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Peter Jentsch
CovidAlertABM
Commits
44426b76
Commit
44426b76
authored
May 03, 2021
by
Peter Jentsch
Browse files
infection growth rate fitting
parent
3c995c3e
Changes
11
Hide whitespace changes
Inline
Side-by-side
CovidAlertVaccinationModel/Manifest.toml
View file @
44426b76
...
...
@@ -92,9 +92,9 @@ version = "1.16.0+6"
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git-tree-sha1
=
"1958272568dc176a1d881acb797beb909c785510"
uuid
=
"82ae8749-77ed-4fe6-ae5f-f523153014b0"
version
=
"1.0.0"
[[StatsBase]]
deps
=
[
"DataAPI"
,
"DataStructures"
,
"LinearAlgebra"
,
"Missings"
,
"Printf"
,
"Random"
,
"SortingAlgorithms"
,
"SparseArrays"
,
"Statistics"
]
git-tree-sha1
=
"
4d8ca45223d7a28839e775d73a6f6b6b2ac64fd1
"
deps
=
[
"DataAPI"
,
"DataStructures"
,
"LinearAlgebra"
,
"Missings"
,
"Printf"
,
"Random"
,
"SortingAlgorithms"
,
"SparseArrays"
,
"Statistics"
,
"StatsAPI"
]
git-tree-sha1
=
"
2f6792d523d7448bbe2fec99eca9218f06cc746d
"
uuid
=
"2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
version
=
"0.33.
6
"
version
=
"0.33.
8
"
[[StatsFuns]]
deps
=
[
"LogExpFunctions"
,
"Rmath"
,
"SpecialFunctions"
]
...
...
@@ -1092,9 +1190,9 @@ version = "0.1.7"
[[TimeZones]]
deps
=
[
"Dates"
,
"EzXML"
,
"Mocking"
,
"Pkg"
,
"Printf"
,
"RecipesBase"
,
"Serialization"
,
"Unicode"
]
git-tree-sha1
=
"
4ba8a9579a243400db412b50300cd61d7447e583
"
git-tree-sha1
=
"
3f6f0be07f33e33bd986a58b4cf2d6c9fd2b7f18
"
uuid
=
"f269a46b-ccf7-5d73-abea-4c690281aa53"
version
=
"1.5.
3
"
version
=
"1.5.
4
"
[[Transducers]]
deps
=
[
"Adapt"
,
"ArgCheck"
,
"BangBang"
,
"Baselet"
,
"CompositionsBase"
,
"DefineSingletons"
,
"Distributed"
,
"InitialValues"
,
"Logging"
,
"Markdown"
,
"MicroCollections"
,
"Requires"
,
"Setfield"
,
"SplittablesBase"
,
"Tables"
]
...
...
@@ -1103,9 +1201,9 @@ uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999"
version
=
"0.4.64"
[[URIs]]
git-tree-sha1
=
"
7855809b88d7b16e9b029afd17880930626f54a2
"
git-tree-sha1
=
"
97bbe755a53fe859669cd907f2d96aee8d2c1355
"
uuid
=
"5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version
=
"1.
2
.0"
version
=
"1.
3
.0"
[[UUIDs]]
deps
=
[
"Random"
,
"SHA"
]
...
...
@@ -1121,9 +1219,9 @@ uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
[[VectorizationBase]]
deps
=
[
"ArrayInterface"
,
"Hwloc"
,
"IfElse"
,
"Libdl"
,
"LinearAlgebra"
,
"Static"
]
git-tree-sha1
=
"
5d66756d2d5c538e9ab0c39efcb4512e3a8c336
1"
git-tree-sha1
=
"
293aa2c5cbf201e6b98810cb36d9eeafdafdafd
1"
uuid
=
"3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
version
=
"0.19.3
1
"
version
=
"0.19.3
4
"
[[VersionParsing]]
git-tree-sha1
=
"80229be1f670524750d905f8fc8148e5a8c4537f"
...
...
CovidAlertVaccinationModel/Project.toml
View file @
44426b76
...
...
