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simulations_cluster.jl
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simulations_cluster.jl
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using Distributed
using Base.Filesystem
using DataFrames
using CSV
using Query
using Statistics
using UnicodePlots
using ClusterManagers
using Dates
using DelimitedFiles
## load the packages by covid19abm
#using covid19abm
#addprocs(2, exeflags="--project=.")
#@everywhere using covid19abm
addprocs(SlurmManager(500), N=17, topology=:master_worker, exeflags="--project=.")
@everywhere using Parameters, Distributions, StatsBase, StaticArrays, Random, Match, DataFrames
@everywhere include("covid19abm.jl")
@everywhere const cv=covid19abm
function run(myp::cv.ModelParameters, nsims=1000, folderprefix="./")
println("starting $nsims simulations...\nsave folder set to $(folderprefix)")
dump(myp)
# will return 6 dataframes. 1 total, 4 age-specific
cdr = pmap(1:nsims) do x
cv.runsim(x, myp)
end
println("simulations finished")
println("total size of simulation dataframes: $(Base.summarysize(cdr))")
## write the infectors
DelimitedFiles.writedlm("$(folderprefix)/infectors.dat", [cdr[i].infectors for i = 1:nsims])
## write contact numbers
#writedlm("$(folderprefix)/ctnumbers.dat", [cdr[i].ct_numbers for i = 1:nsims])
## stack the sims together
allag = vcat([cdr[i].a for i = 1:nsims]...)
ag1 = vcat([cdr[i].g1 for i = 1:nsims]...)
ag2 = vcat([cdr[i].g2 for i = 1:nsims]...)
ag3 = vcat([cdr[i].g3 for i = 1:nsims]...)
ag4 = vcat([cdr[i].g4 for i = 1:nsims]...)
ag5 = vcat([cdr[i].g5 for i = 1:nsims]...)
ag6 = vcat([cdr[i].g6 for i = 1:nsims]...)
mydfs = Dict("all" => allag, "ag1" => ag1, "ag2" => ag2, "ag3" => ag3, "ag4" => ag4, "ag5" => ag5, "ag6" => ag6)
#mydfs = Dict("all" => allag)
## save at the simulation and time level
## to ignore for now: miso, iiso, mild
#c1 = Symbol.((:LAT, :ASYMP, :INF, :PRE, :MILD,:IISO, :HOS, :ICU, :DED), :_INC)
#c2 = Symbol.((:LAT, :ASYMP, :INF, :PRE, :MILD,:IISO, :HOS, :ICU, :DED), :_PREV)
if !myp.heatmap
c1 = Symbol.((:LAT, :HOS, :ICU, :DED,:LAT2, :HOS2, :ICU2, :DED2,:LAT3, :HOS3, :ICU3, :DED3), :_INC)
#c2 = Symbol.((:LAT, :HOS, :ICU, :DED,:LAT2, :HOS2, :ICU2, :DED2), :_PREV)
for (k, df) in mydfs
println("saving dataframe sim level: $k")
# simulation level, save file per health status, per age group
#for c in vcat(c1..., c2...)
for c in vcat(c1...)
#for c in vcat(c2...)
udf = unstack(df, :time, :sim, c)
fn = string("$(folderprefix)/simlevel_", lowercase(string(c)), "_", k, ".dat")
CSV.write(fn, udf)
end
println("saving dataframe time level: $k")
# time level, save file per age group
#yaf = compute_yearly_average(df)
#fn = string("$(folderprefix)/timelevel_", k, ".dat")
#CSV.write(fn, yaf)
end
else
c1 = Symbol.((:LAT, :HOS, :ICU, :DED,:LAT2, :HOS2, :ICU2, :DED2,:LAT3, :HOS3, :ICU3, :DED3), :_INC)
#c2 = Symbol.((:LAT, :HOS, :ICU, :DED,:LAT2, :HOS2, :ICU2, :DED2), :_PREV)
for (k, df) in mydfs
println("saving dataframe sim level: $k")
# simulation level, save file per health status, per age group
for c in vcat(c1...)
