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covidsims - Simulating COVID-19 dynamics in Brazil

To run the preprocessing pipeline:

python preprocess.py ../configs/T01/SP/preprocess_T01_SP.cfg


Config file must encode:

Parameter Description
PARAM_SOURCEPATH directory where the raw datafile is stored, as a list
PARAM_TARGETPATH directory where results will be stored, as a list
PARAM_DATAFILE the name of the raw datafile
PARAM_DATAFIELDS list with the fields that correspond to region, state, city, date, #newCases, and #newDeaths, using the strings in the header of PARAM_DATAFILE
PARAM_TERRITORY list of areas that compose the territory under analysis (i.e., territorial units)
PARAM_POPSIZES dictionary of population sizes per territorial unit
PARAM_OUTCOMES list of (p, mu, rsd) tuples, with p as the share of cases with some outcome category (e.g., cases with mild to moderate progression), mu is the mean recovery time of cases (in days), and rsd is the relative standard deviation of recovery time
PARAM_MA_WINDOW the number of days to consider in the moving average of new cases and deaths
PARAM_CORE_MODEL Peddireddy of IB-forward. See comments in ./code/sharedDefs.py/createBoL(...)
PARAM_MASK_ERRORS If True, adjustments in the R(t) estimator to avoid negative values will be performed

Several UTF-8, flat files with tab-separated fields are created:

File Description
daily_changes.csv File with daily changes of the variables of interest, namely ∆S(t), ∆C(t), ∆I(t), ∆R(t), and ∆D(t)
surveillance.csv File with daily status of the variables of interest, namely S(t), C(t), I(t), R(t), and D(t)
violations.csv File listing the violations of quality criteria for the generated data. See criteria in ./code/sharedDefs.py/playBoL(...)
roulette.csv File describing the roulette employed to assign the recovery time to an arbitrary case

The resulting data can be visualised with Excel; a template is available at ./assets/Helper_SP.xlsx