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Dynamic_update.jl
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Dynamic_update.jl
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include("Initial_data.jl")
function update!(dy::Dynamic, t::Int16)
dim = size(dy.quant)
previous_quant = deepcopy(dy.quant[:,:,:,:,:,:,:,:,:,t])
contagion::Float64 = 0
#1. Contagion Loop
@time begin
for demo1 in CartesianIndices(dim[1:3]),
infection in CartesianIndices(dim[7:9])
# infection = (infection_known, infection_test, infection_symptoms)
"""
alterar - desmarcar!
dy.quant[demo1, 1, 3, 1, infection, t + 1] += (
dy.social_contagion[8] * dy.infectors_hospital[t] *
previous_quant[demo1, 1, 2, 1, infection] ) #+value
dy.quant[demo1, 1, 2, 1, infection, t + 1] -= (
dy.social_contagion[8] * dy.infectors_hospital[t] *
previous_quant[demo1, 1, 2, 1, infection] ) #-value
"""
for demo2 in CartesianIndices(dim[1:3]),
infection_kown in 1:dim[7],
infection_symptom in 1:dim[9],
social_contagion in 1:1
#alterar social_contagion size(dy.social_contagion)[1] - 1
if social_contagion != 1 || demo1[3] == demo2[3]
contagion = (
dy.social_dynamic_correction[
demo1[1],
demo1[2],
infection[1],
infection[3],
dy.social_isolation[
demo1[1],
demo1[2],
infection[1],
infection[3],
t],
social_contagion ]*
dy.static_correction[demo1[3], social_contagion]*
dy.social_contagion[social_contagion]*
dy.static_correction[demo2[3], social_contagion]*
dy.social_dynamic_correction[
demo2[1],
demo2[2],
infection_kown,
infection_symptom,
dy.social_isolation[
demo2[1],
demo2[2],
infection_kown,
infection_symptom,
t],
social_contagion] )
"""
Primeiramente, usamos o fator de contagio Fatores(i, t) para a
situacao de contato i. Depois multiplicamos pelas correcoes es-
taticas, de acordo com caracteristicas intrinsecas do grupo analisa-
do. Essa correcao deve afetar os dois grupos, tanto de susceti-
veis quanto de infectados. Portanto, o fator é igual ao fator de
base multiplicado pelas correcoes dos dois grupos. Ainda aqui
esta incluida a correcao decorrente da medida governamental p_1.
Novamente, a correcao acontece nos dois grupos.
"""
for restriction = 1:size(dy.general_dynamic_correction)[3]
"""
Ainda existem as correcoes de acordo com as medidas p_2 a p_8.Se
elas fo-rem medidas desacopladas (isto e, uma nao influencia
na outra), podemos modelar a aplicacao de duasdelas pelo
produto. Como antes, as medidas afetam suscetivel e infecta-
do.
"""
contagion = dy.general_dynamic_correction[
infection[1],
infection[3],
restriction,
dy.general_isolation[
infection[1],
infection[3],
restriction,
t ],
social_contagion ]*
contagion*
dy.general_dynamic_correction[
infection_kown,
infection_symptom,
restriction,
dy.general_isolation[
infection_kown,
infection_symptom,
restriction,
t ],
social_contagion ]
dy.quant[demo1, 1, 3, 1, infection, t+1 ] += (
contagion*
dy.infectors_out_hosp[
demo2,
infection_kown,
infection_symptom,
t ]*
previous_quant[ demo1, 1, 2, 1, infection] )
dy.quant[demo1, 1, 2, 1, infection, t+1 ] -= (
contagion *
dy.infectors_out_hosp[
demo2,
infection_kown,
infection_symptom,
t ] *
previous_quant[demo1, 1, 2, 1, infection] )
end
end
end
end
end
#2. Gravity Loop
for age_range in 1:dim[1],
group_risk in 1:dim[2],
disease_severity in dim[3]:-1:1
dy.quant[
age_range,
group_risk,
:,
disease_severity,
3,
:,
:,
:,
:,
t + 1] = (
dy.gravity[age_range, group_risk, disease_severity]*
dy.quant[
age_range,
1 ,
:,
disease_severity,
3,
:,
:,
:,
:,
t + 1] )
end
#3. Loop para atualizar estados e testes
quant_temp = Array{Float64}(undef, size(dy.quant)[1:end-1])
previous_quant_temp = Array{Float64}(undef, size(dy.quant)[1:end-1])
for demo in CartesianIndices(dim[1:3]),
infection_symptom in 1:dim[9]
#infection_known = 1 means unknown state... after test expiration...