#for c in vcat(c2...)
udf = unstack(df, :time, :sim, c)
fn = string("$(folderprefix)/simlevel_", lowercase(string(c)), "_", k, ".dat")
CSV.write(fn, udf)
end
println("saving dataframe time level: $k")
# time level, save file per age group
#yaf = compute_yearly_average(df)
#fn = string("$(folderprefix)/timelevel_", k, ".dat")
#CSV.write(fn, yaf)
end
end
########## save general info about vaccine
#= n_vac_sus1 = [cdr[i].n_vac_sus1 for i=1:nsims]
n_vac_rec1 = [cdr[i].n_vac_rec1 for i=1:nsims]
n_inf_vac1 = [cdr[i].n_inf_vac1 for i=1:nsims]
n_dead_vac1 = [cdr[i].n_dead_vac1 for i=1:nsims]
n_hosp_vac1 = [cdr[i].n_hosp_vac1 for i=1:nsims]
n_icu_vac1 = [cdr[i].n_icu_vac1 for i=1:nsims]
n_vac_sus2 = [cdr[i].n_vac_sus2 for i=1:nsims]
n_vac_rec2 = [cdr[i].n_vac_rec2 for i=1:nsims]
n_inf_vac2 = [cdr[i].n_inf_vac2 for i=1:nsims]
n_dead_vac2 = [cdr[i].n_dead_vac2 for i=1:nsims]
n_hosp_vac2 = [cdr[i].n_hosp_vac2 for i=1:nsims]
n_icu_vac2 = [cdr[i].n_icu_vac2 for i=1:nsims]
n_dead_nvac = [cdr[i].n_dead_nvac for i=1:nsims]
n_inf_nvac = [cdr[i].n_inf_nvac for i=1:nsims]
n_hosp_nvac = [cdr[i].n_hosp_nvac for i=1:nsims]
n_icu_nvac = [cdr[i].n_icu_nvac for i=1:nsims] =#
R01 = [cdr[i].R01 for i=1:nsims]
R02 = [cdr[i].R02 for i=1:nsims]
#data = DataFrame(vac_sus_dose1 = n_vac_sus1,vac_herd_dose_1 = n_vac_rec1,inf_dose_1 = n_inf_vac1, dead_dose_1 = n_dead_vac1, hosp_dose_1 = n_hosp_vac1,icu_dose_1 = n_icu_vac1, vac_sus_dose_2 = n_vac_sus2, vac_herd_dose_2 = n_vac_rec2, inf_dose_2 = n_inf_vac2, dead_dose_2 = n_dead_vac2, hosp_dose_2 = n_hosp_vac2, icu_dose_2 = n_icu_vac2, inf_n_vac = n_inf_nvac,dead_n_vac = n_dead_nvac,hosp_n_vac = n_hosp_nvac,icu_n_vac = n_icu_nvac)
#writedlm(string(folderprefix,"/general_vac_info.dat"),data)
#CSV.write("$folderprefix/general_vac_info.csv",data)
#= writedlm(string(folderprefix,"/com_vac1.dat"),[cdr[i].com_v1 for i=1:nsims])
writedlm(string(folderprefix,"/ncom_vac1.dat"),[cdr[i].ncom_v1 for i=1:nsims])
writedlm(string(folderprefix,"/com_vac2.dat"),[cdr[i].com_v2 for i=1:nsims])
writedlm(string(folderprefix,"/ncom_vac2.dat"),[cdr[i].ncom_v2 for i=1:nsims])
writedlm(string(folderprefix,"/com_total.dat"),[cdr[i].com_t for i=1:nsims])
writedlm(string(folderprefix,"/ncom_total.dat"),[cdr[i].ncom_t for i=1:nsims]) =#
writedlm(string(folderprefix,"/R01.dat"),R01)
writedlm(string(folderprefix,"/R02.dat"),R02)
writedlm(string(folderprefix,"/init_iso.dat"),[cdr[i].iniiso for i=1:nsims])
return mydfs
end
function compute_yearly_average(df)
ya = df |> @groupby(_.time) |> @map({time=key(_), cnt=length(_),
sus_prev=mean(_.SUS_PREV),
lat_prev=mean(_.LAT_PREV),
pre_prev=mean(_.PRE_PREV),
asymp_prev=mean(_.ASYMP_PREV),
mild_prev=mean(_.MILD_PREV),
miso_prev=mean(_.MISO_PREV),
inf_prev=mean(_.INF_PREV),
iiso_prev=mean(_.IISO_PREV),
hos_prev=mean(_.HOS_PREV),
icu_prev=mean(_.ICU_PREV),
rec_prev=mean(_.REC_PREV),
ded_prev=mean(_.DED_PREV),
sus_inc=mean(_.SUS_INC),
lat_inc=mean(_.LAT_INC),
pre_inc=mean(_.PRE_INC),
asymp_inc=mean(_.ASYMP_INC),
mild_inc=mean(_.MILD_INC),
miso_inc=mean(_.MISO_INC),
inf_inc=mean(_.INF_INC),
iiso_inc=mean(_.IISO_INC),
hos_inc=mean(_.HOS_INC),
icu_inc=mean(_.ICU_INC),
rec_inc=mean(_.REC_INC),
