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covid19abm.jl
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covid19abm.jl
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module covid19abm
using Base
using Parameters, Distributions, StatsBase, StaticArrays, Random, Match, DataFrames
@enum HEALTH SUS LAT PRE ASYMP MILD MISO INF IISO HOS ICU REC DED LAT2 PRE2 ASYMP2 MILD2 MISO2 INF2 IISO2 HOS2 ICU2 REC2 DED2 LAT3 PRE3 ASYMP3 MILD3 MISO3 INF3 IISO3 HOS3 ICU3 REC3 DED3 UNDEF
Base.@kwdef mutable struct Human
idx::Int64 = 0
health::HEALTH = SUS
health_status::HEALTH = SUS
swap::HEALTH = UNDEF
swap_status::HEALTH = UNDEF
sickfrom::HEALTH = UNDEF
sickby::Int64 = -1
nextday_meetcnt::Int16 = 0 ## how many contacts for a single day
nextday_meetcnt_w::Int16 = 0
age::Int16 = 0 # in years. don't really need this but left it incase needed later
ag::Int16 = 0
tis::Int16 = 0 # time in state
exp::Int16 = 0 # max statetime
dur::NTuple{4, Int8} = (0, 0, 0, 0) # Order: (latents, asymps, pres, infs) TURN TO NAMED TUPS LATER
doi::Int16 = 999 # day of infection.
iso::Bool = false ## isolated (limited contacts)
isovia::Symbol = :null ## isolated via quarantine (:qu), preiso (:pi), intervention measure (:im), or contact tracing (:ct)
comorbidity::Int8 = 0 ##does the individual has any comorbidity?
vac_status::Int8 = 0 ##
wentto::Int8 = 0
incubationp::Int16 = 0
got_inf::Bool = false
herd_im::Bool = false
ag_new::Int16 = -1
days_vac::Int16 = -1
strain::Int16 = -1
index_day::Int16 = 1
recovered::Bool = false
vaccine::Symbol = :none
vaccine_n::Int16 = 0
protected::Int16 = 0
days_recovered::Int32 = -1
boosted::Bool = false
n_boosted::Int8 = 0
recvac::Int8 = 0 # 1 - rec , 2 - vac ... this field shows which immunity will be used for protection
vac_eff_inf::Array{Array{Array{Float32,1},1},1} = [[[0.0]]]
vac_eff_symp::Array{Array{Array{Float32,1},1},1} = [[[0.0]]]
vac_eff_sev::Array{Array{Array{Float32,1},1},1} = [[[0.0]]]
workplace_idx::Int64 = -1
#### for testing
daysisolation::Int64 = 999
days_after_detection::Int64 = 999
positive::Bool = false
days_for_pcr::Int32 = -1
daysinf::Int64 = 999
tookpcr::Bool = false
nra::Int16 = 0
pcrprob::Float64 = 0.0
test::Bool = false
isolate_mild::Bool = false
isolate_sev::Bool = false
isofalse::Bool = false
waning::Vector{Float64} = [1.0;1.0]
proportion_contacts_workplace::Float64 = 0.0
totaldaysiso::Int32 = 0
end
## default system parameters
@with_kw mutable struct ModelParameters @deftype Float64 ## use @with_kw from Parameters
β = 0.0345
seasonal::Bool = false ## seasonal betas or not
popsize::Int64 = 100000
prov::Symbol = :ontario
calibration::Bool = false
calibration2::Bool = false
start_several_inf::Bool = true
modeltime::Int64 = 435
initialinf::Int64 = 1
fmild::Float64 = 0.5 ## percent of people practice self-isolation
fsevere::Float64 = 1.0 #
frelasymp::Float64 = 0.26 ## relative transmission of asymptomatic
fctcapture::Float16 = 0.0 ## how many symptomatic people identified
#vaccine_ef::Float16 = 0.0 ## change this to Float32 typemax(Float32) typemax(Float64)
herd::Int8 = 0 #typemax(Int32) ~ millions
file_index::Int16 = 0
nstrains::Int16 = 3
strain::Int64 = 1 ## which strain is spreading
#the cap for coverage should be 90% for 65+; 95% for HCW; 80% for 50-64; 60% for 16-49; and then 50% for 12-15 (starting from June 1).
