@@ -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`.