ded_inc=mean(_.DED_INC)
}) |> DataFrame
return ya
end
#=
function savestr(p::cv.ModelParameters, custominsert="/", customstart="")
datestr = (Dates.format(Dates.now(), dateformat"mmdd_HHMM"))
## setup folder name based on model parameters
taustr = replace(string(p.τmild), "." => "")
fstr = replace(string(p.fmild), "." => "")
rstr = replace(string(p.β), "." => "")
prov = replace(string(p.prov), "." => "")
eldr = replace(string(p.eldq), "." => "")
eldqag = replace(string(p.eldqag), "." => "")
fpreiso = replace(string(p.fpreiso), "." => "")
tpreiso = replace(string(p.tpreiso), "." => "")
fsev = replace(string(p.fsevere), "." => "")
frelasymp = replace(string(p.frelasymp), "." => "")
strat = replace(string(p.ctstrat), "." => "")
pct = replace(string(p.fctcapture), "." => "")
cct = replace(string(p.fcontactst), "." => "")
idt = replace(string(p.cidtime), "." => "")
tback = replace(string(p.cdaysback), "." => "")
fldrname = "/data/covid19abm/simresults/$(custominsert)/$(customstart)_$(prov)_strat$(strat)_pct$(pct)_cct$(cct)_idt$(idt)_tback$(tback)_fsev$(fsev)_tau$(taustr)_fmild$(fstr)_q$(eldr)_qag$(eldqag)_relasymp$(frelasymp)_tpreiso$(tpreiso)_preiso$(fpreiso)/"
mkpath(fldrname)
end=#
function _calibrate(nsims, myp::cv.ModelParameters)
myp.calibration != true && error("calibration parameter not turned on")
vals = zeros(Int64, nsims)
println("calibrating with beta: $(myp.β), total sims: $nsims, province: $(myp.prov)")
println("calibration parameters:")
dump(myp)
cdr = pmap(1:nsims) do i
h,hh = cv.main(myp,i) ## gets the entire model.
val = sum(cv._get_column_incidence(h, covid19abm.LAT))
return val
end
return mean(cdr), std(cdr)
end
function calibrate(beta, nsims, herdi = 0, cali2 = false, fs = 0.0, prov=:usa, init_inf=1, size=10000)
myp = cv.ModelParameters() # set up default parameters
myp.β = beta
myp.prov = prov
myp.popsize = size
myp.modeltime = 30
myp.calibration = true
myp.calibration2 = cali2
myp.fsevere = fs
myp.fmild = fs
myp.initialinf = init_inf
myp.herd = herdi
m, sd = _calibrate(nsims, myp)
println("mean R0: $(m) with std: $(sd)")
myp.calibration = false
return m
end
function calibrate_robustness(beta, reps, prov=:usa)
#[:ontario, :alberta, :bc, :manitoba, :newbruns, :newfdland, :nwterrito, :novasco, :nunavut, :pei, :quebec, :saskat, :yukon]
# once a beta is found based on nsims simulations,
# see how robust it is. run calibration with same beta 100 times
# to see the variation in R0 produced.
#nsims = [1000]
means = zeros(Float64, reps)
#for (i, ns) in enumerate(nsims)
cd = map(1:reps) do x
println("iter: $x")
mval = calibrate(beta,10000)
return mval
end
#end
# for i in 2:nworkers()
# ## mf defined as: @everywhere mg() = covid19abm.p.β
# rpr = remotecall_fetch(mf, i+1).prov
# rpr != prov && error("province didn't get set in the remote workers")
# end
return cd
end
function create_folder(ip::cv.ModelParameters,vac="none")
#RF = string("heatmap/results_prob_","$(replace(string(ip.β), "." => "_"))","_vac_","$(replace(string(ip.vaccine_ef), "." => "_"))","_herd_immu_","$(ip.herd)","_$strategy","cov_$(replace(string(ip.cov_val)))") ##
main_folder = "/data/thomas-covid/CDC_guidelines"
#main_folder = "."