#comor_comp::Float64 = 0.7 #prop comorbidade tomam
##Alpha - B.1.1.7
sec_strain_trans::Float64 = 1.5#1.5 #transmissibility of second strain
## Gamma - P.1
third_strain_trans::Float64 = 1.6 #transmissibility of third strain
## Delta - B.1.617.2
fourth_strain_trans::Float64 = 1.3 #transmissibility compared to second strain strain
## Iota - B.1.526
fifth_strain_trans::Float64 = 1.35 #transmissibility of fifth strain
##OMICRON
transmissibility_omicron::Float64 = 1.0
immunity_omicron::Float64 = 0.0
rel_trans_sixth::Float64 = transmissibility_omicron*1.35
sixth_strain_trans::Float64 = rel_trans_sixth*sec_strain_trans*fourth_strain_trans #transmissibility of sixth strain
reduction_sev_omicron::Float64 = 0.752 ##reduction of severity compared to Delta
mortality_inc::Float64 = 1.3 #The mortality increase when infected by strain 2
vaccine_proportion::Vector{Float64} = [0.78; 0.22; 0.0]
vaccine_proportion_2::Vector{Float64} = [0.78; 0.22; 0.0]
vac_period::Array{Int64,1} = [21;28;999]
#=------------ Vaccine Efficacy ----------------------------=#
booster_after::Array{Int64,1} = [150;150;999]
n_boosts::Int64 = 1
min_age_booster::Int64 = 16
#=------------ Vaccine Efficacy ----------------------------=#
days_to_protection::Array{Array{Array{Int64,1},1},1} = [[[14;21],[0;7]],[[14;21],[0;7]],[[14]]]
vac_efficacy_inf::Array{Array{Array{Array{Float32,1},1},1},1} = [[[[0.46;0.46],[0.46;0.861]],[[0.416;0.416],[0.416;0.85]],[[0.16;0.16],[0.16;0.33]]],#booster efficacy for omicron changed in vac_time function
[[[0.843;0.843],[0.843,0.964]],[[0.77;0.77],[0.77,0.867]],[[0.16;0.16],[0.16,0.428]]],
[[[0.61]],[[0.56]],[[0.488]],[[0.496]],[[0.61]],[[0.488]]]]#### 50:5:80
vac_efficacy_symp::Array{Array{Array{Array{Float32,1},1},1},1} = [[[[0.63;0.65],[0.65;0.93]],[[0.57;0.59],[0.59;0.92]],[[0.616;0.616],[0.616;0.69]]], #booster efficacy for omicronmchanged in vac_time function
[[[0.63;0.7],[0.7,0.96]],[[0.7;0.69],[0.69,0.95]],[[0.678;0.678],[0.678,0.69]]], #### 50:5:80
[[[0.921]],[[0.88]],[[0.332]],[[0.68]],[[0.921]],[[0.332]]]] #### 50:5:80
vac_efficacy_sev::Array{Array{Array{Array{Float32,1},1},1},1} = [[[[0.77;0.88],[0.88;0.98]],[[0.81;0.81],[0.81;0.97]],[[0.676;0.676],[0.676;0.81]]], #booster efficacy for omicron changed in vac_time function
[[[0.66;0.7],[0.7,0.97]],[[0.9;0.91],[0.91,0.98]],[[0.744;0.744],[0.744,0.81]]],#### 50:5:80
[[[0.921]],[[0.816]],[[0.34]],[[0.781]],[[0.921]],[[0.34]]]]#### 50:5:80
# ----- Recovery efficacy ----- #
#https://www.nejm.org/doi/full/10.1056/NEJMc2200133
# using infection the same as symptoms
rec_eff_inf::Vector{Float64} = [1.0;0.92;0.56]
rec_eff_symp::Vector{Float64} = [1.0;0.92;0.56]
rec_eff_sev::Vector{Float64} = [1.0;1.0;0.878]
time_change_contact::Array{Int64,1} = [1]
change_rate_values::Array{Float64,1} = [1.0]
contact_change_rate::Float64 = 1.0 #the rate that receives the value of change_rate_values
contact_change_2::Float64 = 1.0 ##baseline number that multiplies the contact rate
turnon::Int64 = 1
using_jj::Bool = false
#one waning rate for each efficacy? For each strain? I can change this structure based on that
reduce_days::Int64 = 0
waning::Int64 = 1
### after calibration, how much do we want to increase the contact rate... in this case, to reach 70%
### 0.5*0.95 = 0.475, so we want to multiply this by 1.473684211
hosp_red::Float64 = 3.1
##for testing
initial_day_week::Int64 = 1 # 1- Monday ... 7- Sunday
testing_days::Vector{Int64} = [1;4]
days_ex_test::Int64 = 30 ## 3 months without testing
isolation_days::Int64 = 5 #how many days of isolation after testing
test_ra::Int64 = 0 #1 - PCR, 2 - Abbott_PanBio 3 - BD VERITO 4 - SOFIA
scenariotest::Int64 = 0
size_threshold::Int64 = 100
extra_booster::Int64 = 0
start_testing::Int64 = 1
test_for::Int64 = 112
days_pcr::Int64 = 1
testing::Bool = false
asymp_red::Float16 = 0.6
fwork::Float64 = 1.0 ## proportion of participation of workplaces
#prop_working::Float64 = 0.65 #https://www.ontario.ca/document/ontario-employment-reports/april-june-2021#:~:text=Ontario's%20overall%20labour%20force%20participation,years%20and%20over%20at%2038.8%25.
