Content from Quantifying transmission
-
Last updated on 2024-03-28 | +
Last updated on 2024-03-30 | Edit this page
Case dataR
-head(incidence2::covidregionaldataUK)
+dplyr::as_tibble(incidence2::covidregionaldataUK)
OUTPUT
- date region region_code cases_new cases_total deaths_new
-1 2020-01-30 East Midlands E12000004 NA NA NA
-2 2020-01-30 East of England E12000006 NA NA NA
-3 2020-01-30 England E92000001 2 2 NA
-4 2020-01-30 London E12000007 NA NA NA
-5 2020-01-30 North East E12000001 NA NA NA
-6 2020-01-30 North West E12000002 NA NA NA
- deaths_total recovered_new recovered_total hosp_new hosp_total tested_new
-1 NA NA NA NA NA NA
-2 NA NA NA NA NA NA
-3 NA NA NA NA NA NA
-4 NA NA NA NA NA NA
-5 NA NA NA NA NA NA
-6 NA NA NA NA NA NA
- tested_total
-1 NA
-2 NA
-3 NA
-4 NA
-5 NA
-6 NA# A tibble: 6,370 × 13
+ date region region_code cases_new cases_total deaths_new deaths_total
+ <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
+ 1 2020-01-30 East Mi… E12000004 NA NA NA NA
+ 2 2020-01-30 East of… E12000006 NA NA NA NA
+ 3 2020-01-30 England E92000001 2 2 NA NA
+ 4 2020-01-30 London E12000007 NA NA NA NA
+ 5 2020-01-30 North E… E12000001 NA NA NA NA
+ 6 2020-01-30 North W… E12000002 NA NA NA NA
+ 7 2020-01-30 Norther… N92000002 NA NA NA NA
+ 8 2020-01-30 Scotland S92000003 NA NA NA NA
+ 9 2020-01-30 South E… E12000008 NA NA NA NA
+10 2020-01-30 South W… E12000009 NA NA NA NA
+# ℹ 6,360 more rows
+# ℹ 6 more variables: recovered_new <dbl>, recovered_total <dbl>,
+# hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>, tested_total <dbl>To use the data, we must format the data to have two columns:
-
@@ -447,18 +442,20 @@
-
confirm: number of confirmed cases on that date.
OUTPUT<
Let’s use dplyr for this:
R
-cases <- aggregate(
- cases_new ~ date,
- data = incidence2::covidregionaldataUK[, c("date", "cases_new")],
- FUN = sum
-)
-colnames(cases) <- c("date", "confirm")
+library(dplyr)
+
+cases <- incidence2::covidregionaldataUK %>%
+ select(date, cases_new) %>%
+ group_by(date) %>%
+ summarise(confirm = sum(cases_new, na.rm = TRUE)) %>%
+ ungroup()
There are case data available for 489 days, but in an outbreak +
There are case data available for 490 days, but in an outbreak situation it is likely we would only have access to the beginning of this data set. Therefore we assume we only have the first 90 days of this data.
@@ -507,18 +504,18 @@Delays and data
+Time of symptom onset
+Incubation period
+
+
Time of case report
Incubation period + time from symptom onset to case
notification
-
+
Time of hospitalisation
Incubation period + time from symptom onset to hospitalisation
-
-Time of symptom onset
-Incubation period
-
@@ -746,34 +743,81 @@ R
reported_cases <- cases[1:90, ]
+
estimates <- epinow(
reported_cases = reported_cases,
generation_time = generation_time_opts(generation_time_fixed),
- delays = delay_opts(incubation_period_fixed + reporting_delay_fixed),
- rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd))
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ )
)
OUTPUT
-WARN [2024-03-28 21:34:59] epinow: There were 8 divergent transitions after warmup. See
+WARN [2024-03-30 17:17:33] epinow: There were 1 divergent transitions after warmup. See
https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
to find out why this is a problem and how to eliminate them. -
-WARN [2024-03-28 21:34:59] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-03-30 17:17:33] epinow: Examine the pairs() plot to diagnose sampling problems
-
+
+
+
+
+
+Using an appropriate number of samples and chains is crucial for
+ensuring convergence and obtaining reliable estimates in Bayesian
+computations using Stan. Inadequate sampling or insufficient chains may
+lead to issues such as divergent transitions, impacting the accuracy and
+stability of the inference process.
+For the purpose of this tutorial, we can add more configuration
+details to get an useful output in less time. You can specify a fixed
+number of samples and chains to the
+stan argument using the stan_opts()
+function:
+The code in the previous chunk can take around 10 minutes. We expect
+this chunk below to take approximately 3 minutes:
+
+R
+
+
+estimates <- epinow(
+ # same code as previous chunk
+ reported_cases = reported_cases,
+ generation_time = generation_time_opts(generation_time_fixed),
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ ),
+ # [new] set a fixed number of samples and chains
+ stan = stan_opts(samples = 1000, chains = 3)
+)
+
+
+
+
+
Results
We can extract and visualise estimates of the effective reproduction
number through time:
-
+
R
estimates$plots$R
-
The uncertainty in the estimates increases through time. This is
+
The uncertainty in the estimates increases through time. This is
because estimates are informed by data in the past - within the delay
periods. This difference in uncertainty is categorised into
Estimate (green) utilises all data and Estimate
@@ -783,15 +827,15 @@ RForecast
(purple) is a projection ahead of time.
We can also visualise the growth rate estimate through time:
-
+
R
estimates$plots$growth_rate
-To extract a summary of the key transmission metrics at the
+
To extract a summary of the key transmission metrics at the
latest date in the data:
-
+
R
@@ -802,24 +846,24 @@ OUTPUT<
measure estimate
<char> <char>
-1: New confirmed cases by infection date 7232 (4047 -- 13301)
+1: New confirmed cases by infection date 7097 (3893 -- 12496)
2: Expected change in daily cases Likely decreasing
-3: Effective reproduction no. 0.89 (0.58 -- 1.4)
-4: Rate of growth -0.015 (-0.063 -- 0.043)
-5: Doubling/halving time (days) -47 (16 -- -11)
+3: Effective reproduction no. 0.88 (0.55 -- 1.3)
+4: Rate of growth -0.016 (-0.068 -- 0.037)
+5: Doubling/halving time (days) -42 (19 -- -10)
As these estimates are based on partial data, they have a wide
uncertainty interval.
From the summary of our analysis we see that the expected change
in daily cases is Likely decreasing with the estimated new confirmed
-cases 7232 (4047 – 13301).
The effective reproduction number \(R_t\) estimate (on the last date of the
-data) is 0.89 (0.58 – 1.4).
The exponential growth rate of case numbers is -0.015 (-0.063 –
-0.043).
The exponential growth rate of case numbers is -0.016 (-0.068 –
+0.037).
The doubling time (the time taken for case numbers to double) is
--47 (16 – -11).
@@ -884,7 +928,7 @@
confirm : number of confirmed cases for a region on a
given date.
-
+
R
@@ -912,33 +956,37 @@ OUTPUT<
To find regional estimates, we use the same inputs as
epinow() to the function
regional_epinow():
-
+
R
estimates_regional <- regional_epinow(
reported_cases = regional_cases,
generation_time = generation_time_opts(generation_time_fixed),
- delays = delay_opts(incubation_period_fixed + reporting_delay_fixed),
- rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd))
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ )
)
OUTPUT
-INFO [2024-03-28 21:35:04] Producing following optional outputs: regions, summary, samples, plots, latest
-INFO [2024-03-28 21:35:04] Reporting estimates using data up to: 2020-04-28
-INFO [2024-03-28 21:35:04] No target directory specified so returning output
-INFO [2024-03-28 21:35:04] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
-INFO [2024-03-28 21:35:04] Regions excluded: none
-INFO [2024-03-28 22:21:26] Completed regional estimates
-INFO [2024-03-28 22:21:26] Regions with estimates: 13
-INFO [2024-03-28 22:21:26] Regions with runtime errors: 0
-INFO [2024-03-28 22:21:26] Producing summary
-INFO [2024-03-28 22:21:26] No summary directory specified so returning summary output
-INFO [2024-03-28 22:21:26] No target directory specified so returning timings
+INFO [2024-03-30 17:17:38] Producing following optional outputs: regions, summary, samples, plots, latest
+INFO [2024-03-30 17:17:38] Reporting estimates using data up to: 2020-04-28
+INFO [2024-03-30 17:17:38] No target directory specified so returning output
+INFO [2024-03-30 17:17:38] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
+INFO [2024-03-30 17:17:38] Regions excluded: none
+INFO [2024-03-30 18:03:05] Completed regional estimates
+INFO [2024-03-30 18:03:05] Regions with estimates: 13
+INFO [2024-03-30 18:03:05] Regions with runtime errors: 0
+INFO [2024-03-30 18:03:05] Producing summary
+INFO [2024-03-30 18:03:05] No summary directory specified so returning summary output
+INFO [2024-03-30 18:03:06] No target directory specified so returning timings
-
+
R
@@ -949,57 +997,57 @@ OUTPUT<
Region New confirmed cases by infection date
<char> <char>
- 1: East Midlands 347 (216 -- 568)
- 2: East of England 540 (331 -- 840)
- 3: England 3621 (2184 -- 5730)
- 4: London 293 (191 -- 456)
- 5: North East 251 (144 -- 412)
- 6: North West 561 (326 -- 869)
- 7: Northern Ireland 44 (24 -- 85)
- 8: Scotland 288 (160 -- 538)
- 9: South East 588 (343 -- 975)
-10: South West 415 (290 -- 598)
-11: Wales 94 (63 -- 134)
-12: West Midlands 272 (145 -- 495)
-13: Yorkshire and The Humber 476 (280 -- 787)
+ 1: East Midlands 347 (218 -- 566)
+ 2: East of England 550 (336 -- 853)
+ 3: England 3522 (2184 -- 5621)
+ 4: London 300 (187 -- 468)
+ 5: North East 253 (145 -- 416)
+ 6: North West 550 (337 -- 874)
+ 7: Northern Ireland 44 (23 -- 89)
+ 8: Scotland 283 (160 -- 531)
+ 9: South East 592 (354 -- 986)
+10: South West 420 (290 -- 613)
+11: Wales 95 (67 -- 138)
+12: West Midlands 272 (144 -- 493)
+13: Yorkshire and The Humber 481 (291 -- 770)
Expected change in daily cases Effective reproduction no.