#RF = string(main_folder,"/results_prob_","$(replace(string(ip.β), "." => "_"))","_herd_immu_","$(ip.herd)","_$vac","_$(ip.third_strain_trans)_$(ip.strain_ef_red3)_$(ip.file_index)") ##
#this one below is for US_april in the lancet
RF = string(main_folder,"/results_prob_","$(replace(string(ip.β), "." => "_"))","_herd_immu_","$(ip.herd)","_$vac","_$(ip.third_strain_trans)_$(ip.file_index)") ##
if !Base.Filesystem.isdir(RF)
Base.Filesystem.mkpath(RF)
end
return RF
end
#run_param_fix(0.12,20,60,1.0,1.0,1.4,true,"pfizer")
## now, running vaccine and herd immunity, focusing and not focusing in comorbidity, first argument turns off vac
#= function run_param(b,h_i = 0,ic=1,fs=0.0,fm=0.0,strain2_trans=1.0,vaccinate = false,vac = "none",when_= 999,hl=1,hm=0.0,nsims=500)
if vac == "pfizer"
sdd = 21
pd = [[14],[0;7]]
inf=[[0.46],[0.6;0.92]]
symp=[[0.57],[0.66;0.94]]
sev= [[0.62],[0.80;0.92]]
ag_v = 16
elseif vac == "moderna"
sdd = 28
pd=[[14],[0;14]]
inf=[[0.61],[0.61;0.935]]
symp=[[0.921],[0.921;0.941]]
sev=[[0.921],[0.921;1.0]]
ag_v = 18
elseif vac == "vac1"
sdd = 28
pd=[[14],[0]]
inf=[[0.50],[0.7]]
symp=[[0.75],[0.8]]
sev=[[0.75],[0.8]]
ag_v = 18
else
sdd = 21
pd=[[14],[0;14]]
inf=[[0.61],[0.61;0.935]]
symp=[[0.921],[0.921;0.941]]
sev=[[0.921],[0.921;1.0]]
ag_v = 18
end
#b = bd[h_i]
#ic = init_con[h_i]
@everywhere ip = cv.ModelParameters(β=$b,fsevere = $fs,fmild = $fm,vaccinating = $vaccinate,vac_efficacy_inf = $inf,
vac_efficacy_symp=$symp, vac_efficacy_sev = $sev,
herd = $(h_i),start_several_inf=true,initialinf=$ic,
ins_sec_strain = true,sec_dose_delay = $sdd,vac_period = $sdd,days_to_protection=$pd,
sec_strain_trans=$strain2_trans,
min_age_vac=$ag_v, time_change = $when_, how_long = $hl, how_much = $hm)
folder = create_folder(ip,vac)
#println("$v_e $(ip.vaccine_ef)")
run(ip,nsims,folder)
end
=#
#run_param_fix(0.12,20,60,1.0,1.0,1.4,true,"pfizer")
##
function run_param_scen(b,h_i = 0,ic=1,fs=0.0,fm=0.0,strain2_trans=1.5,vac = "none",red = 0.0,index = 0,tr=999,when_= 999,dosis=3,ta = 999,nsims=500)
if vac == "pfizer"
sdd = 21
pd = [[14],[0;7]]
inf=[[0.46],[0.6;0.92]]
symp=[[0.57],[0.66;0.94]]
sev= [[0.62],[0.80;0.92]]
ag_v = 16
elseif vac == "moderna"
sdd = 28
pd=[[14],[0;14]]
inf=[[0.61],[0.61;0.935]]
symp=[[0.921],[0.921;0.941]]
sev=[[0.921],[0.921;1.0]]
ag_v = 18
elseif vac == "vac1"
sdd = 28
pd=[[14],[0]]
inf=[[0.50],[0.7]]
symp=[[0.75],[0.8]]
sev=[[0.75],[0.8]]
ag_v = 18
else
sdd = 21
pd=[[14],[0;14]]
inf=[[0.61],[0.61;0.935]]
symp=[[0.921],[0.921;0.941]]
sev=[[0.921],[0.921;1.0]]
ag_v = 18
end
#b = bd[h_i]
#ic = init_con[h_i]
@everywhere ip = cv.ModelParameters(β=$b,fsevere = $fs,fmild = $fm,vaccinating = true,vac_efficacy_inf = $inf,
