replace code from interpretation lines for explicit credible intervals

[avallecam]

tutorials-middle/episodes/quantify-transmissibility.Rmd

Lines 459 to 467 in e61911f

	+ From the summary of our analysis we see that the expected change in reports is `r summary(estimates)$estimate[summary(estimates)$measure=="Expected change in reports"]` with the estimated new infections `r summary(estimates)$estimate[summary(estimates)$measure=="New infections per day"]`.
	+ The effective reproduction number $R_t$ estimate (on the last date of the data) is `r summary(estimates)$estimate[summary(estimates)$measure=="Effective reproduction no."]`.
	+ The exponential growth rate of case numbers is `r summary(estimates)$estimate[summary(estimates)$measure=="Rate of growth"]`.
	+ The doubling time (the time taken for case numbers to double) is `r summary(estimates)$estimate[summary(estimates)$measure=="Doubling/halving time (days)"]`.

#| echo: false
estimates_summary <- estimates$summary %>% as_tibble() %>% filter(!stringr::str_detect(measure,"change")) %>% unnest(numeric_estimate)

Interpretation

+ From the summary of our analysis we see that the expected change in reports is 
`r estimates$summary %>% as_tibble() %>% filter(stringr::str_detect(measure,"change")) %>% pull(estimate)` 
with the estimated new infections of
`r estimates_summary %>% filter(stringr::str_detect(measure,"infections")) %>% dplyr::select(median) %>% pull()`
with 90% credible interval of
`r estimates_summary %>% filter(stringr::str_detect(measure,"infections")) %>% dplyr::select(lower_90) %>% pull()` to `r estimates_summary %>% filter(stringr::str_detect(measure,"infections")) %>% dplyr::select(upper_90) %>% pull()`.

+ The effective reproduction number $R_t$ estimate (on the last date of the data),
or the number of new infections caused by one infectious individual, on average, is
`r estimates_summary %>% filter(stringr::str_detect(measure,"reproduction")) %>% dplyr::select(median) %>% pull()`,
with a 90% credible interval of
`r estimates_summary %>% filter(stringr::str_detect(measure,"reproduction")) %>% dplyr::select(lower_90) %>% pull()` to `r estimates_summary %>% filter(stringr::str_detect(measure,"reproduction")) %>% dplyr::select(upper_90) %>% pull()`.

+ The exponential growth rate of case reports is `r summary(estimates)$estimate[summary(estimates)$measure=="Rate of growth"]`.

+ The doubling time (the time taken for case reports to double) is `r summary(estimates)$estimate[summary(estimates)$measure=="Doubling/halving time (days)"]`.