<fctr> <char>
1: Likely increasing 1.2 (0.87 -- 1.6)
- 2: Likely increasing 1.2 (0.83 -- 1.6)
- 3: Likely decreasing 0.92 (0.63 -- 1.3)
- 4: Likely decreasing 0.78 (0.56 -- 1.1)
- 5: Likely decreasing 0.91 (0.59 -- 1.3)
- 6: Likely decreasing 0.87 (0.58 -- 1.2)
- 7: Likely decreasing 0.66 (0.4 -- 1.1)
- 8: Likely decreasing 0.91 (0.58 -- 1.4)
- 9: Stable 0.99 (0.66 -- 1.4)
+ 2: Likely increasing 1.2 (0.85 -- 1.6)
+ 3: Likely decreasing 0.91 (0.63 -- 1.3)
+ 4: Likely decreasing 0.8 (0.55 -- 1.1)
+ 5: Likely decreasing 0.91 (0.6 -- 1.3)
+ 6: Likely decreasing 0.85 (0.6 -- 1.2)
+ 7: Likely decreasing 0.64 (0.38 -- 1.1)
+ 8: Likely decreasing 0.9 (0.6 -- 1.4)
+ 9: Stable 0.99 (0.68 -- 1.4)
10: Increasing 1.4 (1.1 -- 1.8)
-11: Decreasing 0.56 (0.41 -- 0.74)
-12: Likely decreasing 0.71 (0.42 -- 1.1)
-13: Stable 1 (0.69 -- 1.4)
+11: Decreasing 0.57 (0.42 -- 0.76)
+12: Likely decreasing 0.71 (0.43 -- 1.1)
+13: Stable 1 (0.71 -- 1.4)
Rate of growth Doubling/halving time (days)
<char> <char>
- 1: 0.024 (-0.018 -- 0.069) 29 (10 -- -39)
- 2: 0.022 (-0.023 -- 0.066) 31 (11 -- -30)
- 3: -0.01 (-0.054 -- 0.033) -68 (21 -- -13)
- 4: -0.03 (-0.066 -- 0.01) -23 (67 -- -11)
- 5: -0.012 (-0.06 -- 0.034) -57 (20 -- -12)
- 6: -0.017 (-0.063 -- 0.022) -40 (32 -- -11)
- 7: -0.05 (-0.097 -- 0.0067) -14 (100 -- -7.1)
- 8: -0.012 (-0.062 -- 0.049) -58 (14 -- -11)
- 9: -0.0017 (-0.049 -- 0.049) -410 (14 -- -14)
-10: 0.046 (0.013 -- 0.085) 15 (8.2 -- 55)
-11: -0.065 (-0.095 -- -0.036) -11 (-19 -- -7.3)
-12: -0.041 (-0.092 -- 0.014) -17 (50 -- -7.5)
-13: 0.0025 (-0.045 -- 0.051) 270 (14 -- -15)
-
-
+ 1: 0.024 (-0.018 -- 0.07) 29 (9.9 -- -40)
+ 2: 0.024 (-0.021 -- 0.065) 28 (11 -- -34)
+ 3: -0.012 (-0.054 -- 0.031) -58 (22 -- -13)
+ 4: -0.028 (-0.068 -- 0.013) -25 (55 -- -10)
+ 5: -0.012 (-0.059 -- 0.035) -57 (20 -- -12)
+ 6: -0.02 (-0.06 -- 0.023) -35 (30 -- -12)
+ 7: -0.052 (-0.1 -- 0.012) -13 (58 -- -6.8)
+ 8: -0.013 (-0.059 -- 0.045) -52 (15 -- -12)
+ 9: -0.0012 (-0.046 -- 0.048) -560 (15 -- -15)
+10: 0.047 (0.013 -- 0.086) 15 (8.1 -- 54)
+11: -0.065 (-0.092 -- -0.033) -11 (-21 -- -7.6)
+12: -0.042 (-0.091 -- 0.015) -17 (47 -- -7.6)
+13: 0.0029 (-0.041 -- 0.049) 240 (14 -- -17)
+
+
R
estimates_regional$summary$plots$R
-
Summary
+
Summary
EpiNow2 can be used to estimate transmission metrics
@@ -1427,7 +1475,7 @@
Challenge
HINT : data format
-
+
Ensure the data is in the correct format :
@@ -1446,7 +1494,7 @@ HINT : data format
-
+
To estimate the effective reproduction number and growth rate, we
will use the function epinow().
@@ -1883,7 +1931,7 @@ Challenge
Give me a hint
-
+
Inspect the format of the data input.
@@ -1901,7 +1949,7 @@ Give me a hint
-
+
We can use dplyr::rename() to adapt the external data to
fit the data input for cfr_static().
@@ -1967,7 +2015,7 @@ Two biases that affect CFR estimation
Preferential ascertainment of severe cases
-
+
For diseases with a spectrum of clinical presentation, those
cases that come to the attention of public health authorities and
@@ -1987,7 +2035,7 @@
Preferential ascertainment of
-
+
During an ongoing epidemic, there is a delay between the
time someone dies and the time their death is reported. Therefore, at
@@ -2118,7 +2166,7 @@
OUTPUT<
How does {cfr} works?
-
+
To adjust the CFR, Nishiura
et al., 2009 use the case and death incidence data to estimate the
@@ -2191,7 +2239,7 @@
Use the<epidist>class
When to use discrete distributions?
-
+
For cfr_static() and all the cfr_*() family
of functions, the most appropriate choice to pass are
@@ -2235,7 +2283,7 @@
Challenge
Give me a hint
-
+
- Find the appropriate
<epidist> object!
@@ -2249,7 +2297,7 @@ Give me a hint
-
+
R
@@ -2310,7 +2358,7 @@ OUTPUT<
What is the sampling process?
-
+
The population of confirmed cases and sampling
process for estimating the unbiased CFR during the course of an
@@ -2367,7 +2415,7 @@
CFR estimation during SARS outbreak, Hong Kong, 2003
-
+
Figures A and B show the cumulative numbers of cases and deaths of
SARS, and Figure C shows the observed (biased) CFR estimates as a
@@ -2532,7 +2580,7 @@
Interpret the early-stage CFR estimate
Give me a hint
-
+
We can use either visual inspection or analysis of the output data
frames.
@@ -2545,7 +2593,7 @@ Give me a hint
-
+
There is almost one month of difference.
Note that the estimate has considerable uncertainty at the beginning
@@ -2563,7 +2611,7 @@
Show me the solution
rolling or time-varying?
-
+
The cfr package has a function called
cfr_time_varying() with different functionality that makes
@@ -2644,7 +2692,7 @@
Severity heterogeneity
Give me a hint
-
+
One way to do a stratified analysis is to apply a model to
nested data. This {tidyr}
@@ -2660,7 +2708,7 @@ Give me a hint
-
+
R
@@ -2857,7 +2905,7 @@ Hospitalisation Fatality Risk (HFR)
Give me a hint
-
+
Use covidregionaldata::get_regional_data() to get COVID
data from the region England from the country UK.
