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Peter Jentsch
CovidAlertABM
Commits
db98ecd1
Commit
db98ecd1
authored
Apr 08, 2021
by
Peter Jentsch
Browse files
finally contact network done
parent
203b7a3d
Changes
13
Hide whitespace changes
Inline
Side-by-side
CovidAlertVaccinationModel/Manifest.toml
View file @
db98ecd1
...
...
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@@ -644,11 +644,11 @@ version = "0.11.1"
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...
...
@@ -677,9 +677,9 @@ version = "1.1.1"
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[[Reexport]]
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...
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@@ -687,27 +687,28 @@ uuid = "189a3867-3050-52da-a836-e630ba90ab69"
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[[Showoff]]
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@@ -764,9 +765,9 @@ uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
[[SpecialFunctions]]
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=
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@@ -786,15 +793,15 @@ uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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...
...
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[[Tar]]
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...
...
@@ -850,9 +857,9 @@ version = "1.5.3"
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=
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,
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...
...
CovidAlertVaccinationModel/Project.toml
View file @
db98ecd1
...
...
@@ -14,6 +14,7 @@ ImportAll = "c65182e5-40f4-518f-8165-175b85689199"
Intervals
=
"d8418881-c3e1-53bb-8760-2df7ec849ed5"
LabelledArrays
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Pipe
=
"b98c9c47-44ae-5843-9183-064241ee97a0"
...
...
CovidAlertVaccinationModel/scratch_code/model_cuda.jl
0 → 100644
View file @
db98ecd1
#testing some GPU implementations of the ABM model
function
vaccinate_uniformly!
(
num_vaccines
,
u_next
,
u
,
graph
,
population_list
,
index_vectors
)
#vaccination_algorithm
vaccination_indices
=
rand
(
RNG
,
1
:
length
(
u
),
num_vaccines
)
u_next
[
vaccination_indices
]
.=
Immunized
end
function
agents_step_cuda!
(
t
,
u_next
,
u
,
population_list
,
graph
,
params
,
index_vectors
,
vaccination_algorithm!
)
@unpack
p
,
recovery_rate
,
vaccines_per_day
,
vaccination_start_day
=
params
u_next
.=
u
#keep state the same if nothing else happens
if
t
>=
vaccination_start_day
vaccination_algorithm!
(
Int
(
floor
(
vaccines_per_day
*
length
(
u
))),
u_next
,
u
,
graph
,
population_list
,
index_vectors
)
end
@inbounds
for
i
in
1
:
length
(
u
)
agent_status
=
u
[
i
]
agent_demo
=
population_list
[
i
]
if
agent_status
==
Susceptible
@inbounds
for
j
in
1
:
length
(
u
)
if
graph
[
i
,
j
]
!=
0
&&
u
[
j
]
==
Infected
if
rand
(
RNG
)
<
contact_weight
(
p
,
graph
[
i
,
j
])
(
u_next
[
i
]
=
Infected
)
end
end
end
elseif
agent_status
==
Infected
if
rand
(
RNG
)
<
recovery_rate
u_next
[
i
]
=
Recovered
end
end
end
end
function
agents_step_gpu!
(
t
,
u_next
,
u
,
graph
,
params
)
u_next
.=
u
function
kernel
(
state
,
_
,
(
t
,
u_next
,
u
,
g
,
params
,
randstate
))
@unpack
p
,
recovery_rate
=
params
i
=
linear_index
(
state
)
if
i
<=
length
(
u
)
agent_status
=
u
[
i
]
if
agent_status
==
Susceptible
for
j
in
1
:
length
(
u
)
if
g
[
i
,
j
]
==
1
&&
u
[
j
]
==
Infected
&&
GPUArrays
.
gpu_rand
(
Float64
,
state
,
randstate
)
<
p
u_next
[
i
]
=
Infected
end
end
elseif
agent_status
==
Infected
if
GPUArrays
.
gpu_rand
(
Float64
,
state
,
randstate
)
<
recovery_rate
u_next
[
i
]
=
Recovered
end
end
end
return
nothing
end
gpu_call
(
kernel
,
u
,
(
t
,
u_next
,
u
,
graph
,
params
,
GPUArrays
.
default_rng
(
typeof
(
u
))
.
state
))
end
function
solve_cuda!
(
u_0
,
params
,
steps
,
agent_model
,
vaccination_algorithm!
;
record
=
false
)
solution
=
vcat
([
u_0
],
[
fill
(
Susceptible
,
length
(
u_0
))
for
i
in
1
:
steps
])
population_list
=
cu
(
agent_model
.
demographics
)
#list of demographic status for each agent
index_vectors
=
cu
.
