fc: regression - nat benchmark, reg sev index

mechanism_regression.R
- national level benchmark response: age-specific ILI, ORs,
precip, temp, proportion of H3 (in combinations with and
without age-specific ILI and OR)
- total ILI used as an offset because clinical population
severity would typically be normalized by ILI attack rate
(for a fixed CHR or CFR, a higher attack rate means higher
severity in the population)
- motivate use of OR_ca in study
- regional level severity index regression -- less important
fc: pandemic baseline

functions.py
- week_zOR_processing_pandemic: calculate zOR time series
for pandemic season with different baselines -- between
pandemic waves, previous season's baseline, after
pandemic

Supp_pandemic_analyses.py
- plot zOR time series for 2008-2010 with different
pandemic baselines
edit formatting and labels F1

F1_allfigs.R
- margin, axis labels, legends updated for 8/16/14 ms draft
explore region level regression for temp assoc

mechanism_regression.R
- updated region level regression code even though it
is not being used in our study
- Southeastern US (a warm region) seems to experience
more severe seasons than the rest of the nation, which
contradicts findings that colder temperatures are
more highly associated with severe seasons. Perhaps
vaccination or age demographics play a role.
benchmark barplot for regression

F3_benchmarkbars.R
- barplot with benchmark index by season for 97-98
through 13-14 seasons
- 3-period normalization
- for mock-up figure in ms draft with regression/
exploratory analysis results
state deviation from nat baseline - bxp

F3_zOR_state_stlvl.py
- boxplot with deviation of state from nat baseline
in retrozOR
- deviation is calculated as (statezOR - natzOR)/abs(nat zOR)

functions.py
- add readNationalClassifFile for zOR scores
zOR vs vaxmatch & vaxeffic (Flu Summaries)

Supp_zOR_vax.py
- scatterplot retrozOR vs % vaccine match (isolates
matching vaccine strains according to CDC Flu Summary
data - CDC source/not NREVSS)
- scatterplot retrozOR vs % vaccine efficacy (weighted
average of TIV and LAIV estimates reported in
Osterholm 2012 and averaged in USfluvaxdata_June13.ods

functions.py
- season_vaxmatch function imports subtype5.csv data

Note: subtype5.csv was altered with sed to replace
corrupted characters (dash and '&')