@@ -2883,7 +2931,7 @@ R
Show me the solution
-
+
R
diff --git a/create-forecast.html b/create-forecast.html
index 32b775ea..36bbe78b 100644
--- a/create-forecast.html
+++ b/create-forecast.html
@@ -632,7 +632,7 @@ Challenge
HINT : data format
-
+
Ensure the data is in the correct format :
-
@@ -649,7 +649,7 @@
HINT : data format
-
+
To estimate the effective reproduction number and growth rate, we
will use the function epinow().
diff --git a/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png b/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png
index cdc42cdd..7b51c170 100644
Binary files a/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png and b/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png differ
diff --git a/fig/quantify-transmissibility-rendered-unnamed-chunk-17-1.png b/fig/quantify-transmissibility-rendered-unnamed-chunk-17-1.png
new file mode 100644
index 00000000..c1cb171d
Binary files /dev/null and b/fig/quantify-transmissibility-rendered-unnamed-chunk-17-1.png differ
diff --git a/fig/quantify-transmissibility-rendered-unnamed-chunk-20-1.png b/fig/quantify-transmissibility-rendered-unnamed-chunk-20-1.png
new file mode 100644
index 00000000..19d4423d
Binary files /dev/null and b/fig/quantify-transmissibility-rendered-unnamed-chunk-20-1.png differ
diff --git a/images.html b/images.html
index 18283750..c3c5b4a6 100644
--- a/images.html
+++ b/images.html
@@ -281,11 +281,11 @@ Figure 1
Figure 2
Figure 3
-
-Figure 4
+Figure 4
+
Figure 5
-
Create a short-term forecast
+
Create a short-term forecast
Figure 1
diff --git a/instructor/aio.html b/instructor/aio.html
index 496555f4..63254f13 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -278,7 +278,7 @@
Content from Quantifying transmission
-Last updated on 2024-03-28 |
+
Last updated on 2024-03-30 |
Edit this page
Estimated time: 30 minutes
@@ -398,7 +398,7 @@ Bayesian inference
Instructor Note
-
+
We refer to the Prior probability distribution and the Posterior
probability distribution.
@@ -423,7 +423,7 @@ R
Instructor Note
-
+
This tutorial illustrates the usage of epinow() to
estimate the time-varying reproduction number and infection times.
@@ -447,32 +447,27 @@
Case dataR
-head(incidence2::covidregionaldataUK)
+dplyr::as_tibble(incidence2::covidregionaldataUK)
OUTPUT
- date region region_code cases_new cases_total deaths_new
-1 2020-01-30 East Midlands E12000004 NA NA NA
-2 2020-01-30 East of England E12000006 NA NA NA
-3 2020-01-30 England E92000001 2 2 NA
-4 2020-01-30 London E12000007 NA NA NA
-5 2020-01-30 North East E12000001 NA NA NA
-6 2020-01-30 North West E12000002 NA NA NA
- deaths_total recovered_new recovered_total hosp_new hosp_total tested_new
-1 NA NA NA NA NA NA
-2 NA NA NA NA NA NA
-3 NA NA NA NA NA NA
-4 NA NA NA NA NA NA
-5 NA NA NA NA NA NA
-6 NA NA NA NA NA NA
- tested_total
-1 NA
-2 NA
-3 NA
-4 NA
-5 NA
-6 NA
+# A tibble: 6,370 × 13
+ date region region_code cases_new cases_total deaths_new deaths_total
+ <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
+ 1 2020-01-30 East Mi… E12000004 NA NA NA NA
+ 2 2020-01-30 East of… E12000006 NA NA NA NA
+ 3 2020-01-30 England E92000001 2 2 NA NA
+ 4 2020-01-30 London E12000007 NA NA NA NA
+ 5 2020-01-30 North E… E12000001 NA NA NA NA
+ 6 2020-01-30 North W… E12000002 NA NA NA NA
+ 7 2020-01-30 Norther… N92000002 NA NA NA NA
+ 8 2020-01-30 Scotland S92000003 NA NA NA NA
+ 9 2020-01-30 South E… E12000008 NA NA NA NA
+10 2020-01-30 South W… E12000009 NA NA NA NA
+# ℹ 6,360 more rows
+# ℹ 6 more variables: recovered_new <dbl>, recovered_total <dbl>,
+# hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>, tested_total <dbl>
To use the data, we must format the data to have two columns:
@@ -482,18 +477,20 @@ OUTPUT<
-
confirm : number of confirmed cases on that date.
+Let’s use dplyr for this:
R
-cases <- aggregate(
- cases_new ~ date,
- data = incidence2::covidregionaldataUK[, c("date", "cases_new")],
- FUN = sum
-)
-colnames(cases) <- c("date", "confirm")
+library(dplyr)
+
+cases <- incidence2::covidregionaldataUK %>%
+ select(date, cases_new) %>%
+ group_by(date) %>%
+ summarise(confirm = sum(cases_new, na.rm = TRUE)) %>%
+ ungroup()
-There are case data available for 489 days, but in an outbreak
+
There are case data available for 490 days, but in an outbreak
situation it is likely we would only have access to the beginning of
this data set. Therefore we assume we only have the first 90 days of
this data.
@@ -542,18 +539,18 @@ Delays and data
+Time of symptom onset
+Incubation period
+
+
Time of case report
Incubation period + time from symptom onset to case
notification
-
+
Time of hospitalisation
Incubation period + time from symptom onset to hospitalisation
-
-Time of symptom onset
-Incubation period
-
@@ -781,34 +778,81 @@ R
reported_cases <- cases[1:90, ]
+
estimates <- epinow(
reported_cases = reported_cases,
generation_time = generation_time_opts(generation_time_fixed),
- delays = delay_opts(incubation_period_fixed + reporting_delay_fixed),
- rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd))
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ )
)
OUTPUT
-WARN [2024-03-28 21:34:59] epinow: There were 8 divergent transitions after warmup. See
+WARN [2024-03-30 17:17:33] epinow: There were 1 divergent transitions after warmup. See
https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
to find out why this is a problem and how to eliminate them. -
-WARN [2024-03-28 21:34:59] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-03-30 17:17:33] epinow: Examine the pairs() plot to diagnose sampling problems
-
+
+
+
+
+
+Using an appropriate number of samples and chains is crucial for
+ensuring convergence and obtaining reliable estimates in Bayesian
+computations using Stan. Inadequate sampling or insufficient chains may
+lead to issues such as divergent transitions, impacting the accuracy and
+stability of the inference process.
+For the purpose of this tutorial, we can add more configuration
+details to get an useful output in less time. You can specify a fixed
+number of samples and chains to the
+stan argument using the stan_opts()
+function:
+The code in the previous chunk can take around 10 minutes. We expect
+this chunk below to take approximately 3 minutes:
+
+R
+
+
+estimates <- epinow(
+ # same code as previous chunk
+ reported_cases = reported_cases,
+ generation_time = generation_time_opts(generation_time_fixed),
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ ),
+ # [new] set a fixed number of samples and chains
+ stan = stan_opts(samples = 1000, chains = 3)
+)
+
+
+
+
+
Results
We can extract and visualise estimates of the effective reproduction
number through time:
-
+
R
estimates$plots$R
-
The uncertainty in the estimates increases through time. This is
+
The uncertainty in the estimates increases through time. This is
because estimates are informed by data in the past - within the delay
periods. This difference in uncertainty is categorised into
Estimate (green) utilises all data and Estimate
@@ -818,15 +862,15 @@ RForecast
(purple) is a projection ahead of time.
We can also visualise the growth rate estimate through time:
-
+
R
estimates$plots$growth_rate
-To extract a summary of the key transmission metrics at the
+
To extract a summary of the key transmission metrics at the
latest date in the data:
-
+
R
@@ -837,24 +881,24 @@ OUTPUT<
measure estimate
<char> <char>
-1: New confirmed cases by infection date 7232 (4047 -- 13301)
+1: New confirmed cases by infection date 7097 (3893 -- 12496)
2: Expected change in daily cases Likely decreasing
-3: Effective reproduction no. 0.89 (0.58 -- 1.4)
-4: Rate of growth -0.015 (-0.063 -- 0.043)
-5: Doubling/halving time (days) -47 (16 -- -11)
+3: Effective reproduction no. 0.88 (0.55 -- 1.3)
+4: Rate of growth -0.016 (-0.068 -- 0.037)
+5: Doubling/halving time (days) -42 (19 -- -10)
As these estimates are based on partial data, they have a wide
uncertainty interval.
From the summary of our analysis we see that the expected change
in daily cases is Likely decreasing with the estimated new confirmed
-cases 7232 (4047 – 13301).
The effective reproduction number \(R_t\) estimate (on the last date of the
-data) is 0.89 (0.58 – 1.4).
The exponential growth rate of case numbers is -0.015 (-0.063 –
-0.043).
The exponential growth rate of case numbers is -0.016 (-0.068 –
+0.037).
The doubling time (the time taken for case numbers to double) is
--47 (16 – -11).
@@ -919,7 +963,7 @@
confirm : number of confirmed cases for a region on a
given date.
