Commit 0e7fc9dd authored by Chris Jewell's avatar Chris Jewell
Browse files

Corrected list formatting in README.md 2

parent 1cdaa850
...@@ -61,19 +61,19 @@ national reproduction number estimate. ...@@ -61,19 +61,19 @@ national reproduction number estimate.
6. Prediction: `covid.tasks.predict` calculates the Bayesian predictive distribution of the epidemic given the observed 6. Prediction: `covid.tasks.predict` calculates the Bayesian predictive distribution of the epidemic given the observed
data and joint posterior distribution. This is used in two ways: data and joint posterior distribution. This is used in two ways:
a. in-sample predictions are made for the latest 7 and 14 day time intervals in the observed data time window. These - in-sample predictions are made for the latest 7 and 14 day time intervals in the observed data time window. These
are saved as `<output_dir>/insample7.pkl` and `<output_dir>/insample14.pkl` `xarray` data structures. are saved as `<output_dir>/insample7.pkl` and `<output_dir>/insample14.pkl` `xarray` data structures.
b. medium-term predictions are made by simulating forward 56 days from the last+1 day of the observed data time window. These is saved as `<output_dir>/medium_term.pkl` `xarray` data structure. - medium-term predictions are made by simulating forward 56 days from the last+1 day of the observed data time window. These is saved as `<output_dir>/medium_term.pkl` `xarray` data structure.
7. Summary output: 7. Summary output:
a. LAD-level reproduction number: `covid.tasks.summarize.rt` takes the column sums of the next generation matrix as the - LAD-level reproduction number: `covid.tasks.summarize.rt` takes the column sums of the next generation matrix as the
LAD-level reproduction number. This is saved in `<output_dir>/rt_summary.csv`. LAD-level reproduction number. This is saved in `<output_dir>/rt_summary.csv`.
b. Incidence summary: `covid.tasks.summarize.infec_incidence` calculates mean and quantile information for the medium term prediction, `<output_dir>/infec_incidence_summary.csv`. - Incidence summary: `covid.tasks.summarize.infec_incidence` calculates mean and quantile information for the medium term prediction, `<output_dir>/infec_incidence_summary.csv`.
c. Prevalence summary: `covid.tasks.summarize.prevalence` calculated the predicted prevalence of COVID-19 infection - Prevalence summary: `covid.tasks.summarize.prevalence` calculated the predicted prevalence of COVID-19 infection
(model E+I compartments) at LAD level, `<output_dir>/prevalence_summary.csv`. (model E+I compartments) at LAD level, `<output_dir>/prevalence_summary.csv`.
d. Population attributable risk fraction for infection: `covid.tasks.within_between` calculates the population - Population attributable risk fraction for infection: `covid.tasks.within_between` calculates the population
attributable fraction of within-LAD versus between-LAD infection risk, `<output_dir>/within_between_summary.csv`. attributable fraction of within-LAD versus between-LAD infection risk, `<output_dir>/within_between_summary.csv`.
e. Case exceedance: `covid.tasks.case_exceedance` calculates the probability that observed cases in the last 7 and 14 - Case exceedance: `covid.tasks.case_exceedance` calculates the probability that observed cases in the last 7 and 14
days of the observed timeseries exceeding the predictive distribution. This highlights regions that are behaving days of the observed timeseries exceeding the predictive distribution. This highlights regions that are behaving
atypically given the model, `<output_dir>/exceedance_summary.csv`. atypically given the model, `<output_dir>/exceedance_summary.csv`.
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment