Many factors surrounding the transmission, severity of infections, and remaining susceptibility of local populations to COVID-19 remain highly uncertain. However, as new data on hospitalized cases becomes available, we wish to incorporate this data in order to update and refine our projections of future demand to better inform capacity planners. To that end we have extended CHIME to increase the epidemiological process realism and to coherently incorporate new data as it becomes available. This extension allows us to transition from a small number of scenarios to assess best and worst case projections based on parameter assumptions, to a probabilistic forecast representing a continuous distribution of likely scenarios.
First run the sims by passing the name of the location (matching the prefix of the data/<prefix>_parameters.csv and data/<prefix>_ts.csv files).
python _01_GOF_sims.py <prefix> <n_chains> <n_iters> <penalty_factor>To run for each of a list of locations, use:
for loc in 'CCH' 'LGH' 'Downtown' 'MCP'; do python _01_GOF_sims.py $loc 8 5000 -99; doneIf you already know the appropriate penalty factor, (and it's common across all the hospitals), do this instead:
for loc in 'CCH' 'LGH' 'Downtown' 'MCP'; do python _01_GOF_sims.py $loc 8 5000 <<known_penalty_factor>>; doneTHe penalty factor should be between .05 and less than 1. 1 is maximum penalization.
Results will be saved to output/<prefix>_chains.pkl, which can be analysed/plotted using:
for loc in 'CCH' 'LGH' 'Downtown' 'MCP'; do python _02_munge_chains.py $loc; done