test differenced OR metric

functions.py
- week_zOR_processing function could be altered to return
the OR differenced by mean across first 'normweeks' of the
season --> potential new metric to replace zOR

F3_zOR_benchmark.py
ILINet_F3_zOR_benchmark.py
- reverted back to initial state
- had played around with x and y limits and data to test
zOR, differenced OR, and raw OR as severity indexes
- interested in robustness to normalization and consistency
across datasets
savefigs to compare index: raw OR, diff OR, zOR

functions.py
F3_zOR_benchmark.py
- cycled through parameters and plot limits to
draw and save raw OR vs benchmark, mean differenced
OR vs benchmark, and zOR vs benchmark for a 7
week normalization period
- returned functions and plot formatting back to
original state
normalize_incidCA, normalize_attackCA

functions.py
- normalize incidence rates by children and attack rates
by children and adults by gp_fluweeks length (wks 40 to 20)
only, for Figure 6
color change - mms

functions.py
- mild = blue, moderate = yellow, red = severe
in plotting colors for different season types

F6_incid_time_CA_normalized.py
- change comments in header to better reflect
chart purpose
folder change, plot formatting

F4_zORregion_choropleth.R
- choropleth figure saving folder name had changed

F6_incid_time_CA_normalized.py
- plotting formatting references were reassigned;
more tightly linked to functions.py
export local early warning, plot accuracy choropl

evaluate_state_early_warning.py
- export number of matching classifications for
early warning state to retrospective state level
(local severity early warning)

F4_zOR_region_choropleth.R
- plot choropleths for the following number of
matching classifications: early warning at state
level matching retrospective national level,
early warning at state level matching retrospective
state level, retrospective state level matching
retrospective at national level
prev commit typo

F4_state_accuracy_choropleth.R
- choropleths for matching classifications for
different spatial scales (state or nation) and time
periods (early warning or retrospective)
- immediately preceding commit had incorrect description
fc: example mild/mod/sev ch and adult incid curves

F6_incid_time_CA_mildmodsev.py
- plot child and adult incidence curves per 100,000
non-normalized for example mild, moderate, and severe
seasons
- alternative for tri panel with child and adult
incidence curves with mild/mod/sev specific season
plots
update cdc_import, redraw CFR/CHR plots for supp

functions.py
- correct cdc_import_CFR_CHR function indexes for
'ILI' and 'allpatients'
- in same function, replace 'NA' strings with
float('nan')
- skip data import for rows where week is 'NA'
(the all_cdc_import_data.csv file was updated
to include all 2013-14 flu season weeks)

Supp_zOR_CFR_CHR.py
- update retrozOR metric import to reflect
changes in functions.py structure
- redraw charts after cdc_import_CFR_CHR
indexes for ILI and allpatients are corrected
fc: mn retro zOR distr for states (bxp)

F4_zOR_state.py
- first commit for boxplot of mean retrospective
zOR distributions by state across all seasons
- values sorted by median
- NaN values are masked
- colors match for states within the same HHS
administrative region
SDI week variable exploration

F2_incid_time.py
- Thanksgiving week number does not correspond with the
week number in incidence vs. time plots
- explore reasons why that might be: verify SDI 'week'
variable - does it correspond to data collected in
week before or after that of the referenced date?
- emailed Cecile and awaiting response
add fig titles and adjust formatting

F4_state_accuracy_choropleth.R
- add figure titles for manuscript choropleths
- decreased title fontsize for readability
pk_flu_week_ix - calc max incid wk, fluweeks only

functions.py
- add peak_flu_week_index function
- calculate week of maximum incidence based on flu weeks
only
- classif_zOR_index and classif_zOR_processing functions
all reference this function now for the calculation of
the retrospective index
fc: zOR vs peak timing national

Supp_zOR_peaktime.py
- scatterplot of retrospective and early warning index
vs. week of peak incidence at national level

functions.py
- rm checks for peak_flu_week_index function
state/reg incid retro pkwk based classif

previously, most analyses had been performed at the
"nation level retrospective peak week based classification",
which means that the peak incidence week of the nation level
incidence curve was used to define the retrospective zOR
classification period for the state and regional levels. In
an attempt to make the analyses more compact, we decided
to explore the option of state/reg incidence peaks defining
state/reg retrospective classifications. In this new
framework, the retrospective period is different for every
state/region within a season.

Results do not appear to be the same at first glance.
readStateClassifFile function moved to functions.py

F4_zOR_state.py
- remove readStateClassifFile function from
within-script functions

functions.py
- add readStateClassifFile, which imports files of
the format (season, state, mn_retro_zOR, mn_early_zOR)
into a dict
fc: zOR vs. peakweek at state level

Supp_zOR_peaktime_state.py
- state peak retrospective classification
- one plot per season per classification period vs. peak
incidence week for state
- a single plot for all seasons for retrospective period vs.
peak incidence week for state
cum_incid_at_classif season reference

functions.py
- cum_incid_at_classif function had been changed
yesterday to grab peak week index using the
peak_flu_week_index function. "s" was replaced
with "snum" in calling incidence dictionary list
fc: zOR vs epiduration - state level

functions.py
- epidemic_duration fxn: return number of weeks
in an epidemic given incidence list and cumulative
incidence percents that defined epidemic duration

Supp_zOR_epiduration_state.py
- plot zOR vs epidemic duration in weeks at
state level by season and for all seasons (include
both retrospective and early warning zOR)
- epidemic duration defined as 10% to 90% cumulative
incidence inclusive
fc: zOR vs inpatient/outpatient ILI supp plot

functions.py
- add proportion_ILI_anydiag function to import
inpatient and outpatient SDI data of different
format
- returns dictionary with proportion of cases due
to ILI in a given flu season

Supp_zOR_CFR_CHR.py
- add plot zOR vs ratio of inpatient to outpatient
ILI proportion of any diagnoses (at season level)
- correlation coefficients are NaN for CHR, CFR,
and dI_ratio lists vs zOR  because there is at least
one missing value in each of those lists