end
Base.show(io::IO, ::MIME"text/plain", z::Human) = dump(z)
include("matrices_code.jl")
include("matrices.jl")
## constants
const humans = Array{Human}(undef, 0)
const p = ModelParameters() ## setup default parameters
const agebraks = @SVector [0:4, 5:19, 20:49, 50:64, 65:99]
#const agebraks_vac = @SVector [0:0,1:4,5:14,15:24,25:44,45:64,65:74,75:100]
const BETAS = Array{Float64, 1}(undef, 0) ## to hold betas (whether fixed or seasonal), array will get resized
const waning_factors = waning_factor()
const waning_factors_rec = waning_factor()
export ModelParameters, HEALTH, Human, humans, BETAS
function runsim(simnum, ip::ModelParameters)
# function runs the `main` function, and collects the data as dataframes.
hmatrix, hh1, nra, npcr, niso_t_p, niso_t_w,niso_f_p,niso_f_w, nleft = main(ip,simnum)
#Get the R0
R01 = length(findall(k -> k.sickby in hh1,humans))/length(hh1)
###use here to create the vector of comorbidity
# get simulation age groups
#ags = [x.ag for x in humans] # store a vector of the age group distribution
#ags = [x.ag_new for x in humans] # store a vector of the age group distribution
ags = map(x-> x.workplace_idx > 0 ? 1 : 2,humans)
all1 = _collectdf(hmatrix)
spl = _splitstate(hmatrix, ags)
work = _collectdf(spl[1])
age_groups = [0:14, 15:24, 25:34, 35:44, 45:54, 55:64, 65:999]
ags = map(x->findfirst(y-> x.age in y, age_groups),humans) # store a vector of the age group distribution
spl = _splitstate(hmatrix, ags)
ag1 = _collectdf(spl[1])
ag2 = _collectdf(spl[2])
ag3 = _collectdf(spl[3])
ag4 = _collectdf(spl[4])
ag5 = _collectdf(spl[5])
ag6 = _collectdf(spl[6])
ag7 = _collectdf(spl[7])
insertcols!(all1, 1, :sim => simnum); insertcols!(ag1, 1, :sim => simnum); insertcols!(ag2, 1, :sim => simnum);
insertcols!(ag3, 1, :sim => simnum); insertcols!(ag4, 1, :sim => simnum); insertcols!(ag5, 1, :sim => simnum);
insertcols!(ag6, 1, :sim => simnum); insertcols!(ag7, 1, :sim => simnum); insertcols!(work, 1, :sim => simnum);
pos = findall(y-> y in (11,22,33),hmatrix[:,end])
vector_ded::Vector{Int64} = zeros(Int64,100)
for i = pos
x = humans[i]
vector_ded[(x.age+1)] += 1
end
### total days of isolation per age group, both working and general
geniso_gr = map(y->findall(x-> x.age in y,humans),age_groups)
workiso_gr = map(y->findall(x-> x.age in y && x.workplace_idx > 0,humans),age_groups)
giso = map(y-> sum([ii.totaldaysiso for ii in humans[y]]),geniso_gr)
wiso = map(y-> sum([ii.totaldaysiso for ii in humans[y]]),workiso_gr)
return (a=all1, g1=ag1, g2=ag2, g3=ag3, g4=ag4, g5=ag5,g6=ag6,g7=ag7, work = work,
vector_dead=vector_ded,nra=nra,npcr=npcr, R0 = R01, niso_t_p=niso_t_p, niso_t_w=niso_t_w,
niso_f_p=niso_f_p,niso_f_w=niso_f_w, nleft=nleft,giso = giso, wiso = wiso)
end
export runsim
function main(ip::ModelParameters,sim::Int64)
Random.seed!(sim*726)
## datacollection
# matrix to collect model state for every time step
# reset the parameters for the simulation scenario
reset_params(ip) #logic: outside "ip" parameters are copied to internal "p" which is a global const and available everywhere.
p.popsize == 0 && error("no population size given")
hmatrix = zeros(Int16, p.popsize, p.modeltime)
initialize() # initialize population
vac_rate_1::Matrix{Int64} = vaccination_rate_1(sim)
vac_rate_2::Matrix{Int64} = vaccination_rate_2(sim)
vac_rate_booster::Vector{Int64} = booster_doses()
