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RtForecastR walkthrough

library(RtForecastR)

Fit R_t and get a forecast

rt_forecast() estimates the filtered (real-time) and smoothed (retrospective) effective reproduction number from a case-count time series, and produces a genuine one-step-ahead out-of-sample forecast.

data(measles_cdmx)
fit <- rt_forecast(measles_cdmx$time, measles_cdmx$cases,
                    mean_GI = 11/7, var_GI = (4/7)^2)
fit
#> <rtforecast> 31 time points
#> Latest R_t (filtered): 0.722 (0.462-1.064) 
#> One-step-ahead forecast for time 48 :
#>   4.5 cases (95% CI: 1-10, 50% CI: 3-6)
plot(fit, which = "Rt")

plot(fit, which = "forecast")

Checking calibration

fit$predictions holds in-sample one-step-ahead predictions - a quick adequacy check:

mae(fit$predictions$cases, fit$predictions$pred_next)
#> [1] 9.38
coverage(fit$predictions$cases, fit$predictions$pred_lo95, fit$predictions$pred_hi95)
#> [1] 0.7666667

For a genuine prospective evaluation, accumulate fit$forecast and the following week’s actual case count over several weeks and pass the resulting quantile lists to wis(); see ?wis and ?score_batches.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.