#Why not only sum with dy.quant[args..]?
#Here, move from infection_known = 2 to infection_known = 1
previous_quant_temp[demo, :, 2, :, 1, 1, infection_symptom] = (
previous_quant[demo, :, 2, :, 2, dim[8], infection_symptom]
)
previous_quant[demo, :, 2, :, 2, dim[8], infection_symptom] .= 0
"""
bloco 1
QuantidadeAnteriorTemp(d11, d21, d31, :, 2, :, 1, 1, e51) =
QuantidadeAnterior(d11, d21, d31, :, 2, :, 2, e41, e51);
QuantidadeAnterior(d11, d21, d31, :, 2, :, 2, e41, e51) = 0;
"""
#Bloco 2
previous_quant_temp[demo, :, 2, :, 3, 1, infection_symptom] = (
previous_quant[demo, :, 2, :, 3, dim[8], infection_symptom]
)
previous_quant[demo, :, 2, :, 3, dim[8], infection_symptom] .= 0
"""
bloco 2
QuantidadeAnteriorTemp(d11, d21, d31, :, 2, :, 3, 1, e51) =
QuantidadeAnterior(d11, d21, d31, :, 2, :, 3, e41, e51);
QuantidadeAnterior(d11, d21, d31, :, 2, :, 3, e41, e51) = 0;
"""
#Bloco 3
previous_quant_temp[
demo, :, 3:dy.max_infected_days+ 3 , :, 2, 1, infection_symptom] = (
previous_quant[
demo,
:,
3:dy.max_infected_days + 3 ,
:,
2,
dim[8],
infection_symptom]
)
previous_quant[
demo,
:,
3:dy.max_infected_days + 3,
:,
2,
dim[8],
infection_symptom] .= 0
"""
bloco 3
QuantidadeAnteriorTemp(d11, d21, d31, :,
3:NumeroDeDiasInfectado+3, :, 2, 1, e51)
=
QuantidadeAnterior(d11, d21, d31, :,
3:NumeroDeDiasInfectado+3, :, 2, e41, e51)
QuantidadeAnterior(d11, d21, d31, :,
3:NumeroDeDiasInfectado+3, :, 2, e41, e51) = 0;
"""
previous_quant_temp[
demo, :, 3:dy.max_infected_days+3, :, 1, 1, infection_symptom] = (
previous_quant[
demo, :, 3:dy.max_infected_days+3, :, 3, dim[8], infection_symptom])
previous_quant[
demo, :, 3:dy.max_infected_days+3, :, 3, 1, infection_symptom] .= 0
"""
bloco 4
QuantidadeAnteriorTemp(d11, d21, d31, :,
3:NumeroDeDiasInfectado+3, :, 1, 1, e51)
=
QuantidadeAnterior(d11, d21, d31, :,
3:NumeroDeDiasInfectado+3, :, 3, e41, e51)
QuantidadeAnterior(d11, d21, d31, :,
3:NumeroDeDiasInfectado+3, :, 3, e41, e51) = 0;
"""
quant_temp[demo, :, 3, :, 1, 1, infection_symptom] = (
dy.quant[demo, :, 3, :, 2, dim[8], infection_symptom, t + 1] )
dy.quant[demo, :, 3, :, 2, dim[8], infection_symptom, t + 1] .= 0
"""
bloco 5
QuantidadesTemp(d11, d21, d31, :, 3, :, 1, 1, e51)
= Quantidades(d11, d21, d31, :, 3, :, 2, e41, e51, t + 1);
Quantidades(d11, d21, d31, :, 3, :, 2, e41, e51, t + 1) = 0;
"""
quant_temp[demo, :, 3, :, 2, 1, infection_symptom] = (
dy.quant[demo, :, 3, :, 3, dim[8], infection_symptom, t+1] )
dy.quant[demo, :, 3, :, 3, dim[8], infection_symptom, t+1] .= 0
"""
bloco 6
QuantidadesTemp(d11, d21, d31, :, 3, :, 2, 1, e51) =
Quantidades(d11, d21, d31, :, 3, :, 3, e41, e51, t + 1);
Quantidades(d11, d21, d31, :, 3, :, 3, e41, e51, t + 1) = 0;
"""
for infection_test in dim[8] - 1:-1:2
previous_quant[
demo,
:,
2:dy.max_infected_days+3,
:,
2:3,
infection_test + 1,
infection_symptom] =
previous_quant[
demo,
:,
2:dy.max_infected_days+3,
:,
2:3,