#h_init::Int64 = 0
# insert initial infected agents into the model
# and setup the right swap function.
herd_immu_dist_4(sim,1)
distribute_vaccine(vac_rate_1,vac_rate_2,vac_rate_booster,sim)
# split population in agegroups
grps = get_ag_dist()
workplaces = create_workplace()
#schools = create_schools()
initial_dw::Int64 = 0
nra::Vector{Int64} = zeros(Int64,p.modeltime)
npcr::Vector{Int64} = zeros(Int64,p.modeltime)
niso_t_w::Vector{Int64} = zeros(Int64,p.modeltime)
niso_f_w::Vector{Int64} = zeros(Int64,p.modeltime)
niso_t_p::Vector{Int64} = zeros(Int64,p.modeltime)
niso_f_p::Vector{Int64} = zeros(Int64,p.modeltime)
nleft::Vector{Int64} = zeros(Int64,p.modeltime)
testing_group::Vector{Int64} = select_testing_group(workplaces,sim)
insert_infected(LAT, p.initialinf, 4, p.strain)[1]
h_init1 = findall(x->x.health_status in (LAT,MILD,INF,PRE,ASYMP),humans)
## save the preisolation isolation parameters
#we need the workplaces to get the next days counts
for x in humans
get_nextday_counts(x)
end
# start the time loop
for st = 1:min((p.start_testing-1),p.modeltime)
initial_dw = st+(p.initial_day_week-1)-7*Int(floor((st-1+(p.initial_day_week-1))/7))
for x in humans
if x.iso && !(x.health_status in (HOS,ICU,DED))
x.totaldaysiso += 1
if x.isofalse
niso_f_p[st] += 1
if x.workplace_idx > 0
niso_f_w[st] += 1
end
else
niso_t_p[st] += 1
if x.workplace_idx > 0
niso_t_w[st] += 1
end
end
if x.isovia == :sev
if !x.tookpcr#x.daysisolation == 0
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr[st]+=1
x.pcrprob = _get_prob_test(x,1)
x.tookpcr = true
end
end
end
end
_get_model_state(st, hmatrix) ## this datacollection needs to be at the start of the for loop
dyntrans(st, grps,workplaces,initial_dw,sim)
sw = time_update() ###update the system
# end of day
end
setfield!(p,:testing,true)
# start the time loop
for st = p.start_testing:min((p.start_testing+p.test_for-1),p.modeltime)
initial_dw = st+(p.initial_day_week-1)-7*Int(floor((st-1+(p.initial_day_week-1))/7))
for x in humans
if x.iso && !(x.health_status in (HOS,ICU,DED))
x.totaldaysiso += 1
if x.isofalse
niso_f_p[st] += 1
if x.workplace_idx > 0
niso_f_w[st] += 1
end
else
niso_t_p[st] += 1
if x.workplace_idx > 0
niso_t_w[st] += 1
end
end
end
end
nra[st],npcr[st],nleft[st] = testing(initial_dw)
_get_model_state(st, hmatrix) ## this datacollection needs to be at the start of the for loop
dyntrans(st, grps,workplaces,initial_dw,sim)
sw = time_update() ###update the system
nra[st]+= sw[6]
# end of day
end
setfield!(p,:testing,false)
for st = (p.start_testing+p.test_for):p.modeltime
initial_dw = st+(p.initial_day_week-1)-7*Int(floor((st-1+(p.initial_day_week-1))/7))
for x in humans
if x.iso && !(x.health_status in (HOS,ICU,DED))
x.totaldaysiso += 1
if x.isofalse
niso_f_p[st] += 1
if x.workplace_idx > 0
niso_f_w[st] += 1
end
else
niso_t_p[st] += 1
if x.workplace_idx > 0
niso_t_w[st] += 1
end
end
if x.isovia == :sev
if !x.tookpcr#x.daysisolation == 0
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr[st]+=1
x.pcrprob = _get_prob_test(x,1)
x.tookpcr = true
end
end
end
end
_get_model_state(st, hmatrix) ## this datacollection needs to be at the start of the for loop
dyntrans(st, grps,workplaces,initial_dw,sim)
sw = time_update() ###update the system
# end of day
end
return hmatrix, h_init1, nra, npcr, niso_t_p, niso_t_w,niso_f_p,niso_f_w, nleft## return the model state as well as the age groups.
end
export main
##### creating workplaces
function work_size() #https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310039501
breaks = [1:4,5:9,10:19,20:49,50:99,100:199,200:499,500:1000]
#s = [748387, 233347, 152655, 99732, 32889, 14492, 7119, 2803]
#s/=sum(s)
# s = [0.5795052593106524,0.18068968828208243,0.11820672374061501,0.07722637956240554,0.025467236167207672,0.01122172113883589,0.00551251951334341,0.0021704722848576454]
aux = Distributions.Categorical(@SVector [0.5795052593106524,0.18068968828208243,0.11820672374061501,0.07722637956240554,0.025467236167207672,0.01122172113883589,0.00551251951334341,0.0021704722848576454])
return aux,breaks
end
### I can change it to split basic, mid, and high
function create_workplace()
#https://www.ontario.ca/document/ontario-employment-reports/april-june-2021#:~:text=Ontario's%20overall%20labour%20force%20participation,years%20and%20over%20at%2038.8%25.
groups = [18:24,25:54,55:65]
unemp = [0.204;0.069;0.077]
pos = map(y->findall(x-> x.age in y,humans),groups)
pos1 = map(y->sample(pos[y],Int(round(length(pos[y])*(1-unemp[y]))),replace = false),1:length(pos))
pos2 = vcat(pos1...)