-
+
R
@@ -947,33 +991,37 @@ OUTPUT<
To find regional estimates, we use the same inputs as
epinow() to the function
regional_epinow():
-
+
R
estimates_regional <- regional_epinow(
reported_cases = regional_cases,
generation_time = generation_time_opts(generation_time_fixed),
- delays = delay_opts(incubation_period_fixed + reporting_delay_fixed),
- rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd))
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ )
)
OUTPUT
-INFO [2024-03-28 21:35:04] Producing following optional outputs: regions, summary, samples, plots, latest
-INFO [2024-03-28 21:35:04] Reporting estimates using data up to: 2020-04-28
-INFO [2024-03-28 21:35:04] No target directory specified so returning output
-INFO [2024-03-28 21:35:04] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
-INFO [2024-03-28 21:35:04] Regions excluded: none
-INFO [2024-03-28 22:21:26] Completed regional estimates
-INFO [2024-03-28 22:21:26] Regions with estimates: 13
-INFO [2024-03-28 22:21:26] Regions with runtime errors: 0
-INFO [2024-03-28 22:21:26] Producing summary
-INFO [2024-03-28 22:21:26] No summary directory specified so returning summary output
-INFO [2024-03-28 22:21:26] No target directory specified so returning timings
+INFO [2024-03-30 17:17:38] Producing following optional outputs: regions, summary, samples, plots, latest
+INFO [2024-03-30 17:17:38] Reporting estimates using data up to: 2020-04-28
+INFO [2024-03-30 17:17:38] No target directory specified so returning output
+INFO [2024-03-30 17:17:38] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
+INFO [2024-03-30 17:17:38] Regions excluded: none
+INFO [2024-03-30 18:03:05] Completed regional estimates
+INFO [2024-03-30 18:03:05] Regions with estimates: 13
+INFO [2024-03-30 18:03:05] Regions with runtime errors: 0
+INFO [2024-03-30 18:03:05] Producing summary
+INFO [2024-03-30 18:03:05] No summary directory specified so returning summary output
+INFO [2024-03-30 18:03:06] No target directory specified so returning timings
-
+
R
@@ -984,57 +1032,57 @@ OUTPUT<
Region New confirmed cases by infection date
<char> <char>
- 1: East Midlands 347 (216 -- 568)
- 2: East of England 540 (331 -- 840)
- 3: England 3621 (2184 -- 5730)
- 4: London 293 (191 -- 456)
- 5: North East 251 (144 -- 412)
- 6: North West 561 (326 -- 869)
- 7: Northern Ireland 44 (24 -- 85)
- 8: Scotland 288 (160 -- 538)
- 9: South East 588 (343 -- 975)
-10: South West 415 (290 -- 598)
-11: Wales 94 (63 -- 134)
-12: West Midlands 272 (145 -- 495)
-13: Yorkshire and The Humber 476 (280 -- 787)
+ 1: East Midlands 347 (218 -- 566)
+ 2: East of England 550 (336 -- 853)
+ 3: England 3522 (2184 -- 5621)
+ 4: London 300 (187 -- 468)
+ 5: North East 253 (145 -- 416)
+ 6: North West 550 (337 -- 874)
+ 7: Northern Ireland 44 (23 -- 89)
+ 8: Scotland 283 (160 -- 531)
+ 9: South East 592 (354 -- 986)
+10: South West 420 (290 -- 613)
+11: Wales 95 (67 -- 138)
+12: West Midlands 272 (144 -- 493)
+13: Yorkshire and The Humber 481 (291 -- 770)
Expected change in daily cases Effective reproduction no.
<fctr> <char>
1: Likely increasing 1.2 (0.87 -- 1.6)
- 2: Likely increasing 1.2 (0.83 -- 1.6)
- 3: Likely decreasing 0.92 (0.63 -- 1.3)
- 4: Likely decreasing 0.78 (0.56 -- 1.1)
- 5: Likely decreasing 0.91 (0.59 -- 1.3)
- 6: Likely decreasing 0.87 (0.58 -- 1.2)
- 7: Likely decreasing 0.66 (0.4 -- 1.1)
- 8: Likely decreasing 0.91 (0.58 -- 1.4)
- 9: Stable 0.99 (0.66 -- 1.4)
+ 2: Likely increasing 1.2 (0.85 -- 1.6)
+ 3: Likely decreasing 0.91 (0.63 -- 1.3)
+ 4: Likely decreasing 0.8 (0.55 -- 1.1)
+ 5: Likely decreasing 0.91 (0.6 -- 1.3)
+ 6: Likely decreasing 0.85 (0.6 -- 1.2)
+ 7: Likely decreasing 0.64 (0.38 -- 1.1)
+ 8: Likely decreasing 0.9 (0.6 -- 1.4)
+ 9: Stable 0.99 (0.68 -- 1.4)
10: Increasing 1.4 (1.1 -- 1.8)
-11: Decreasing 0.56 (0.41 -- 0.74)
-12: Likely decreasing 0.71 (0.42 -- 1.1)
-13: Stable 1 (0.69 -- 1.4)
+11: Decreasing 0.57 (0.42 -- 0.76)
+12: Likely decreasing 0.71 (0.43 -- 1.1)
+13: Stable 1 (0.71 -- 1.4)
Rate of growth Doubling/halving time (days)
<char> <char>
- 1: 0.024 (-0.018 -- 0.069) 29 (10 -- -39)
- 2: 0.022 (-0.023 -- 0.066) 31 (11 -- -30)
- 3: -0.01 (-0.054 -- 0.033) -68 (21 -- -13)
- 4: -0.03 (-0.066 -- 0.01) -23 (67 -- -11)
- 5: -0.012 (-0.06 -- 0.034) -57 (20 -- -12)
- 6: -0.017 (-0.063 -- 0.022) -40 (32 -- -11)
- 7: -0.05 (-0.097 -- 0.0067) -14 (100 -- -7.1)
- 8: -0.012 (-0.062 -- 0.049) -58 (14 -- -11)
- 9: -0.0017 (-0.049 -- 0.049) -410 (14 -- -14)
-10: 0.046 (0.013 -- 0.085) 15 (8.2 -- 55)
-11: -0.065 (-0.095 -- -0.036) -11 (-19 -- -7.3)
-12: -0.041 (-0.092 -- 0.014) -17 (50 -- -7.5)
-13: 0.0025 (-0.045 -- 0.051) 270 (14 -- -15)
-
-
+ 1: 0.024 (-0.018 -- 0.07) 29 (9.9 -- -40)
+ 2: 0.024 (-0.021 -- 0.065) 28 (11 -- -34)
+ 3: -0.012 (-0.054 -- 0.031) -58 (22 -- -13)
+ 4: -0.028 (-0.068 -- 0.013) -25 (55 -- -10)
+ 5: -0.012 (-0.059 -- 0.035) -57 (20 -- -12)
+ 6: -0.02 (-0.06 -- 0.023) -35 (30 -- -12)
+ 7: -0.052 (-0.1 -- 0.012) -13 (58 -- -6.8)
+ 8: -0.013 (-0.059 -- 0.045) -52 (15 -- -12)
+ 9: -0.0012 (-0.046 -- 0.048) -560 (15 -- -15)
+10: 0.047 (0.013 -- 0.086) 15 (8.1 -- 54)
+11: -0.065 (-0.092 -- -0.033) -11 (-21 -- -7.6)
+12: -0.042 (-0.091 -- 0.015) -17 (47 -- -7.6)
+13: 0.0029 (-0.041 -- 0.049) 240 (14 -- -17)
+
+
R
estimates_regional$summary$plots$R
-
Summary
+
Summary
EpiNow2 can be used to estimate transmission metrics
@@ -1463,7 +1511,7 @@
Challenge
HINT : data format
-
+
Ensure the data is in the correct format :
@@ -1482,7 +1530,7 @@ HINT : data format
-
+
To estimate the effective reproduction number and growth rate, we
will use the function epinow().
@@ -1708,7 +1756,7 @@ Prerequisites
Instructor Note
-
+
The periods are relevant: Period 1 – 15 days where CFR is zero to
indicate this is due to no reported deaths; Period from Mar 15 – Apr 26
@@ -1936,7 +1984,7 @@
Challenge
Give me a hint
-
+
Inspect the format of the data input.
@@ -1954,7 +2002,7 @@ Give me a hint
-
+
We can use dplyr::rename() to adapt the external data to
fit the data input for cfr_static().
@@ -2020,7 +2068,7 @@ Two biases that affect CFR estimation
Preferential ascertainment of severe cases
-
+
For diseases with a spectrum of clinical presentation, those
cases that come to the attention of public health authorities and
@@ -2040,7 +2088,7 @@
Preferential ascertainment of
-
+
During an ongoing epidemic, there is a delay between the
time someone dies and the time their death is reported. Therefore, at
@@ -2065,7 +2113,7 @@
Bias due to delayed reporting
Instructor Note
-
+
We can showcase this last bias using the concept
described in this {cfr} vignette.
@@ -2185,7 +2233,7 @@ OUTPUT<
How does {cfr} works?
-
+
To adjust the CFR, Nishiura
et al., 2009 use the case and death incidence data to estimate the
@@ -2258,7 +2306,7 @@
Use the<epidist>class
When to use discrete distributions?
-
+
For cfr_static() and all the cfr_*() family
of functions, the most appropriate choice to pass are
@@ -2302,7 +2350,7 @@
Challenge
Give me a hint
-
+
- Find the appropriate
<epidist> object!
@@ -2316,7 +2364,7 @@ Give me a hint
-
+
R
@@ -2377,7 +2425,7 @@ OUTPUT<
What is the sampling process?
-
+
The population of confirmed cases and sampling
process for estimating the unbiased CFR during the course of an
@@ -2434,7 +2482,7 @@
CFR estimation during SARS outbreak, Hong Kong, 2003
-
+
Figures A and B show the cumulative numbers of cases and deaths of
SARS, and Figure C shows the observed (biased) CFR estimates as a
@@ -2599,7 +2647,7 @@
Interpret the early-stage CFR estimate
Give me a hint
-
+
We can use either visual inspection or analysis of the output data
frames.
@@ -2612,7 +2660,7 @@ Give me a hint
-
+
There is almost one month of difference.