infection_test,
infection_symptom]
previous_quant[
demo,
:,
2:dy.max_infected_days+3,
:,
2:3,
infection_test,
infection_symptom] .= 0
#Alocação desnecessária? Sim, no próximo loop será redefinido
#Só não é verdade para infection_test = 1
dy.quant[demo,:,3,:,3,infection_test + 1,infection_symptom, t+1] = (
dy.quant[
demo,:,3,:,2,infection_test, infection_symptom, t+1] )
dy.quant[demo,:,3,:,2,infection_test, infection_symptom, t+1] .= 0
#Alocação desnecessária? Sim, a alocação abaixo no próxima iteração
#garante... exceto para infection_test = 2
dy.quant[demo,:,3,:,2,infection_test + 1,infection_symptom, t+1] = (
dy.quant[
demo,:,3,:,3,infection_test, infection_symptom, t+1] )
dy.quant[demo,:,3,:,3,infection_test,infection_symptom,t + 1] .= 0
#Alocação desnecessária? Sim, exceto para infection_test = 2, tal
#alocação será feita na próxima iteração pela linha 275
end
"""
Para alocações que fo-ram presumidas desnecessárias:
previous_quant[
demo,
:,
2:dy.max_infected_days+3,
:,
2:3,
2,
infection_symptom] .= 0
dy.quant[demo, :, 3, :, 2:3, 2, infection_symptom, t + 1] .= 0
"""
tests_available = dy.tests[demo[1], demo[2], infection_symptom, t]
for hospital_use in 1:dim[6]
if tests_available != 0
this_state_total = sum(
dy.quant[demo, :, 2:dy.max_infected_days+3, hospital_use,
1, 1, infection_symptom, t+1] +
previous_quant[demo,:,2:dy.max_infected_days+3,hospital_use,
1, 1, infection_symptom]
)
test_to_apply = min(
minimum(tests_available),
this_state_total)
if this_state_total != 0
value = (
test_to_apply/this_state_total*
dy.quant[
demo,
:,
3,
hospital_use,
1,
1,
infection_symptom,
t + 1] )
dy.quant[
demo,
:,
3,
hospital_use,
3,
2,
infection_symptom,
t+1] += value
dy.quant[
demo,
:,
3,
hospital_use,
1,
1,
infection_symptom,
t+1] -= value
value = (
test_to_apply/this_state_total*
previous_quant[
demo,
:,
2:dy.max_infected_days+3,
hospital_use,
1,
1,
infection_symptom
] )
previous_quant[
demo,
:,
2:dy.max_infected_days+3,
hospital_use,
2,
2,
infection_symptom
] += value
previous_quant[
demo,
:,
2:dy.max_infected_days+3,
hospital_use,
2,
2,
infection_symptom
] -= value
tests_available -= test_to_apply
end
end
end
dy.test_spare[demo[1], demo[2], infection_symptom, t] = tests_available
dy.quant[demo, :, :, :, :, :, infection_symptom, t + 1] += (
quant_temp[demo, :, :, :, :, :, infection_symptom] )
previous_quant[demo, :, :, :, :, :, infection_symptom] += (
previous_quant_temp[demo, :, :, :, :, :, infection_symptom] )
end
#4. Loop para atualizar o ciclo de falso sintomas (e_5 na referência)
#Os dados continuarao guardados em dy.quant[:, :, :, :, :, :, :, :, :, t +1]
# previous_quant[:, :, :, :, :, :, :, :, :]
previous_quant_temp[:, :, :, :, 2:dy.max_infected_days + 3, :, :, :, 1] = (
previous_quant[:, :, :, :, 2:dy.max_infected_days + 3, :, :, :, dim[9]]
)
previous_quant[:, :, :, :, 2:dy.max_infected_days + 3, :, :, :, dim[9]] .= 0
#desnecessário?