N = length(pos2)
probs,breaks = work_size()
vaux = map(y-> rand(breaks[rand(probs)]),1:Int(round(p.popsize/10.0)))
vvaux = cumsum(vaux)
aux = findfirst(y-> y >= N, vvaux)
aux == nothing && error("increase the number of workplaces")
vaux = vaux[1:aux]
vvaux = vvaux[1:aux]
vaux[end] = N-vvaux[end-1]
samples::Vector{Vector{Int64}} = map(y-> [y],1:length(vaux))
agebrak_work = [18:24,25:29,30:34,35:39,40:44,45:49,50:54,55:59,60:65]
work_prop = [0.4910;0.5825;0.5378;0.4852;0.5039;0.5039;0.4930;0.4501;0.3183]
for i = 1:length(samples)
xx = sample(pos2,vaux[i],replace=false)
pos2 = setdiff(pos2,xx)
for j in xx
x = humans[j]
x.workplace_idx = i
aa = findfirst(y-> x.age in y,agebrak_work)
if aa != nothing && length(xx) > 1
x.proportion_contacts_workplace = work_prop[aa]
end
end
samples[i] = deepcopy(xx)
end
return samples
end
function select_testing_group(workplaces::Vector{Vector{Int64}},sim::Int64)
### we can change this function to implement different test strategies
rng = MersenneTwister(200*sim)
if p.scenariotest == 0
grp = findall(x-> x.age >= 5, humans)
grp_iso_sev = deepcopy(grp)
grp_iso_mild = []
elseif p.scenariotest == 1
grp = findall(x-> x.age >= 5, humans)
grp_iso_sev = deepcopy(grp)
grp_iso_mild = deepcopy(grp)#sample(rng,grp,Int(round(0.5*length(grp))),replace=false)
elseif p.scenariotest == 2
grp = findall(x-> x.age >= 5, humans)
grp_iso_sev = deepcopy(grp)
grp_iso_mild = deepcopy(grp)
elseif p.scenariotest == 3
wpr = findall(x-> length(x) >= p.size_threshold,workplaces)
wpr = sample(rng,wpr,Int(round(p.fwork*length(wpr))),replace=false)
grp = vcat(workplaces[wpr]...)
grp_iso_sev = findall(x-> x.age >= 5, humans)
grp_iso_mild = deepcopy(grp_iso_sev)
elseif p.scenariotest == 4
wpr = findall(x-> length(x) >= p.size_threshold,workplaces)
wpr = sample(rng,wpr,Int(round(p.fwork*length(wpr))),replace=false)
grp = vcat(workplaces[wpr]...)
grp_iso_sev = findall(x-> x.age >= 5, humans)
grp_iso_mild = deepcopy(grp_iso_sev)
elseif p.scenariotest == 5
wpr = findall(x-> length(x) >= p.size_threshold,workplaces)
wpr = sample(rng,wpr,Int(round(p.fwork*length(wpr))),replace=false)
grp = vcat(workplaces[wpr]...)
grp_iso_sev = findall(x-> x.age >= 5, humans)
grp_iso_mild = deepcopy(grp_iso_sev)
elseif p.scenariotest == 999
grp = []
grp_iso_sev = deepcopy(grp)
grp_iso_mild = deepcopy(grp)
else
error("error in testing group")
end
for i in grp
humans[i].test = true
end
for i in grp_iso_sev
humans[i].isolate_sev = true
end
for i in grp_iso_mild
humans[i].isolate_mild = true
end
return grp
end
function testing(dayweek)
npcr::Int64 = 0
nra::Int64 = 0
nleft::Int64 = 0
if p.scenariotest == 0
for x in humans
x.days_for_pcr -= 1
if x.isovia == :sev
if !x.tookpcr#x.daysisolation == 0
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr+=1
x.pcrprob = _get_prob_test(x,1)
x.tookpcr = true
end
end
end
elseif p.scenariotest == 1
for x in humans
x.days_for_pcr -= 1
if x.isovia == :sev
if !x.tookpcr#x.daysisolation == 0
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr+=1
x.pcrprob = _get_prob_test(x,1)
x.tookpcr = true
end
elseif x.isovia == :mild
if !x.tookpcr#x.daysisolation == 0
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr+=1
x.pcrprob = _get_prob_test(x,1)
x.tookpcr = true
elseif x.days_for_pcr == 0
if rand() > x.pcrprob
x.daysisolation = 999
x.tookpcr = false
x.days_after_detection = 999
nleft += 1
else
x.positive = true
end
end
end
end
elseif p.scenariotest == 2
for x in humans
x.days_for_pcr -= 1
if x.isovia == :sev
if !x.tookpcr#x.daysisolation == 0
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr+=1
x.pcrprob = _get_prob_test(x,1)
x.tookpcr = true
end
end
end
elseif p.scenariotest == 3
