P&I death cts/ILI cts

functions.py
- return dicts with P&I death counts and ILI counts raw

Supp_zOR_CFR_CHR.py
- plot ratio of P&I deaths and ILI counts (raw) vs.
retro zOR metric
H3 isolate proportion up to Thxgiving, supplement

Supp_zOR_H3.py
- plot severity metrics versus H3 proportion of
subtyped isolates collected in all 10 regions up
to and including the exact week of Thanksgiving
- import benchmark index as one of the severity
metrics
- other metrics: mean retro zOR, mean early warning
zOR, benchmark index, H3 proportion- NREVSS, whole
season, H3 proportion- CDC, whole season, H3
proportion- NREVSS, season through week of Thanksgiving

functions.py
- import Thanksgiving H3 isolate data (My_Bansal_
Lab/Clean_Data_for_Import/NREVSS_Isolates_Thanksgiving.csv)
- Thanksgiving data had been cleaned in R in
"sum_isolates_Thanksgiving.R"
nation & region lvl peak-based retro classif

functions.py
- classif_zOR_index function updated for
nation and region designations; nation uses
max peak incidence for aggregate nation-level
data; region uses max peak incidence for
each region such that regions may have
different weeks that designate zOR

F4_zOR_region.py
- draws boxplots of zOR by region at nation
level

F4_zOR_region_reglvl.py
- draws boxplots of zOR by region at region
level
fc: export mean zOR data by state for choropleth

extract_meanzOR_by_region.py
- export mean retro and early zOR by state and
hhs region and season

functions.py
- region_state_dictionary function has region
number as key and list of full state names
(includes only states in the continental US) as
values
fc: choropleths mean zOR by season

F4_zORregion_choropleth.R
- from state-level mean zOR data, draw choropleth
of mean retro zOR by state using ggplot2
- states in the same region are all the same color
explore increased SD for 'region' method of retrozOR

F4_zOR_region.py
F4_zOR_region_reglvl.py
- added some code to print dict values but removed
it for this commit
- it seems that the 'region' incidence peak based
retrozOR method has greater SDs across all regions
than the 'nation' incidence peak based method
because the metric is capturing the epidemic growth
phase of the season. this part of the season is likely
to have more monotonic changes in zOR than if the
metric captured part of the epidemic growth phase
and the epidemic peak. perhaps this monotonicity
in zOR is what is causing the values of the mean
retrozOR to have greater standard deviation across
the board
changes to charts for Biggerstaff talk
add fxns for ILINet plotting complements

functions.py
ILINet_week_OR_processing function
- should import CDC_Source/Import_Data/all_cdc_source_data.csv
data correctly and process into incid and OR dicts for plotting
- correctness of function has not yet been checked

to do:
determine if there need to be ILINet specific functions for
"week_zOR_processing" and "week_plotting_dicts" (there are templates
for them in functions.py, but they may be deleted)
upgraded to ubuntu14.04, SDI reimport to mysql

changed pathways for admin/load_sdi.sql and admin/
sdi_rapidd_cleaning_9_11.py
minor changes F4 script

F4_zOR_region.py
fc: draw F2 with ILINet not SDI data

ILINet data spans 1997-8 to 2013-14 flu seasons

functions.py
- ILINet-data-specific functions for creating incid, OR,
and zOR plotting dicts and processing
- ILINet_week_OR_processing, ILINet_week_plotting_dicts,
ILINet_week_zOR_processing

ILINet_F2_OR_time.py
- draw OR vs. time for ILINet data

ILINet_F2_incid_time.py
- draw incidence per 100,000 vs. time for ILINet data

clean_pop_age_Census.py
- take Census data from multiple sources and clean
and format population data for children, adults, and
'other' for each year of ILINet data
- children = 5-24, adults = 25-64 (as in ILINet)
- exported data: Census/Import_Data/totalpop_age_Census_98-14.csv
- variables: season number, agegroup code, population
- season number 0 = 1999-2000 and range from -2 (1997-98) to 14 (2013-14)