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Chris Jewell
covid19uk
Commits
43faa37d
Commit
43faa37d
authored
Jan 04, 2021
by
Chris Jewell
Browse files
Switched to FNC TierData ingester.
parent
1d912c7a
Changes
4
Hide whitespace changes
Inline
Side-by-side
inference.py
View file @
43faa37d
...
...
@@ -42,14 +42,10 @@ if __name__ == "__main__":
config
=
yaml
.
load
(
f
,
Loader
=
yaml
.
FullLoader
)
inference_period
=
[
np
.
datetime64
(
x
)
for
x
in
config
[
"
settings
"
][
"inference_period"
]
np
.
datetime64
(
x
)
for
x
in
config
[
"
Global
"
][
"inference_period"
]
]
covar_data
=
model_spec
.
read_covariates
(
config
[
"data"
],
date_low
=
inference_period
[
0
],
date_high
=
inference_period
[
1
],
)
covar_data
=
model_spec
.
read_covariates
(
config
)
# We load in cases and impute missing infections first, since this sets the
# time epoch which we are analysing.
...
...
model_spec.py
View file @
43faa37d
...
...
@@ -19,7 +19,7 @@ XI_FREQ = 14 # baseline transmission changes every 14 days
NU
=
tf
.
constant
(
0.28
,
dtype
=
DTYPE
)
# E->I rate assumed known.
def
read_covariates
(
paths
,
date_low
,
date_h
ig
h
):
def
read_covariates
(
conf
ig
):
"""Loads covariate data
:param paths: a dictionary of paths to data with keys {'mobility_matrix',
...
...
@@ -27,6 +27,9 @@ def read_covariates(paths, date_low, date_high):
:returns: a dictionary of covariate information to be consumed by the model
{'C': commute_matrix, 'W': traffic_flow, 'N': population_size}
"""
paths
=
config
[
"data"
]
date_low
=
np
.
datetime64
(
config
[
"Global"
][
"inference_period"
][
0
])
date_high
=
np
.
datetime64
(
config
[
"Global"
][
"inference_period"
][
1
])
mobility
=
data
.
read_mobility
(
paths
[
"mobility_matrix"
])
popsize
=
data
.
read_population
(
paths
[
"population_size"
])
commute_volume
=
data
.
read_traffic_flow
(
...
...
@@ -35,11 +38,12 @@ def read_covariates(paths, date_low, date_high):
geo
=
gp
.
read_file
(
paths
[
"geopackage"
])
geo
=
geo
.
loc
[
geo
[
"lad19cd"
].
str
.
startswith
(
"E"
)]
tier_restriction
=
data
.
read_challen_tier_restriction
(
paths
[
"tier_restriction_csv"
],
date_low
,
date_high
,
)
# tier_restriction = data.read_challen_tier_restriction(
# paths["tier_restriction_csv"],
# date_low,
# date_high,
# )
tier_restriction
=
data
.
TierData
.
process
(
config
)[:,
:,
2
:]
weekday
=
pd
.
date_range
(
date_low
,
date_high
).
weekday
<
5
return
dict
(
...
...
pyproject.toml
View file @
43faa37d
...
...
@@ -27,6 +27,7 @@ ipython = "^7.18.1"
jupyter
=
"^1.0.0"
flake8
=
"^3.8.4"
black
=
"^20.8b1"
pytest
=
"^6.2.1"
[tool.black]
line-length
=
80
...
...
template_config.yaml
View file @
43faa37d
# Covid_ODE model configuration
# Covid stochastic model configuration
Global
:
prependID
:
False
prependDate
:
False
inference_period
:
-
2020-10-11
-
2021-01-04
time_step
:
1.
GenerateOutput
:
storeInputs
:
False
scrapedDataDir
:
scraped_data
storeProcessedInputs
:
False
data
:
mobility_matrix
:
data/mergedflows.csv
population_size
:
data/c2019modagepop.csv
commute_volume
:
data/2012
1
1_OFF_SEN_COVID19_road_traffic_national_table.xlsx
reported_cases
:
data/Anonymised Combined Line List 20201
109
.csv
commute_volume
:
data/2012
3
1_OFF_SEN_COVID19_road_traffic_national_table.xlsx
reported_cases
:
data/Anonymised Combined Line List 20201
231
.csv
case_date_type
:
specimen
pillar
:
both
tier_restriction_csv
:
data/tidyLAD19Tiers.csv
geopackage
:
data/UK2019mod_pop.gpkg
parameter
:
beta1
:
0.291
# R0 2.4
beta2
:
0.876
# Contact with commuters 1/3rd of the time
beta3
:
1.0
# lockdown vs normal
omega
:
1.0
# Non-linearity parameter for commuting volume
nu
:
0.5
# E -> I transition rate
gamma
:
0.45
# I -> R transition rate
xi
:
-
2.07
-
0.15
-
-1.08
-
-0.78
-
-0.73
-
-0.51
settings
:
inference_period
:
-
2020-07-03
-
2020-09-29
time_step
:
1.
prediction_period
:
-
2020-02-19
-
2020-08-01
mcmc
:
dmax
:
84
...
...
@@ -48,8 +39,23 @@ mcmc:
concentration
:
2.0
rate
:
4.0
output
:
results_dir
:
$global_scratch/covid19/
results_dir
:
output/testing
posterior
:
posterior.hd5
national_rt
:
national_rt.xlsx
geopackage
:
prediction.gpkg
AreaCodeData
:
input
:
json
address
:
"
https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LAD_APR_2019_UK_NC/FeatureServer/0/query?where=1%3D1&outFields=LAD19CD,LAD19NM&returnGeometry=false&returnDistinctValues=true&orderByFields=LAD19CD&outSR=4326&f=json"
format
:
ons
output
:
processed_data/processed_lad19cd.csv
regions
:
[
"
E"
]
TierData
:
input
:
api
address
:
None
format
:
api
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