for x in humans
x.days_for_pcr -= 1
if x.isovia == :sev
if !x.tookpcr#x.daysisolation == 0
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr+=1
x.pcrprob = _get_prob_test(x,1)
x.tookpcr = true
end
elseif x.test && !x.iso
if dayweek in p.testing_days
if x.days_after_detection > p.days_ex_test
pp = _get_prob_test(x,p.test_ra)
if rand() < pp
x.positive = true
_set_isolation(x,true,:test)
x.isofalse = x.health_status in (SUS,REC) ? true : false
end
end
x.nra += 1
nra += 1
end
end
end
elseif p.scenariotest == 4
for x in humans
x.days_for_pcr -= 1
if x.isovia == :sev
if !x.tookpcr#x.daysisolation == 0
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr+=1
x.pcrprob = _get_prob_test(x,1)
x.tookpcr = true
end
elseif x.test
if !x.iso
if dayweek in p.testing_days
if x.days_after_detection > p.days_ex_test
pp = _get_prob_test(x,p.test_ra)
if rand() < pp
x.positive = true
_set_isolation(x,true,:test)
x.tookpcr = true
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr+=1
x.pcrprob = _get_prob_test(x,1)
x.isofalse = x.health_status in (SUS,REC) ? true : false
end
end
x.nra += 1
nra += 1
end
else
if x.days_for_pcr == 0
if rand() > x.pcrprob
x.daysisolation = 999
x.tookpcr = false
x.days_after_detection = 999
nleft += Int(!(x.health_status in (SUS,REC)))
else
x.positive = true
end
end
end
end
end
elseif p.scenariotest == 5
for x in humans
x.days_for_pcr -= 1
if x.isovia == :sev
if !x.tookpcr#x.daysisolation == 0
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr+=1
x.pcrprob = _get_prob_test(x,1)
x.tookpcr = true
end
elseif x.test
if !x.iso
if dayweek in p.testing_days
if x.days_after_detection > p.days_ex_test
pp = _get_prob_test(x,p.test_ra)
if rand() < pp
x.positive = true
_set_isolation(x,true,:test)
x.tookpcr = true
x.days_for_pcr = p.days_pcr#rand(1:2)
npcr+=1
x.pcrprob = _get_prob_test(x,1)
x.isofalse = x.health_status in (SUS,REC) ? true : false
end
end
x.nra += 1
nra += 1
end
else
if x.days_for_pcr == 0
pp = _get_prob_test(x,p.test_ra)
if rand() > pp
x.daysisolation = 999
x.tookpcr = false
x.days_after_detection = 999
nleft += Int(!(x.health_status in (SUS,REC)))
else
x.positive = true
end
end
end
end
end
elseif p.scenariotest == 999
else
error("no scenario")
end
return nra,npcr,nleft
end
function waning_immunity(x::Human)
index = Int(floor(x.days_vac/7))
if index > 0
if index <= size(waning_factors,1)
waning = [waning_factors[index,x.vaccine_n]^p.waning; waning_factors[index,x.vaccine_n+2]^p.waning]
else
waning = [waning_factors[end,x.vaccine_n]^p.waning; waning_factors[end,x.vaccine_n+2]^p.waning]
end
else
waning = [1.0;1.0]
end
return waning
end
function vac_update(x::Human)
if x.vac_status == 1
#x.index_day == 2 && error("saiu com indice 2")
if x.days_vac == p.days_to_protection[x.vaccine_n][x.vac_status][1]#14
x.protected = 1
x.index_day = min(length(p.days_to_protection[x.vaccine_n][x.vac_status]),x.index_day+1)
elseif x.days_vac == p.days_to_protection[x.vaccine_n][x.vac_status][x.index_day]#14
x.protected = x.index_day
x.index_day = min(length(p.days_to_protection[x.vaccine_n][x.vac_status]),x.index_day+1)
end
x.waning = waning_immunity(x)
x.days_vac += 1
elseif x.vac_status == 2
if x.days_vac == p.days_to_protection[x.vaccine_n][x.vac_status][1] && !x.boosted#0
x.protected = 1
x.index_day = min(length(p.days_to_protection[x.vaccine_n][x.vac_status]),x.index_day+1)
elseif x.days_vac == p.days_to_protection[x.vaccine_n][x.vac_status][x.index_day]#7
x.protected = x.index_day
x.index_day = min(length(p.days_to_protection[x.vaccine_n][x.vac_status]),x.index_day+1)
end
x.waning = waning_immunity(x)
x.days_vac += 1
end
end
function reset_params(ip::ModelParameters)