quant_temp[:, :, :, :, 3, :, :, :, 1] = (
dy.quant[:, :, :, :, 3, :, :, :, dim[9], t+ 1] )
dy.quant[:, :, :, :, 3, :, :, :, dim[9], t+ 1] .= 0
#desnecessário?
for infection_symptom in dim[9] - 1:-1:2
previous_quant[
:,
:,
:,
:,
2:dy.max_infected_days+3,
:,
:,
:,
infection_symptom + 1] = (
previous_quant[
:,
:,
:,
:,
2:dy.max_infected_days+3,
:,
:,
:,
infection_symptom]
)
previous_quant[
:,
:,
:,
:,
2:dy.max_infected_days+3,
:,
:,
:,
infection_symptom] .= 0 #desnecessário?
dy.quant[
:,
:,
:,
:,
2:dy.max_infected_days+3,
:,
:,
:,
infection_symptom + 1,
t + 1 ] = (
dy.quant[
:,
:,
:,
:,
2:dy.max_infected_days+3,
:,
:,
:,
infection_symptom,
t + 1]
)
dy.quant[
:,
:,
:,
:,
2:dy.max_infected_days+3,
:,
:,
:,
infection_symptom,
t + 1] .= 0 # desnecessário
end
"""
Após remover as alocações desnecessárias, fazer:
previous_quant[
:,
:,
:,
:,
2:dy.max_infected_days+3,
:,
:,
:,
2] .= 0
dy.quant[
:,
:,
:,
:,
2:dy.max_infected_days+3,
:,
:,
:,
2,
t + 1] .= 0
"""
value = dy.false_symptom_ratio/dy.false_symptoms_days*
dy.quant[:, :, :, :, 3, :, :, :, 1, t+1]
dy.quant[:, :, :, :, 3, :, :, :, 1, t+1] -= value
dy.quant[:, :, :, :, 3, :, :, :, 2, t+1] = value
value = dy.false_symptom_ratio/dy.false_symptoms_days*
previous_quant[:, :, :, :, 2:dy.max_infected_days + 3, :, :, :, 1]
previous_quant[:, :, :, :, 2:dy.max_infected_days + 3, :, :, :, 1] -= value
previous_quant[:, :, :, :, 2:dy.max_infected_days + 3, :, :, :, 2] = value
dy.quant[:,:,:,:,3,:,:,:,1,t+1] += quant_temp[:,:,:,:,3,:,:,:,1]
previous_quant[:,:,:,:,2:dy.max_infected_days+3, :,:,:,1] +=
previous_quant_temp[:,:,:,:,2:dy.max_infected_days+3, :,:,:,1]
#5. Loop para transição de estados
for health_condition in dy.max_infected_days + 2: -1: 3,
disease_severity in dim[4]
if dy.max_day_disease_severity[disease_severity] == health_condition -2
if disease_severity == dim[4]
previous_quant[
:,
:,
:,
disease_severity,
dy.max_infected_days + 4,
:,
:,
:,
:] += previous_quant[
:,
:,
:,
disease_severity,
health_condition,
:,
:,
:,
:]
else
previous_quant[
:,
:,
:,
disease_severity,
dy.max_infected_days + 3,
:,
:,
:,
:] += previous_quant[
:,
:,
:,
disease_severity,
health_condition,
:,
:,
:,
: ]
end
previous_quant[
:,
:,
:,
disease_severity,
health_condition,
:,
:,
:,
:] .= 0
end
if dy.max_day_disease_severity[disease_severity] > health_condition - 2
hospital_use_new = 1
hospital_use_old = 1
if health_condition - 1 == dy.cycles[disease_severity, 3, 1]
hospital_use_new = 2
end
if health_condition - 2 >= dy.cycles[disease_severity, 3, 1] &
health_condition - 2 < dy.cycles[disease_severity, 3, 2]
hospital_use_new = 2
hospital_use_old = 2
end
if health_condition - 2 == dy.cycles[disease_severity, 3, 2]
hospital_use_new = 3
hospital_use_old = 2
end
if health_condition - 2 >= dy.cycles[disease_severity, 4, 1] &
