Commit 0e7fc9dd authored by Chris Jewell's avatar Chris Jewell
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Corrected list formatting in README.md 2

parent 1cdaa850
......@@ -61,19 +61,19 @@ national reproduction number estimate.
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:
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.
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:
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`.
b. 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
- Incidence summary: `covid.tasks.summarize.infec_incidence` calculates mean and quantile information for the medium term prediction, `<output_dir>/infec_incidence_summary.csv`.
- 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`.
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`.
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
atypically given the model, `<output_dir>/exceedance_summary.csv`.
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