# the p is a global const
# the ip is an incoming different instance of parameters
# copy the values from ip to p.
for x in propertynames(p)
setfield!(p, x, getfield(ip, x))
end
# reset the contact tracing data collection structure
#= for x in propertynames(ct_data)
setfield!(ct_data, x, 0)
end
=# # resize and update the BETAS constant array
#init_betas()
# resize the human array to change population size
resize!(humans, p.popsize)
end
export reset_params, reset_params_default
## Data Collection/ Model State functions
function _get_model_state(st, hmatrix)
# collects the model state (i.e. agent status at time st)
for i=1:length(humans)
hmatrix[i, st] = Int(humans[i].health)
end
end
export _get_model_state
function _collectdf(hmatrix)
## takes the output of the humans x time matrix and processes it into a dataframe
#_names_inci = Symbol.(["lat_inc", "mild_inc", "miso_inc", "inf_inc", "iiso_inc", "hos_inc", "icu_inc", "rec_inc", "ded_inc"])
#_names_prev = Symbol.(["sus", "lat", "mild", "miso", "inf", "iiso", "hos", "icu", "rec", "ded"])
mdf_inc, mdf_prev = _get_incidence_and_prev(hmatrix)
mdf = hcat(mdf_inc, mdf_prev)
_names_inc = Symbol.(string.((Symbol.(instances(HEALTH)[1:end - 1])), "_INC"))
_names_prev = Symbol.(string.((Symbol.(instances(HEALTH)[1:end - 1])), "_PREV"))
_names = vcat(_names_inc..., _names_prev...)
datf = DataFrame(mdf, _names)
insertcols!(datf, 1, :time => 1:p.modeltime) ## add a time column to the resulting dataframe
return datf
end
function _splitstate(hmatrix, ags)
#split the full hmatrix into 4 age groups based on ags (the array of age group of each agent)
#sizes = [length(findall(x -> x == i, ags)) for i = 1:4]
matx = []#Array{Array{Int64, 2}, 1}(undef, 4)
for i = 1:maximum(ags)#length(agebraks)
idx = findall(x -> x == i, ags)
push!(matx, view(hmatrix, idx, :))
end
return matx
end
export _splitstate
function _get_incidence_and_prev(hmatrix)
cols = instances(HEALTH)[1:end - 1] ## don't care about the UNDEF health status
inc = zeros(Int64, p.modeltime, length(cols))
pre = zeros(Int64, p.modeltime, length(cols))
for i = 1:length(cols)
inc[:, i] = _get_column_incidence(hmatrix, cols[i])
pre[:, i] = _get_column_prevalence(hmatrix, cols[i])
end
return inc, pre
end
function _get_column_incidence(hmatrix, hcol)
inth = Int(hcol)
timevec = zeros(Int64, p.modeltime)
for r in eachrow(hmatrix)
idx = findall(x-> r[x] == inth && r[x] != r[x-1],2:length(r))
idx = idx .+ 1
#idx = findfirst(x -> x == inth, r)
if idx !== nothing
for i in idx
timevec[i] += 1
end
end
end
return timevec
end
function herd_immu_dist_4(sim::Int64,strain::Int64)
rng = MersenneTwister(200*sim)
vec_n = zeros(Int32,6)
N::Int64 = 0
if p.herd == 5
vec_n = [9; 148; 262; 68; 4; 9]
N = 5
elseif p.herd == 10
vec_n = [32; 279; 489; 143; 24; 33]
N = 9
elseif p.herd == 20
vec_n = [71; 531; 962; 302; 57; 77]
N = 14
elseif p.herd == 30
vec_n = [105; 757; 1448; 481; 87; 122]
N = 16
elseif p.herd == 50
vec_n = map(y->y*5,[32; 279; 489; 143; 24; 33])
N = 16
elseif p.herd == 0
vec_n = [0;0;0;0;0;0]
else
vec_n = map(y->Int(round(y*p.herd/10)),[32; 279; 489; 143; 24; 33])
N = 16
end
vprob::Vector{Float64} = vector_probs()
dprob = Distributions.Categorical(vprob)
for g = 1:6
pos = findall(y->y.ag_new == g && y.health == SUS,humans)
n_dist = min(length(pos),Int(floor(vec_n[g]*p.popsize/10000)))
pos2 = sample(rng,pos,n_dist,replace=false)
for i = pos2
humans[i].strain = strain
humans[i].swap = strain == 1 ? REC : REC2
humans[i].swap_status = REC
move_to_recovered(humans[i])
r = rand()
day = rand(dprob)
humans[i].days_recovered = day
humans[i].sickfrom = INF
humans[i].herd_im = true
end
end
return N
end
function _get_column_prevalence(hmatrix, hcol)
inth = Int(hcol)
timevec = zeros(Int64, p.modeltime)
for (i, c) in enumerate(eachcol(hmatrix))
idx = findall(x -> x == inth, c)