health_condition - 2 < dy.cycles[disease_severity, 4, 2]
hospital_use_new = 3
hospital_use_old = 3
end
if health_condition - 2 == dy.cycles[disease_severity, 4, 2]
hospital_use_new = 4
hospital_use_old = 3
end
if health_condition - 2 >= dy.cycles[disease_severity, 5, 1] &
health_condition - 2 < dy.cycles[disease_severity, 5, 2]
hospital_use_new = 4
hospital_use_old = 4
end
previous_quant[
:,
:,
:,
disease_severity,
health_condition + 1,
hospital_use_new,
:,
:,
: ] += previous_quant[
:,
:,
:,
disease_severity,
hospital_use_old,
:,
:,
: ]
previous_quant[
:,
:,
:,
disease_severity,
hospital_use_old,
:,
:,
: ] .= 0
end
end
#6. Loop to update the status of the deads due to lake of hospital resources
for hospital_use in 2:dim[6]
dy.hospital_need[hospital_use - 1] = (
sum(previous_quant[
:,
:,
:,
:,
3:dy.max_infected_days + 3,
hospital_use,
:,
:,
: ]
)
)
end
for i in length(dy.hospital_available):-1:1
dy.hospital_available[i] = max(
dy.effective_hospital[i] - dy.hospital_need[i], 0
)
availability = sum(
dy.hospital_available[i:length(dy.hospital_available)]
)
deficit = max(dy.hospital_need[i] - availability, 0)
for cycle_day in 1:dy.max_infected_days
this_day_total = 0
for disease_severity in 3:dim[4]
if dy.cycles[disease_severity, i + 2, 1] != -1 &
dy.cycles[disease_severity, i + 2, 1] + cycle_day - 1 <=
dy.cycles[disease_severity, i + 2, 2]
this_day_total += sum(
previous_quant[
:,
:,
:,
disease_severity,
2 + dy.cycles[disease_severity,i+2,1] + cycle_day-1,
i + 1,
:,
:,
:]
)
end
end
today_death = min(this_day_total, deficit)
if this_day_total != 0
for disease_severity in 3:dim[4]
if dy.cycles[disease_severity, i+2, 1] != -1 &
dy.cycles[disease_severity, i+2, 1] + cycle_day - 1 <=
dy.cycles[disease_severity, i+2, 2]
value = today_death/this_day_total*previous_quant[
:,
:,
:,
disease_severity,
2+dy.cycles[disease_severity,i+2,1]+cycle_day-1,
i + 1,
:,
:,
:]
previous_quant[
:,
:,
:,
disease_severity,
dy.max_infected_days + 5,
1,
:,
:,
:] += value
previous_quant[
:,
:,
:,
disease_severity,
2 + dy.cycles[disease_severity,i+2,1] + cycle_day-1,
i + 1,
:,
:,
:] -= value
end
end
end
deficit -= today_death
end
end
dy.quant[:, :, :, :, :, :, :, :, :, t + 1] += previous_quant
for demo in CartesianIndices(dim[1:3]),
disease_severity in 1:dim[4],
health_condition in 1:dim[5],
state in CartesianIndices(dim[6:9])
#state:
#1 = hospital_use, 2 = infectown_known, 3 = test, 4 = infection_symptom
if (health_condition - 2 >= dy.cycles[disease_severity, 1 , 1]) &
(health_condition - 2 <= dy.cycles[disease_severity, 1 , 2])
if state[1] == 1
dy.infectors_out_hosp[demo, state[2], state[3], t+1] +=
dy.quant[demo,disease_severity,health_condition,state,t+1]
else
dy.infectors_hospital[t+1] +=
dy.quant[demo,disease_severity,health_condition,state,t+1]
end
end
end
end