/data.jl

@@ -101,7 +101,7 @@ function load_mixing_matrices()
 end
 
 function load_contact_time_distributions()
-    dat = deserialize(joinpath(PACKAGE_FOLDER,"data/intervals_model_particle/hh.dat"))
+    dat = deserialize(joinpath(PACKAGE_FOLDER,"intervals_model_output/simulation_output/hh.dat"))
     return dat
 end
 """