@@ -6,13 +6,17 @@ version = "0.1.0"
[deps]
BenchmarkTools
=
"6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
CSV
=
"336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
CurveFit
=
"5a033b19-8c74-5913-a970-47c3779ef25c"
DataFrames
=
"a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataStructures
=
"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Dates
=
"ade2ca70-3891-5945-98fb-dc099432e06a"
DelimitedFiles
=
"8bb1440f-4735-579b-a4ab-409b98df4dab"
Dictionaries
=
"85a47980-9c8c-11e8-2b9f-f7ca1fa99fb4"
Distributions
=
"31c24e10-a181-5473-b8eb-7969acd0382f"
FiniteDiff
=
"6a86dc24-6348-571c-b903-95158fe2bd41"
FiniteDifferences
=
"26cc04aa-876d-5657-8c51-4c34ba976000"
ImportAll
=
"c65182e5-40f4-518f-8165-175b85689199"
Interpolations
=
"a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
Intervals
=
"d8418881-c3e1-53bb-8760-2df7ec849ed5"
KernelDensity
=
"5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
KissABC
=
"9c9dad79-530a-4643-a18b-2704674d4108"
...
...
@@ -20,7 +24,9 @@ LaTeXStrings = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
LabelledArrays
=
"2ee39098-c373-598a-b85f-a56591580800"
LightGraphs
=
"093fc24a-ae57-5d10-9952-331d41423f4d"
LinearAlgebra
=
"37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Loess
=
"4345ca2d-374a-55d4-8d30-97f9976e7612"
LoopVectorization
=
"bdcacae8-1622-11e9-2a5c-532679323890"
LsqFit
=
"2fda8390-95c7-5789-9bda-21331edee243"
NamedTupleTools
=
"d9ec5142-1e00-5aa0-9d6a-321866360f50"
NetworkLayout
=
"46757867-2c16-5918-afeb-47bfcb05e46a"
OnlineStats
=
"a15396b6-48d5-5d58-9928-6d29437db91e"
...
...
CovidAlertVaccinationModel/abm_fit_parameters.jl
View file @
44426b76
using
CovidAlertVaccinationModel
using
OnlineStats
using
Plots
const
samples
=
1
const
parameters
=
(
sim_length
=
5
0
,
sim_length
=
3
0
,
num_households
=
5000
,
I_0_fraction
=
0.00
2
,
base_transmission_probability
=
0.001
5
,
I_0_fraction
=
0.00
5
,
base_transmission_probability
=
0.001
,
recovery_rate
=
1
/
7
,
immunization_loss_prob
=
0.0055
,
#mean time of 6 months
π_base
=
-
0.2
,
κ
=
0.001
,
ω
=
0.0000
,
ρ
=
[
0.0
,
0.0
,
0.0
],
π_base
=
-
4.0
,
η
=
0.0
,
κ
=
1.0
,
ω
=
0.001
,
ρ
=
[
0.0
,
0.0
,
0.0
],
ω_en
=
0.00
,
ρ_en
=
[
0.0
,
0.0
,
0.0
],
γ
=
0.0
,
β
=
100
.0
,
notification_parameter
=
0.00
0
,
β
=
5
.0
,
notification_parameter
=
0.00
1
,
vaccinator_prob
=
0.2
,
app_user_fraction
=
0.4
,
notification_threshold
=
2
,
infection_introduction_delay
=
50
immunizing
=
true
,
immunizing
=
false
,
immunization_delay
=
14
,
immunization_begin_delay
=
0
,
infection_introduction_delay
=
1
,
)
function
solve_and_plot_parameters
()
out
=
mean_solve
(
samples
,
parameters
,
DebugRecorder
)
p
=
plot_model
(
nothing
,[
nothing
],[
out
])
savefig
(
p
,
"timeseries.pdf"
)
end
solve_and_plot_parameters
()
\ No newline at end of file
seasonal_transmission_dist
=
CovidAlertVaccinationModel
.
fit_epi_parameters
(
parameters
,
0.073
)
##seasonal
# outbreak_transmission_dist = CovidAlertVaccinationModel.fit_epi_parameters(parameters,0.241) ##outbreak
\ No newline at end of file
CovidAlertVaccinationModel/abm_timeseries.jl
View file @
44426b76
using
CovidAlertVaccinationModel
using
OnlineStats
using
Plots
const
samples
=
5
const
samples
=
2
const
parameters
=
(
sim_length
=
600
,
num_households
=
5000
,
...
...
CovidAlertVaccinationModel/src/ABM/model_setup.jl
View file @
44426b76
...
...