(
agent_model
.
demographic_index_vectors
)
#lists of indices of each agent with a given demographic
base_network
=
cu
(
agent_model
.
base_network
)
#network with households and LTC homes
ws_contacts
=
cu
(
agent_model
.
ws_matrix_list
)
rest_contacts
=
cu
(
agent_model
.
rest_matrix_list
)
ws_daily_graph
=
MixingGraph
(
index_vectors
,
ws_contacts
.
daily
,
contact_time_distribution_matrix
)
rest_daily_graph
=
MixingGraph
(
index_vectors
,
rest_contacts
.
daily
,
contact_time_distribution_matrix
)
ws_weekly_graph
=
MixingGraph
(
index_vectors
,
ws_contacts
.
twice_a_week
,
contact_time_distribution_matrix
)
rest_weekly_graph
=
MixingGraph
(
index_vectors
,
rest_contacts
.
twice_a_week
,
contact_time_distribution_matrix
)
graph
=
similar
(
base_network
)
graphlist
=
typeof
(
graph
)[]
for
t
in
1
:
steps
day_of_week
=
mod
(
t
,
7
)
graph
.=
0
graph
.=
base_network
if
!
(
day_of_week
==
3
||
day_of_week
==
4
)
apply_mixing_graph
(
graph
,
ws_daily_graph
)
end
apply_mixing_graph
(
graph
,
rest_daily_graph
)
if
rand
(
RNG
)
<
5
/
7
apply_mixing_graph
(
graph
,
ws_weekly_graph
)
apply_mixing_graph
(
graph
,
rest_weekly_graph
)
end
ws_once_graph
=
MixingGraph
(
index_vectors
,
ws_contacts
.
otherwise
,
contact_time_distribution_matrix
)
rest_once_graph
=
MixingGraph
(
index_vectors
,
rest_contacts
.
otherwise
,
contact_time_distribution_matrix
)
apply_mixing_graph
(
graph
,
ws_once_graph
)
apply_mixing_graph
(
graph
,
rest_once_graph
)
if
record
push!
(
graphlist
,
deepcopy
(
graph
))
end
agents_step!
(
t
,
solution
[
t
+
1
],
solution
[
t
],
population_list
,
graph
,
params
,
index_vectors
,
vaccination_algorithm!
)
# advance agent states based on the new network, vaccination process given by vaccination_algorithm!, which is just a function defined as above
end
return
solution
,
graphlist
end
\ No newline at end of file
CovidAlertVaccinationModel/scratch_code/other_model_implementation.jl
0 → 100644
View file @
db98ecd1
function
agents_step_2!
(
t
,
S_indicator
,
R_indicator
,
I_indicies
,
I_indicies_next
,
population_list
,
graph
,
params
,
index_vectors
,
vaccination_algorithm!
)
@unpack
p
,
recovery_rate
,
vaccines_per_day
,
vaccination_start_day
=
params
# if t >= vaccination_start_day
# vaccination_algorithm!(Int(floor(vaccines_per_day*length(u))),u_next,u,graph,population_list,index_vectors)
# end
while
!
isempty
(
I_indicies
)
u
=
dequeue!
(
I_indicies
)
for
v
in
1
:
length
(
population_list
)
if
graph
[
u
,
v
]
!=
0
&&
S_indicator
[
v
]
==
true
if
rand
(
RNG
)
<
contact_weight
(
p
,
graph
[
u
,
v
])
# display(v)
enqueue!
(
I_indicies_next
,
v
)
S_indicator
[
v
]
=
false
end
end
end
if
rand
(
RNG
)
<
recovery_rate
R_indicator
[
u
]
=
true
else
enqueue!
(
I_indicies_next
,
u
)
end
end
end
using
BenchmarkTools
using
DataStructures
function
solve_2!
(
u_0
,
params
,
steps
,
agent_model
,
vaccination_algorithm!
;
record
=
false
)
S_indicator
=
u_0
.==
Susceptible
R_indicator
=
u_0
.==
Recovered
I_indicies_next
=
Queue
{
Int
}()
I_indicies
=
Queue
{
Int
}()
for
ind
in
findall
(
==
(
Infected
),
u_0
)
enqueue!
(
I_indicies
,
ind
)
end
population_list
=
agent_model
.
demographics
#list of demographic status for each agent
index_vectors
=
agent_model
.
demographic_index_vectors
#lists of indices of each agent with a given demographic
base_network
=
agent_model
.
base_network
#network with households and LTC homes
ws_contacts
=
agent_model
.
ws_matrix_list
rest_contacts
=
agent_model
.
rest_matrix_list
ws_daily_graph
=
MixingGraph
(
index_vectors
,
ws_contacts
.
daily
,
contact_time_distribution_matrix
)
rest_daily_graph
=
MixingGraph
(
index_vectors
,
rest_contacts
.
daily
,
contact_time_distribution_matrix
)
ws_weekly_graph
=
MixingGraph
(
index_vectors
,
ws_contacts
.
twice_a_week
,
contact_time_distribution_matrix
)
rest_weekly_graph
=
MixingGraph
(
index_vectors
,
rest_contacts
.
twice_a_week
,
contact_time_distribution_matrix
)
graph
=
similar
(
base_network
)
graphlist
=
typeof
(
graph
)[]
for
t
in
1
:
steps
day_of_week
=
mod
(
t
,
7
)
graph
.=
0
graph
.=
base_network
if
!
(
day_of_week
==
3
||
day_of_week
==
4
)
apply_mixing_graph
(
graph
,
ws_daily_graph
)
end
apply_mixing_graph
(
graph
,
rest_daily_graph
)