Note that the estimate has considerable uncertainty at the beginning
@@ -2630,7 +2678,7 @@
Show me the solution
rolling or time-varying?
-
+
The cfr package has a function called
cfr_time_varying() with different functionality that makes
@@ -2711,7 +2759,7 @@
Severity heterogeneity
Give me a hint
-
+
One way to do a stratified analysis is to apply a model to
nested data. This {tidyr}
@@ -2727,7 +2775,7 @@ Give me a hint
-
+
R
@@ -2924,7 +2972,7 @@ Hospitalisation Fatality Risk (HFR)
Give me a hint
-
+
Use covidregionaldata::get_regional_data() to get COVID
data from the region England from the country UK.
@@ -2950,7 +2998,7 @@ R
Show me the solution
-
+
R
diff --git a/instructor/create-forecast.html b/instructor/create-forecast.html
index 2213a79a..1584066a 100644
--- a/instructor/create-forecast.html
+++ b/instructor/create-forecast.html
@@ -634,7 +634,7 @@ Challenge
HINT : data format
-
+
Ensure the data is in the correct format :
-
@@ -651,7 +651,7 @@
HINT : data format
-
+
To estimate the effective reproduction number and growth rate, we
will use the function epinow().
diff --git a/instructor/images.html b/instructor/images.html
index 432b597c..b4f49d0c 100644
--- a/instructor/images.html
+++ b/instructor/images.html
@@ -283,11 +283,11 @@ Figure 1
Figure 2
Figure 3
-
-Figure 4
+Figure 4
+
Figure 5
-
Create a short-term forecast
+
Create a short-term forecast
Figure 1
diff --git a/instructor/index.html b/instructor/index.html
index f1d8109e..197a0ea9 100644
--- a/instructor/index.html
+++ b/instructor/index.html
@@ -350,7 +350,7 @@
reported_cases <- cases[1:90, ]
+
estimates <- epinow(
reported_cases = reported_cases,
generation_time = generation_time_opts(generation_time_fixed),
- delays = delay_opts(incubation_period_fixed + reporting_delay_fixed),
- rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd))
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ )
)
OUTPUT
-WARN [2024-03-28 21:34:59] epinow: There were 8 divergent transitions after warmup. See
+WARN [2024-03-30 17:17:33] epinow: There were 1 divergent transitions after warmup. See
https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
to find out why this is a problem and how to eliminate them. -
-WARN [2024-03-28 21:34:59] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-03-30 17:17:33] epinow: Examine the pairs() plot to diagnose sampling problems
-
Using an appropriate number of samples and chains is crucial for +ensuring convergence and obtaining reliable estimates in Bayesian +computations using Stan. Inadequate sampling or insufficient chains may +lead to issues such as divergent transitions, impacting the accuracy and +stability of the inference process.
+For the purpose of this tutorial, we can add more configuration
+details to get an useful output in less time. You can specify a fixed
+number of samples and chains to the
+stan argument using the stan_opts()
+function:
The code in the previous chunk can take around 10 minutes. We expect +this chunk below to take approximately 3 minutes:
+R +
+
+estimates <- epinow(
+ # same code as previous chunk
+ reported_cases = reported_cases,
+ generation_time = generation_time_opts(generation_time_fixed),
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ ),
+ # [new] set a fixed number of samples and chains
+ stan = stan_opts(samples = 1000, chains = 3)
+)
+Results
We can extract and visualise estimates of the effective reproduction number through time:
-R
estimates$plots$R
The uncertainty in the estimates increases through time. This is
+
The uncertainty in the estimates increases through time. This is
because estimates are informed by data in the past - within the delay
periods. This difference in uncertainty is categorised into
Estimate (green) utilises all data and Estimate
@@ -783,15 +827,15 @@ RForecast
(purple) is a projection ahead of time.
We can also visualise the growth rate estimate through time:
-R
estimates$plots$growth_rate
To extract a summary of the key transmission metrics at the
+
To extract a summary of the key transmission metrics at the latest date in the data:
-R
@@ -802,24 +846,24 @@OUTPUT<
+3: Effective reproduction no. 0.88 (0.55 -- 1.3) +4: Rate of growth -0.016 (-0.068 -- 0.037) +5: Doubling/halving time (days) -42 (19 -- -10)measure estimate <char> <char> -1: New confirmed cases by infection date 7232 (4047 -- 13301) +1: New confirmed cases by infection date 7097 (3893 -- 12496) 2: Expected change in daily cases Likely decreasing -3: Effective reproduction no. 0.89 (0.58 -- 1.4) -4: Rate of growth -0.015 (-0.063 -- 0.043) -5: Doubling/halving time (days) -47 (16 -- -11)
As these estimates are based on partial data, they have a wide uncertainty interval.
From the summary of our analysis we see that the expected change in daily cases is Likely decreasing with the estimated new confirmed -cases 7232 (4047 – 13301).
+cases 7097 (3893 – 12496).
The effective reproduction number \(R_t\) estimate (on the last date of the -data) is 0.89 (0.58 – 1.4).
-The exponential growth rate of case numbers is -0.015 (-0.063 – -0.043).
+data) is 0.88 (0.55 – 1.3).
+The exponential growth rate of case numbers is -0.016 (-0.068 – +0.037).
The doubling time (the time taken for case numbers to double) is --47 (16 – -11).
+-42 (19 – -10).
confirm : number of confirmed cases for a region on a
given date.
-
+
R
@@ -912,33 +956,37 @@ OUTPUT<
To find regional estimates, we use the same inputs as
epinow() to the function
regional_epinow():
-
+
R
estimates_regional <- regional_epinow(
reported_cases = regional_cases,
generation_time = generation_time_opts(generation_time_fixed),
- delays = delay_opts(incubation_period_fixed + reporting_delay_fixed),
- rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd))
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ )
)
OUTPUT
-INFO [2024-03-28 21:35:04] Producing following optional outputs: regions, summary, samples, plots, latest
-INFO [2024-03-28 21:35:04] Reporting estimates using data up to: 2020-04-28
-INFO [2024-03-28 21:35:04] No target directory specified so returning output
-INFO [2024-03-28 21:35:04] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
-INFO [2024-03-28 21:35:04] Regions excluded: none
-INFO [2024-03-28 22:21:26] Completed regional estimates
-INFO [2024-03-28 22:21:26] Regions with estimates: 13
-INFO [2024-03-28 22:21:26] Regions with runtime errors: 0
-INFO [2024-03-28 22:21:26] Producing summary
-INFO [2024-03-28 22:21:26] No summary directory specified so returning summary output
-INFO [2024-03-28 22:21:26] No target directory specified so returning timings
+INFO [2024-03-30 17:17:38] Producing following optional outputs: regions, summary, samples, plots, latest
+INFO [2024-03-30 17:17:38] Reporting estimates using data up to: 2020-04-28
+INFO [2024-03-30 17:17:38] No target directory specified so returning output
+INFO [2024-03-30 17:17:38] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
+INFO [2024-03-30 17:17:38] Regions excluded: none
+INFO [2024-03-30 18:03:05] Completed regional estimates
+INFO [2024-03-30 18:03:05] Regions with estimates: 13
+INFO [2024-03-30 18:03:05] Regions with runtime errors: 0
+INFO [2024-03-30 18:03:05] Producing summary
+INFO [2024-03-30 18:03:05] No summary directory specified so returning summary output
+INFO [2024-03-30 18:03:06] No target directory specified so returning timings
-
+
R
@@ -949,57 +997,57 @@ OUTPUT<
Region New confirmed cases by infection date
<char> <char>
- 1: East Midlands 347 (216 -- 568)
- 2: East of England 540 (331 -- 840)
- 3: England 3621 (2184 -- 5730)
- 4: London 293 (191 -- 456)
- 5: North East 251 (144 -- 412)
- 6: North West 561 (326 -- 869)
- 7: Northern Ireland 44 (24 -- 85)
- 8: Scotland 288 (160 -- 538)
- 9: South East 588 (343 -- 975)
-10: South West 415 (290 -- 598)
-11: Wales 94 (63 -- 134)
-12: West Midlands 272 (145 -- 495)
-13: Yorkshire and The Humber 476 (280 -- 787)
+ 1: East Midlands 347 (218 -- 566)
+ 2: East of England 550 (336 -- 853)
+ 3: England 3522 (2184 -- 5621)
+ 4: London 300 (187 -- 468)
+ 5: North East 253 (145 -- 416)
+ 6: North West 550 (337 -- 874)
+ 7: Northern Ireland 44 (23 -- 89)
+ 8: Scotland 283 (160 -- 531)
+ 9: South East 592 (354 -- 986)
+10: South West 420 (290 -- 613)
+11: Wales 95 (67 -- 138)
+12: West Midlands 272 (144 -- 493)
+13: Yorkshire and The Humber 481 (291 -- 770)
Expected change in daily cases Effective reproduction no.