@@ -32,7 +32,22 @@ function get_u_0(nodes,I_0_fraction,vaccinator_prob)
return
status
,
is_vaccinator
end
struct
ModelSolution
{
T
,
InfNet
,
SocNet
,
WSMixingDist
,
RestMixingDist
}
function
app_users
(
demographics
,
app_usage_prob
)
ymo_usage
=
[
0.979
,
0.921
,
0.604
,
]
is_app_user
=
Vector
{
Bool
}(
undef
,
length
(
demographics
))
@inbounds
for
i
in
eachindex
(
demographics
)
demo
=
demographics
[
i
]
is_app_user
[
i
]
=
rand
(
RNG
)
<
app_usage_prob
*
ymo_usage
[
Int
(
demo
)]
end
return
is_app_user
end
mutable struct
ModelSolution
{
T
,
InfNet
,
SocNet
,
WSMixingDist
,
RestMixingDist
}
sim_length
::
Int
nodes
::
Int
params
::
T
...
...
@@ -50,7 +65,8 @@ struct ModelSolution{T,InfNet,SocNet,WSMixingDist,RestMixingDist}
app_user_list
::
Vector
{
Int
}
app_user_index
::
Vector
{
Int
}
status_totals
::
Vector
{
Int
}
status_totals_next
::
Vector
{
Int
}
daily_vaccinators
::
Int
daily_infected
::
Int
ws_matrix_tuple
::
WSMixingDist
rest_matrix_tuple
::
RestMixingDist
immunization_countdown
::
Vector
{
Int
}
...
...
@@ -63,8 +79,8 @@ struct ModelSolution{T,InfNet,SocNet,WSMixingDist,RestMixingDist}
ws_matrix_tuple
=
map_symmetrize
(
workschool_mixing
)
rest_matrix_tuple
=
map_symmetrize
(
rest_mixing
)
is_app_user
=
app_users
(
demographics
,
params
.
app_user_fraction
)
app_user_list
=
zeros
(
nodes
)
is_app_user
=
rand
(
RNG
,
nodes
)
.<
params
.
app_user_fraction
app_user_index
=
findall
(
==
(
true
),
is_app_user
)
app_user_list
[
is_app_user
]
.=
collect
(
1
:
length
(
app_user_index
))
...
...
@@ -95,7 +111,8 @@ struct ModelSolution{T,InfNet,SocNet,WSMixingDist,RestMixingDist}
app_user_list
,
app_user_index
,
status_totals
,
copy
(
status_totals
),
0
,
0
,
ws_matrix_tuple
,
rest_matrix_tuple
,
immunization_countdown
...
...
CovidAlertVaccinationModel/src/ABM/output.jl
View file @
44426b76
...
...
@@ -18,6 +18,7 @@ DebugRecorder should store everything we might want to know about the model outp
"""
struct
DebugRecorder
{
ElType
,
ArrType1
<:
AbstractArray
{
ElType
},
ArrType2
<:
AbstractArray
{
ElType
}}
<:
AbstractRecorder
{
ElType
}
recorded_status_totals
::
ArrType1
daily_cases
::
ArrType2
total_vaccinators
::
ArrType2
mean_time_since_last_notification
::
ArrType2
end
...
...
@@ -29,8 +30,10 @@ function DebugRecorder(sim_length)
state_totals
=
@LArray
Array
{
Int
}(
undef
,
4
,
sim_length
)
(
S
=
(
1
,
:
),
I
=
(
2
,
:
),
R
=
(
3
,
:
),
V
=
(
4
,
:
))
total_vaccinators
=
Vector
{
Int
}(
undef
,
sim_length
)
mean_time_since_last_notification
=
Vector
{
Int
}(
undef
,
sim_length
)
daily_cases
=
Vector
{
Int
}(
undef
,
sim_length
)
return
DebugRecorder
(
state_totals
,
daily_cases
,
total_vaccinators
,
mean_time_since_last_notification
,
)
...
...
@@ -46,16 +49,18 @@ function DebugRecorder(val::T, sim_length) where T
total_vaccinators
=
[
copy
(
val
)
for
j
in
1
:
sim_length
]
mean_time_since_last_notification
=
[
copy
(
val
)
for
j
in
1
:
sim_length
]
daily_cases
=
[
copy
(
val
)
for
j
in
1
:
sim_length
]
return
DebugRecorder
(
state_totals
,
daily_cases
,
total_vaccinators
,
mean_time_since_last_notification
)
end
function
record!
(
t
,
modelsol
,
recorder
::
DebugRecorder
)
recorder
.
total_vaccinators
[
t
]
=
count
(
==
(
true
),
modelsol
.
u_vac
)
recorder
.
total_vaccinators
[
t
]
=
modelsol
.
daily_vaccinators
recorder
.
daily_cases
[
t
]
=
modelsol
.
daily_infected
recorder
.
recorded_status_totals
[
:
,
t
]
.=
modelsol
.
status_totals
alerts
=
filter
(
>
(
0
),
modelsol
.
time_of_last_alert
)
if
!
isempty
(
alerts
)
...