vac_efficacy_symp=$symp, vac_efficacy_sev = $sev,
herd = $(h_i),start_several_inf=true,initialinf3=$ic,
ins_sec_strain = true,sec_dose_delay = $sdd,vac_period = $sdd,days_to_protection=$pd,
third_strain_trans=$strain2_trans, strain_ef_red3 = $red,
min_age_vac=$ag_v, time_back_to_normal = $when_,relaxing_time=$tr,status_relax = $dosis, relax_after = $ta,file_index = $index)
folder = create_folder(ip,vac)
#println("$v_e $(ip.vaccine_ef)")
run(ip,nsims,folder)
end
function run_param_scen_cal(b,h_i = 0,ic=1,fs=0.0,fm=0.0,strain2_trans=1.5,vaccinate = false,vac = "none",red = 0.0,index = 0,when_= 999,hl = 1,hm = 0.0,nsims=500)
if vac == "pfizer"
sdd = 21
pd = [[14],[0;7]]
inf=[[0.46],[0.6;0.92]]
symp=[[0.57],[0.66;0.94]]
sev= [[0.62],[0.80;0.92]]
ag_v = 16
elseif vac == "moderna"
sdd = 28
pd=[[14],[0;14]]
inf=[[0.61],[0.61;0.935]]
symp=[[0.921],[0.921;0.941]]
sev=[[0.921],[0.921;1.0]]
ag_v = 18
elseif vac == "vac1"
sdd = 28
pd=[[14],[0]]
inf=[[0.50],[0.7]]
symp=[[0.75],[0.8]]
sev=[[0.75],[0.8]]
ag_v = 18
else
sdd = 21
pd=[[14],[0;14]]
inf=[[0.61],[0.61;0.935]]
symp=[[0.921],[0.921;0.941]]
sev=[[0.921],[0.921;1.0]]
ag_v = 18
end
#b = bd[h_i]
#ic = init_con[h_i]
@everywhere ip = cv.ModelParameters(β=$b,fsevere = $fs,fmild = $fm,vaccinating = $vaccinate,vac_efficacy_inf = $inf,
vac_efficacy_symp=$symp, vac_efficacy_sev = $sev,
herd = $(h_i),start_several_inf=true,initialinf3=$ic,
ins_sec_strain = true,sec_dose_delay = $sdd,vac_period = $sdd,days_to_protection=$pd,
third_strain_trans=$strain2_trans, strain_ef_red3 = $red,
min_age_vac=$ag_v, time_back_to_normal = 307,how_long = $hl,how_much = $hm,time_change = $when_,status_relax = 1, relax_after = 14,file_index = $index)
folder = create_folder(ip,vac)
#println("$v_e $(ip.vaccine_ef)")
run(ip,nsims,folder)
end
## now, running vaccine and herd immunity, focusing and not focusing in comorbidity, first argument turns off vac
function run_calibration(beta = 0.0345,herd_im_v = 0,fs = 0.0,insert_sec=false,strain2_trans=1.0,nsims=1000)
init_con = Dict(5=>3, 10=>4, 20=>6, 30=>9)
ic = init_con[herd_im_v]
time_s = insert_sec ? 100 : 30
@everywhere ip = cv.ModelParameters(β=$beta,herd = $herd_im_v,modeltime = $(time_s),initialinf = $ic,ins_sec_strain = $insert_sec,
sec_strain_trans=$strain2_trans,fsevere = $fs,fmild=$fs,start_several_inf=true)
folder = create_folder(ip)
#println("$v_e $(ip.vaccine_ef)")
run(ip,nsims,folder)
R0 = readdlm(string(folder,"/R01.dat"),header=false)[:,1]
m = mean(R0)
sd = std(R0)
R02 = readdlm(string(folder,"/R02.dat"),header=false)[:,1]
m2 = mean(R02)
sd2 = std(R02)
println("mean R01: $(m) with std1: $(sd) mean R02: $(m2) with std2: $(sd2)")
end