<fctr> <char>
1: Likely increasing 1.2 (0.87 -- 1.6)
- 2: Likely increasing 1.2 (0.83 -- 1.6)
- 3: Likely decreasing 0.92 (0.63 -- 1.3)
- 4: Likely decreasing 0.78 (0.56 -- 1.1)
- 5: Likely decreasing 0.91 (0.59 -- 1.3)
- 6: Likely decreasing 0.87 (0.58 -- 1.2)
- 7: Likely decreasing 0.66 (0.4 -- 1.1)
- 8: Likely decreasing 0.91 (0.58 -- 1.4)
- 9: Stable 0.99 (0.66 -- 1.4)
+ 2: Likely increasing 1.2 (0.85 -- 1.6)
+ 3: Likely decreasing 0.91 (0.63 -- 1.3)
+ 4: Likely decreasing 0.8 (0.55 -- 1.1)
+ 5: Likely decreasing 0.91 (0.6 -- 1.3)
+ 6: Likely decreasing 0.85 (0.6 -- 1.2)
+ 7: Likely decreasing 0.64 (0.38 -- 1.1)
+ 8: Likely decreasing 0.9 (0.6 -- 1.4)
+ 9: Stable 0.99 (0.68 -- 1.4)
10: Increasing 1.4 (1.1 -- 1.8)
-11: Decreasing 0.56 (0.41 -- 0.74)
-12: Likely decreasing 0.71 (0.42 -- 1.1)
-13: Stable 1 (0.69 -- 1.4)
+11: Decreasing 0.57 (0.42 -- 0.76)
+12: Likely decreasing 0.71 (0.43 -- 1.1)
+13: Stable 1 (0.71 -- 1.4)
Rate of growth Doubling/halving time (days)
<char> <char>
- 1: 0.024 (-0.018 -- 0.069) 29 (10 -- -39)
- 2: 0.022 (-0.023 -- 0.066) 31 (11 -- -30)
- 3: -0.01 (-0.054 -- 0.033) -68 (21 -- -13)
- 4: -0.03 (-0.066 -- 0.01) -23 (67 -- -11)
- 5: -0.012 (-0.06 -- 0.034) -57 (20 -- -12)
- 6: -0.017 (-0.063 -- 0.022) -40 (32 -- -11)
- 7: -0.05 (-0.097 -- 0.0067) -14 (100 -- -7.1)
- 8: -0.012 (-0.062 -- 0.049) -58 (14 -- -11)
- 9: -0.0017 (-0.049 -- 0.049) -410 (14 -- -14)
-10: 0.046 (0.013 -- 0.085) 15 (8.2 -- 55)
-11: -0.065 (-0.095 -- -0.036) -11 (-19 -- -7.3)
-12: -0.041 (-0.092 -- 0.014) -17 (50 -- -7.5)
-13: 0.0025 (-0.045 -- 0.051) 270 (14 -- -15)
-
-
+ 1: 0.024 (-0.018 -- 0.07) 29 (9.9 -- -40)
+ 2: 0.024 (-0.021 -- 0.065) 28 (11 -- -34)
+ 3: -0.012 (-0.054 -- 0.031) -58 (22 -- -13)
+ 4: -0.028 (-0.068 -- 0.013) -25 (55 -- -10)
+ 5: -0.012 (-0.059 -- 0.035) -57 (20 -- -12)
+ 6: -0.02 (-0.06 -- 0.023) -35 (30 -- -12)
+ 7: -0.052 (-0.1 -- 0.012) -13 (58 -- -6.8)
+ 8: -0.013 (-0.059 -- 0.045) -52 (15 -- -12)
+ 9: -0.0012 (-0.046 -- 0.048) -560 (15 -- -15)
+10: 0.047 (0.013 -- 0.086) 15 (8.1 -- 54)
+11: -0.065 (-0.092 -- -0.033) -11 (-21 -- -7.6)
+12: -0.042 (-0.091 -- 0.015) -17 (47 -- -7.6)
+13: 0.0029 (-0.041 -- 0.049) 240 (14 -- -17)
+
+
R
estimates_regional$summary$plots$R
-Summary
+
Summary
EpiNow2 can be used to estimate transmission metrics
@@ -1427,7 +1475,7 @@
Challenge
HINT : data format
-
+
Ensure the data is in the correct format :
@@ -1446,7 +1494,7 @@ HINT : data format
-
+
To estimate the effective reproduction number and growth rate, we
will use the function epinow().
@@ -1883,7 +1931,7 @@ Challenge
Give me a hint
-
+
Inspect the format of the data input.
@@ -1901,7 +1949,7 @@ Give me a hint
-
+
We can use dplyr::rename() to adapt the external data to
fit the data input for cfr_static().
@@ -1967,7 +2015,7 @@ Two biases that affect CFR estimation
Preferential ascertainment of severe cases
-
+
For diseases with a spectrum of clinical presentation, those
cases that come to the attention of public health authorities and
@@ -1987,7 +2035,7 @@
Preferential ascertainment of
-
+
During an ongoing epidemic, there is a delay between the
time someone dies and the time their death is reported. Therefore, at
@@ -2118,7 +2166,7 @@
OUTPUT<
How does {cfr} works?
-
+
To adjust the CFR, Nishiura
et al., 2009 use the case and death incidence data to estimate the
@@ -2191,7 +2239,7 @@
Use the<epidist>class
When to use discrete distributions?
-
+
For cfr_static() and all the cfr_*() family
of functions, the most appropriate choice to pass are
@@ -2235,7 +2283,7 @@
Challenge
Give me a hint
-
+
- Find the appropriate
<epidist> object!
@@ -2249,7 +2297,7 @@ Give me a hint
-
+
R
@@ -2310,7 +2358,7 @@ OUTPUT<
What is the sampling process?
-
+
The population of confirmed cases and sampling
process for estimating the unbiased CFR during the course of an
@@ -2367,7 +2415,7 @@
CFR estimation during SARS outbreak, Hong Kong, 2003
-
+
Figures A and B show the cumulative numbers of cases and deaths of
SARS, and Figure C shows the observed (biased) CFR estimates as a
@@ -2532,7 +2580,7 @@
Interpret the early-stage CFR estimate
Give me a hint
-
+
We can use either visual inspection or analysis of the output data
frames.
@@ -2545,7 +2593,7 @@ Give me a hint
-
+
There is almost one month of difference.
Note that the estimate has considerable uncertainty at the beginning
@@ -2563,7 +2611,7 @@
Show me the solution
rolling or time-varying?
-
+
The cfr package has a function called
cfr_time_varying() with different functionality that makes
@@ -2644,7 +2692,7 @@
Severity heterogeneity
Give me a hint
-
+
One way to do a stratified analysis is to apply a model to
nested data. This {tidyr}
@@ -2660,7 +2708,7 @@ Give me a hint
-
+
R
@@ -2857,7 +2905,7 @@ Hospitalisation Fatality Risk (HFR)
Give me a hint
-
+
Use covidregionaldata::get_regional_data() to get COVID
data from the region England from the country UK.
@@ -2883,7 +2931,7 @@ R
Show me the solution
-
+
R
diff --git a/create-forecast.html b/create-forecast.html
index 32b775ea..36bbe78b 100644
--- a/create-forecast.html
+++ b/create-forecast.html
@@ -632,7 +632,7 @@ Challenge
HINT : data format
-
+
Ensure the data is in the correct format :
-
@@ -649,7 +649,7 @@
HINT : data format
-
+
To estimate the effective reproduction number and growth rate, we
will use the function epinow().
diff --git a/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png b/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png
index cdc42cdd..7b51c170 100644
Binary files a/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png and b/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png differ
diff --git a/fig/quantify-transmissibility-rendered-unnamed-chunk-17-1.png b/fig/quantify-transmissibility-rendered-unnamed-chunk-17-1.png
new file mode 100644
index 00000000..c1cb171d
Binary files /dev/null and b/fig/quantify-transmissibility-rendered-unnamed-chunk-17-1.png differ
diff --git a/fig/quantify-transmissibility-rendered-unnamed-chunk-20-1.png b/fig/quantify-transmissibility-rendered-unnamed-chunk-20-1.png
new file mode 100644
index 00000000..19d4423d
Binary files /dev/null and b/fig/quantify-transmissibility-rendered-unnamed-chunk-20-1.png differ
diff --git a/images.html b/images.html
index 18283750..c3c5b4a6 100644
--- a/images.html
+++ b/images.html
@@ -281,11 +281,11 @@ Figure 1
Figure 2
Figure 3
-
-Figure 4
+Figure 4
+
Figure 5
-
Create a short-term forecast
+
Create a short-term forecast
Figure 1
diff --git a/instructor/aio.html b/instructor/aio.html
index 496555f4..63254f13 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -278,7 +278,7 @@
Content from Quantifying transmission
-Last updated on 2024-03-28 |
+
Last updated on 2024-03-30 |
Edit this page
Estimated time: 30 minutes
@@ -398,7 +398,7 @@ Bayesian inference
Instructor Note
-
+
We refer to the Prior probability distribution and the Posterior
probability distribution.
@@ -423,7 +423,7 @@ R
Instructor Note
-
+
This tutorial illustrates the usage of epinow() to
estimate the time-varying reproduction number and infection times.