if idx !== nothing
ps = length(c[idx])
timevec[i] = ps
end
end
return timevec
end
export _collectdf, _get_incidence_and_prev, _get_column_incidence, _get_column_prevalence
## initialization functions
function get_province_ag(prov)
ret = @match prov begin
#= :alberta => Distributions.Categorical(@SVector [0.0655, 0.1851, 0.4331, 0.1933, 0.1230])
:bc => Distributions.Categorical(@SVector [0.0475, 0.1570, 0.3905, 0.2223, 0.1827])
:manitoba => Distributions.Categorical(@SVector [0.0634, 0.1918, 0.3899, 0.1993, 0.1556])
:newbruns => Distributions.Categorical(@SVector [0.0460, 0.1563, 0.3565, 0.2421, 0.1991])
:newfdland => Distributions.Categorical(@SVector [0.0430, 0.1526, 0.3642, 0.2458, 0.1944])
:nwterrito => Distributions.Categorical(@SVector [0.0747, 0.2026, 0.4511, 0.1946, 0.0770])
:novasco => Distributions.Categorical(@SVector [0.0455, 0.1549, 0.3601, 0.2405, 0.1990])
:nunavut => Distributions.Categorical(@SVector [0.1157, 0.2968, 0.4321, 0.1174, 0.0380])
:pei => Distributions.Categorical(@SVector [0.0490, 0.1702, 0.3540, 0.2329, 0.1939])
:quebec => Distributions.Categorical(@SVector [0.0545, 0.1615, 0.3782, 0.2227, 0.1831])
:saskat => Distributions.Categorical(@SVector [0.0666, 0.1914, 0.3871, 0.1997, 0.1552])
:yukon => Distributions.Categorical(@SVector [0.0597, 0.1694, 0.4179, 0.2343, 0.1187])
:newyorkcity => Distributions.Categorical(@SVector [0.064000, 0.163000, 0.448000, 0.181000, 0.144000])=#
:ontario => Distributions.Categorical(@SVector [0.04807822, 0.10498712, 0.12470340, 0.14498051, 0.13137129, 0.12679091, 0.13804896, 0.10292032, 0.05484776, 0.02327152])
:canada => Distributions.Categorical(@SVector [0.04922255,0.10812899,0.11792442,0.13956709,0.13534216,0.12589012,0.13876094,0.10687438,0.05550450,0.02278485])
_ => error("shame for not knowing your canadian provinces and territories")
end
return ret
end
export get_province_ag
function comorbidity(ag::Int16)
a = [4;19;49;64;79;999]
g = findfirst(x->x>=ag,a)
prob = [0.05; 0.1; 0.28; 0.55; 0.74; 0.81]
com = rand() < prob[g] ? 1 : 0
return com
end
export comorbidity
function initialize()
agedist = get_province_ag(p.prov)
agebraksnew = [0:4,5:14,15:24,25:34,35:44,45:54,55:64,65:74,75:84,85:99]
for i = 1:p.popsize
humans[i] = Human() ## create an empty human
x = humans[i]
x.idx = i
agn = rand(agedist)
x.age = rand(agebraksnew[agn])
x.ag = findfirst(y-> x.age in y, agebraks)
a = [4;19;49;64;79;999]
g = findfirst(y->y>=x.age,a)
x.ag_new = g
x.exp = 999 ## susceptible people don't expire.
#x.dur = sample_epi_durations() # sample epi periods
x.comorbidity = comorbidity(x.age)
# initialize the next day counts (this is important in initialization since dyntrans runs first)
end
end
export initialize
function get_ag_dist()
# splits the initialized human pop into its age groups
grps = map(x -> findall(y -> y.ag == x, humans), 1:length(agebraks))
return grps
end
function insert_infected(health, num, ag,strain)
## inserts a number of infected people in the population randomly
## this function should resemble move_to_inf()
l = findall(x -> x.health == SUS && x.ag == ag, humans)
aux_pre = [PRE;PRE2;PRE3]
aux_lat = [LAT;LAT2;LAT3]
aux_mild = [MILD;MILD2;MILD3]
aux_inf = [INF;INF2;INF3]
aux_asymp = [ASYMP;ASYMP2;ASYMP3]
aux_rec = [REC;REC2;REC3]
if length(l) > 0 && num < length(l)
h = sample(l, num; replace = false)
@inbounds for i in h
x = humans[i]
x.strain = strain
x.dur = sample_epi_durations(x)
if x.strain > 0
if health == PRE
x.swap = aux_pre[x.strain]
x.swap_status = PRE
x.daysinf = x.dur[1]+1
x.wentto = 1
move_to_pre(x) ## the swap may be asymp, mild, or severe, but we can force severe in the time_update function
elseif health == LAT
x.swap = aux_lat[x.strain]
x.swap_status = LAT
x.daysinf = rand(1:x.dur[1])
move_to_latent(x)
elseif health == MILD
x.swap = aux_mild[x.strain]