...
@@ -93,7 +98,7 @@ end
using
Printf
const
ts_colors
=
cgrad
(
:
PuBu_9
)
function
plot_model
(
varname
,
univariate_series
,
output_list
::
Vector
{
T
},
infection_begin
,
vac_begin
)
where
T
<:
DebugRecorder
plts
=
[
plot
()
,
plot
(),
plot
(),
plot
(),
plot
(),
plot
()
]
plts
=
[
plot
()
for
i
=
1
:
7
]
for
(
i
,(
p
,
data
))
in
enumerate
(
zip
(
univariate_series
,
output_list
))
# display(p[varname])
if
!
isnothing
(
varname
)
...
...
@@ -117,6 +122,7 @@ function plot_model(varname,univariate_series, output_list::Vector{T},infection_
title
=
"Infected over time"
,
legend
=
false
,
)
plot!
(
plts
[
3
],
mean
.
(
data
.
recorded_status_totals
.
R
);
ribbon
=
std
.
(
data
.
recorded_status_totals
.
R
),
label
=
labelname
,
line_z
=
i
,
color
=:
blues
,
ylabel
=
"no. of people"
,
...
...
@@ -145,10 +151,21 @@ function plot_model(varname,univariate_series, output_list::Vector{T},infection_
title
=
"Mean time since last notification"
,
legend
=
false
)
# ic,exp_growth_rate = exp_growth_rate_estimation(mean.(data.daily_cases[infection_begin,:]))
plot!
(
plts
[
7
],
mean
.
(
data
.
daily_cases
);
ribbon
=
std
.
(
data
.
daily_cases
),
label
=
labelname
,
line_z
=
i
,
color
=:
blues
,
ylabel
=
"Days"
,
colorbar
=
false
,
title
=
"Daily (incident) cases"
,
legend
=
false
)
end
for
p
in
plts
vline!
(
p
,[
infection_begin
];
label
=
"infection begin"
,
line
=:
dot
)
vline!
(
p
,[
vac_begin
];
label
=
"vaccination begin"
,
line
=
:
dot
)
end
return
plot
(
plts
...
;
layout
=
(
6
,
1
),
size
=
(
800
,
2
5
00
),
leftmargin
=
8
Plots
.
mm
)
return
plot
(
plts
...
;
layout
=
(
7
,
1
),
size
=
(
800
,
2
9
00
),
leftmargin
=
8
Plots
.
mm
)
end
CovidAlertVaccinationModel/src/ABM/parameter_optimization.jl
0 → 100644
View file @
44426b76
using
KissABC
using
CurveFit
using
Loess
@views
function
exp_growth_rate_estimation
(
incident_cases
)
diff
(
l
)
=
[
l
[
i
]
-
l
[
i
-
1
]
for
i
=
2
:
length
(
l
)]
l
=
length
(
incident_cases
)
loess_interp
=
loess
(
1
:
l
,
incident_cases
)
interp_incident_cases
=
Loess
.
predict
(
loess_interp
,
collect
(
1.0
:
l
))
second_deriative
=
diff
(
diff
(
interp_incident_cases
))
inflection_pt
=
any
(
second_deriative
.<=-
1
)
?
findfirst
(
<=
(
-
1
),
second_deriative
)
:
l
#completely heuristic
ic
,
exp_growth_rate
=
exp_fit
(
1
:
inflection_pt
,
incident_cases
[
1
:
inflection_pt
])
#fit y = a exp(b x), b is exp growth rate
return
ic
,
exp_growth_rate
end
function
vaccination_data
()
end
function
fit_epi_parameters
(
p_tuple
,
target_growth_rate
)
samples
=
1
priors
=
Factored
(
Uniform
(
0.0001
,
0.005
))
function
cost
(
p
)
new_params
=
merge
(
p_tuple
,
(
base_transmission_probability
=
p
[
1
],))
out
=
mean_solve
(
samples
,
new_params
,
DebugRecorder
)
incident_cases
=
mean
.
(
out
.
daily_cases
)
_
,
exp_growth_rate
=
exp_growth_rate_estimation
(
incident_cases
)
return
(
target_growth_rate
-
exp_growth_rate
)
^
2
end
out
=
ABCDE
(
priors
,
cost
,
0.01
;
verbose
=
true
,
nparticles
=
100
,
generations
=
20
,
parallel
=
true
)
#this one has better NaN handling
return
out
end