@@ -447,32 +447,27 @@
Case dataR
-head(incidence2::covidregionaldataUK)
+dplyr::as_tibble(incidence2::covidregionaldataUK)
OUTPUT
- date region region_code cases_new cases_total deaths_new
-1 2020-01-30 East Midlands E12000004 NA NA NA
-2 2020-01-30 East of England E12000006 NA NA NA
-3 2020-01-30 England E92000001 2 2 NA
-4 2020-01-30 London E12000007 NA NA NA
-5 2020-01-30 North East E12000001 NA NA NA
-6 2020-01-30 North West E12000002 NA NA NA
- deaths_total recovered_new recovered_total hosp_new hosp_total tested_new
-1 NA NA NA NA NA NA
-2 NA NA NA NA NA NA
-3 NA NA NA NA NA NA
-4 NA NA NA NA NA NA
-5 NA NA NA NA NA NA
-6 NA NA NA NA NA NA
- tested_total
-1 NA
-2 NA
-3 NA
-4 NA
-5 NA
-6 NA
+# A tibble: 6,370 × 13
+ date region region_code cases_new cases_total deaths_new deaths_total
+ <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
+ 1 2020-01-30 East Mi… E12000004 NA NA NA NA
+ 2 2020-01-30 East of… E12000006 NA NA NA NA
+ 3 2020-01-30 England E92000001 2 2 NA NA
+ 4 2020-01-30 London E12000007 NA NA NA NA
+ 5 2020-01-30 North E… E12000001 NA NA NA NA
+ 6 2020-01-30 North W… E12000002 NA NA NA NA
+ 7 2020-01-30 Norther… N92000002 NA NA NA NA
+ 8 2020-01-30 Scotland S92000003 NA NA NA NA
+ 9 2020-01-30 South E… E12000008 NA NA NA NA
+10 2020-01-30 South W… E12000009 NA NA NA NA
+# ℹ 6,360 more rows
+# ℹ 6 more variables: recovered_new <dbl>, recovered_total <dbl>,
+# hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>, tested_total <dbl>
To use the data, we must format the data to have two columns:
@@ -482,18 +477,20 @@ OUTPUT<
-
confirm : number of confirmed cases on that date.
+Let’s use dplyr for this:
R
-cases <- aggregate(
- cases_new ~ date,
- data = incidence2::covidregionaldataUK[, c("date", "cases_new")],
- FUN = sum
-)
-colnames(cases) <- c("date", "confirm")
+library(dplyr)
+
+cases <- incidence2::covidregionaldataUK %>%
+ select(date, cases_new) %>%
+ group_by(date) %>%
+ summarise(confirm = sum(cases_new, na.rm = TRUE)) %>%
+ ungroup()
-There are case data available for 489 days, but in an outbreak
+
There are case data available for 490 days, but in an outbreak
situation it is likely we would only have access to the beginning of
this data set. Therefore we assume we only have the first 90 days of
this data.
@@ -542,18 +539,18 @@ Delays and data
+Time of symptom onset
+Incubation period
+
+
Time of case report
Incubation period + time from symptom onset to case
notification
-
+
Time of hospitalisation
Incubation period + time from symptom onset to hospitalisation
-
-Time of symptom onset
-Incubation period
-
@@ -781,34 +778,81 @@ R
reported_cases <- cases[1:90, ]
+
estimates <- epinow(
reported_cases = reported_cases,
generation_time = generation_time_opts(generation_time_fixed),
- delays = delay_opts(incubation_period_fixed + reporting_delay_fixed),
- rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd))
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ )
)
OUTPUT
-WARN [2024-03-28 21:34:59] epinow: There were 8 divergent transitions after warmup. See
+WARN [2024-03-30 17:17:33] epinow: There were 1 divergent transitions after warmup. See
https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
to find out why this is a problem and how to eliminate them. -
-WARN [2024-03-28 21:34:59] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-03-30 17:17:33] epinow: Examine the pairs() plot to diagnose sampling problems
-
+
+
+
+
+
+Using an appropriate number of samples and chains is crucial for
+ensuring convergence and obtaining reliable estimates in Bayesian
+computations using Stan. Inadequate sampling or insufficient chains may
+lead to issues such as divergent transitions, impacting the accuracy and
+stability of the inference process.
+For the purpose of this tutorial, we can add more configuration
+details to get an useful output in less time. You can specify a fixed
+number of samples and chains to the
+stan argument using the stan_opts()
+function:
+The code in the previous chunk can take around 10 minutes. We expect
+this chunk below to take approximately 3 minutes:
+
+R
+
+
+estimates <- epinow(
+ # same code as previous chunk
+ reported_cases = reported_cases,
+ generation_time = generation_time_opts(generation_time_fixed),
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ ),
+ # [new] set a fixed number of samples and chains
+ stan = stan_opts(samples = 1000, chains = 3)
+)
+
+
+
+
+
Results
We can extract and visualise estimates of the effective reproduction
number through time:
-
+
R
estimates$plots$R
-The uncertainty in the estimates increases through time. This is
+
The uncertainty in the estimates increases through time. This is
because estimates are informed by data in the past - within the delay
periods. This difference in uncertainty is categorised into
Estimate (green) utilises all data and Estimate
@@ -818,15 +862,15 @@ RForecast
(purple) is a projection ahead of time.
We can also visualise the growth rate estimate through time:
-
+
R
estimates$plots$growth_rate
-To extract a summary of the key transmission metrics at the
+
To extract a summary of the key transmission metrics at the
latest date in the data:
-
+
R
@@ -837,24 +881,24 @@ OUTPUT<
measure estimate
<char> <char>
-1: New confirmed cases by infection date 7232 (4047 -- 13301)
+1: New confirmed cases by infection date 7097 (3893 -- 12496)
2: Expected change in daily cases Likely decreasing
-3: Effective reproduction no. 0.89 (0.58 -- 1.4)
-4: Rate of growth -0.015 (-0.063 -- 0.043)
-5: Doubling/halving time (days) -47 (16 -- -11)
+3: Effective reproduction no. 0.88 (0.55 -- 1.3)
+4: Rate of growth -0.016 (-0.068 -- 0.037)
+5: Doubling/halving time (days) -42 (19 -- -10)
As these estimates are based on partial data, they have a wide
uncertainty interval.
From the summary of our analysis we see that the expected change
in daily cases is Likely decreasing with the estimated new confirmed
-cases 7232 (4047 – 13301).
The effective reproduction number \(R_t\) estimate (on the last date of the
-data) is 0.89 (0.58 – 1.4).
The exponential growth rate of case numbers is -0.015 (-0.063 –
-0.043).
The exponential growth rate of case numbers is -0.016 (-0.068 –
+0.037).
The doubling time (the time taken for case numbers to double) is
--47 (16 – -11).
@@ -919,7 +963,7 @@
confirm : number of confirmed cases for a region on a
given date.
-
+
R
@@ -947,33 +991,37 @@ OUTPUT<
To find regional estimates, we use the same inputs as
epinow() to the function
regional_epinow():
-
+
R
estimates_regional <- regional_epinow(
reported_cases = regional_cases,
generation_time = generation_time_opts(generation_time_fixed),
- delays = delay_opts(incubation_period_fixed + reporting_delay_fixed),
- rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd))
+ delays = delay_opts(
+ incubation_period_fixed + reporting_delay_fixed
+ ),
+ rt = rt_opts(
+ prior = list(mean = rt_log_mean, sd = rt_log_sd)
+ )
)
OUTPUT
-INFO [2024-03-28 21:35:04] Producing following optional outputs: regions, summary, samples, plots, latest
-INFO [2024-03-28 21:35:04] Reporting estimates using data up to: 2020-04-28
-INFO [2024-03-28 21:35:04] No target directory specified so returning output
-INFO [2024-03-28 21:35:04] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
-INFO [2024-03-28 21:35:04] Regions excluded: none
-INFO [2024-03-28 22:21:26] Completed regional estimates
-INFO [2024-03-28 22:21:26] Regions with estimates: 13
-INFO [2024-03-28 22:21:26] Regions with runtime errors: 0
-INFO [2024-03-28 22:21:26] Producing summary
-INFO [2024-03-28 22:21:26] No summary directory specified so returning summary output
-INFO [2024-03-28 22:21:26] No target directory specified so returning timings
+INFO [2024-03-30 17:17:38] Producing following optional outputs: regions, summary, samples, plots, latest
+INFO [2024-03-30 17:17:38] Reporting estimates using data up to: 2020-04-28
+INFO [2024-03-30 17:17:38] No target directory specified so returning output
+INFO [2024-03-30 17:17:38] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
+INFO [2024-03-30 17:17:38] Regions excluded: none
+INFO [2024-03-30 18:03:05] Completed regional estimates
+INFO [2024-03-30 18:03:05] Regions with estimates: 13
+INFO [2024-03-30 18:03:05] Regions with runtime errors: 0
+INFO [2024-03-30 18:03:05] Producing summary
+INFO [2024-03-30 18:03:05] No summary directory specified so returning summary output
+INFO [2024-03-30 18:03:06] No target directory specified so returning timings
-
+
R
@@ -984,57 +1032,57 @@ OUTPUT<
Region New confirmed cases by infection date
<char> <char>
- 1: East Midlands 347 (216 -- 568)
- 2: East of England 540 (331 -- 840)
- 3: England 3621 (2184 -- 5730)
- 4: London 293 (191 -- 456)
- 5: North East 251 (144 -- 412)
- 6: North West 561 (326 -- 869)
- 7: Northern Ireland 44 (24 -- 85)
- 8: Scotland 288 (160 -- 538)
- 9: South East 588 (343 -- 975)
-10: South West 415 (290 -- 598)
-11: Wales 94 (63 -- 134)
-12: West Midlands 272 (145 -- 495)
-13: Yorkshire and The Humber 476 (280 -- 787)
+ 1: East Midlands 347 (218 -- 566)
+ 2: East of England 550 (336 -- 853)
+ 3: England 3522 (2184 -- 5621)
+ 4: London 300 (187 -- 468)
+ 5: North East 253 (145 -- 416)
+ 6: North West 550 (337 -- 874)
+ 7: Northern Ireland 44 (23 -- 89)
+ 8: Scotland 283 (160 -- 531)
+ 9: South East 592 (354 -- 986)
+10: South West 420 (290 -- 613)
+11: Wales 95 (67 -- 138)
+12: West Midlands 272 (144 -- 493)
+13: Yorkshire and The Humber 481 (291 -- 770)
Expected change in daily cases Effective reproduction no.
<fctr> <char>
1: Likely increasing 1.2 (0.87 -- 1.6)
- 2: Likely increasing 1.2 (0.83 -- 1.6)
- 3: Likely decreasing 0.92 (0.63 -- 1.3)
- 4: Likely decreasing 0.78 (0.56 -- 1.1)
- 5: Likely decreasing 0.91 (0.59 -- 1.3)
- 6: Likely decreasing 0.87 (0.58 -- 1.2)
- 7: Likely decreasing 0.66 (0.4 -- 1.1)
- 8: Likely decreasing 0.91 (0.58 -- 1.4)
- 9: Stable 0.99 (0.66 -- 1.4)
+ 2: Likely increasing 1.2 (0.85 -- 1.6)
+ 3: Likely decreasing 0.91 (0.63 -- 1.3)
+ 4: Likely decreasing 0.8 (0.55 -- 1.1)
+ 5: Likely decreasing 0.91 (0.6 -- 1.3)
+ 6: Likely decreasing 0.85 (0.6 -- 1.2)
+ 7: Likely decreasing 0.64 (0.38 -- 1.1)
+ 8: Likely decreasing 0.9 (0.6 -- 1.4)
+ 9: Stable 0.99 (0.68 -- 1.4)
10: Increasing 1.4 (1.1 -- 1.8)
-11: Decreasing 0.56 (0.41 -- 0.74)
-12: Likely decreasing 0.71 (0.42 -- 1.1)
-13: Stable 1 (0.69 -- 1.4)
+11: Decreasing 0.57 (0.42 -- 0.76)
+12: Likely decreasing 0.71 (0.43 -- 1.1)
+13: Stable 1 (0.71 -- 1.4)
Rate of growth Doubling/halving time (days)
<char> <char>
- 1: 0.024 (-0.018 -- 0.069) 29 (10 -- -39)
- 2: 0.022 (-0.023 -- 0.066) 31 (11 -- -30)
- 3: -0.01 (-0.054 -- 0.033) -68 (21 -- -13)
- 4: -0.03 (-0.066 -- 0.01) -23 (67 -- -11)
- 5: -0.012 (-0.06 -- 0.034) -57 (20 -- -12)
- 6: -0.017 (-0.063 -- 0.022) -40 (32 -- -11)
- 7: -0.05 (-0.097 -- 0.0067) -14 (100 -- -7.1)
- 8: -0.012 (-0.062 -- 0.049) -58 (14 -- -11)
- 9: -0.0017 (-0.049 -- 0.049) -410 (14 -- -14)
-10: 0.046 (0.013 -- 0.085) 15 (8.2 -- 55)
-11: -0.065 (-0.095 -- -0.036) -11 (-19 -- -7.3)
-12: -0.041 (-0.092 -- 0.014) -17 (50 -- -7.5)
-13: 0.0025 (-0.045 -- 0.051) 270 (14 -- -15)
-
-
+ 1: 0.024 (-0.018 -- 0.07) 29 (9.9 -- -40)
+ 2: 0.024 (-0.021 -- 0.065) 28 (11 -- -34)
+ 3: -0.012 (-0.054 -- 0.031) -58 (22 -- -13)
+ 4: -0.028 (-0.068 -- 0.013) -25 (55 -- -10)
+ 5: -0.012 (-0.059 -- 0.035) -57 (20 -- -12)
+ 6: -0.02 (-0.06 -- 0.023) -35 (30 -- -12)
+ 7: -0.052 (-0.1 -- 0.012) -13 (58 -- -6.8)
+ 8: -0.013 (-0.059 -- 0.045) -52 (15 -- -12)
+ 9: -0.0012 (-0.046 -- 0.048) -560 (15 -- -15)
+10: 0.047 (0.013 -- 0.086) 15 (8.1 -- 54)
+11: -0.065 (-0.092 -- -0.033) -11 (-21 -- -7.6)
+12: -0.042 (-0.091 -- 0.015) -17 (47 -- -7.6)
+13: 0.0029 (-0.041 -- 0.049) 240 (14 -- -17)
+
+
R
estimates_regional$summary$plots$R
-Summary
+
Summary
EpiNow2 can be used to estimate transmission metrics
@@ -1463,7 +1511,7 @@
Challenge
HINT : data format
-
+
Ensure the data is in the correct format :
@@ -1482,7 +1530,7 @@ HINT : data format
-
+
To estimate the effective reproduction number and growth rate, we
will use the function epinow().
@@ -1708,7 +1756,7 @@ Prerequisites
Instructor Note
-
+
The periods are relevant: Period 1 – 15 days where CFR is zero to
indicate this is due to no reported deaths; Period from Mar 15 – Apr 26
@@ -1936,7 +1984,7 @@
Challenge
Give me a hint
-
+
Inspect the format of the data input.
@@ -1954,7 +2002,7 @@ Give me a hint
-
+
We can use dplyr::rename() to adapt the external data to
fit the data input for cfr_static().
@@ -2020,7 +2068,7 @@ Two biases that affect CFR estimation
Preferential ascertainment of severe cases
-
+
For diseases with a spectrum of clinical presentation, those
cases that come to the attention of public health authorities and
@@ -2040,7 +2088,7 @@
Preferential ascertainment of
-
+
During an ongoing epidemic, there is a delay between the
time someone dies and the time their death is reported. Therefore, at
@@ -2065,7 +2113,7 @@
Bias due to delayed reporting
Instructor Note
-
+
We can showcase this last bias using the concept
described in this {cfr} vignette.
@@ -2185,7 +2233,7 @@ OUTPUT<
How does {cfr} works?
-
+
To adjust the CFR, Nishiura
et al., 2009 use the case and death incidence data to estimate the
@@ -2258,7 +2306,7 @@
Use the<epidist>class
When to use discrete distributions?
-
+
For cfr_static() and all the cfr_*() family
of functions, the most appropriate choice to pass are
@@ -2302,7 +2350,7 @@
Challenge
Give me a hint
-
+
- Find the appropriate
<epidist> object!
@@ -2316,7 +2364,7 @@ Give me a hint
-
+
R
@@ -2377,7 +2425,7 @@ OUTPUT<
What is the sampling process?
-
+
The population of confirmed cases and sampling
process for estimating the unbiased CFR during the course of an
@@ -2434,7 +2482,7 @@
CFR estimation during SARS outbreak, Hong Kong, 2003
-
+
Figures A and B show the cumulative numbers of cases and deaths of
SARS, and Figure C shows the observed (biased) CFR estimates as a
@@ -2599,7 +2647,7 @@
Interpret the early-stage CFR estimate
Give me a hint
-
+
We can use either visual inspection or analysis of the output data
frames.
@@ -2612,7 +2660,7 @@ Give me a hint
-
+
There is almost one month of difference.
Note that the estimate has considerable uncertainty at the beginning
@@ -2630,7 +2678,7 @@
Show me the solution
rolling or time-varying?
-
+
The cfr package has a function called
cfr_time_varying() with different functionality that makes
@@ -2711,7 +2759,7 @@
Severity heterogeneity
Give me a hint
-
+
One way to do a stratified analysis is to apply a model to
nested data. This {tidyr}
@@ -2727,7 +2775,7 @@ Give me a hint
-
+
R
@@ -2924,7 +2972,7 @@ Hospitalisation Fatality Risk (HFR)
Give me a hint
-
+
Use covidregionaldata::get_regional_data() to get COVID
data from the region England from the country UK.
@@ -2950,7 +2998,7 @@ R
Show me the solution
-
+
R
diff --git a/instructor/create-forecast.html b/instructor/create-forecast.html
index 2213a79a..1584066a 100644
--- a/instructor/create-forecast.html
+++ b/instructor/create-forecast.html
@@ -634,7 +634,7 @@ Challenge
HINT : data format
-
+
Ensure the data is in the correct format :
-
@@ -651,7 +651,7 @@
HINT : data format
-
+
To estimate the effective reproduction number and growth rate, we
will use the function epinow().
diff --git a/instructor/images.html b/instructor/images.html
index 432b597c..b4f49d0c 100644
--- a/instructor/images.html
+++ b/instructor/images.html
@@ -283,11 +283,11 @@ Figure 1
Figure 2
Figure 3
-
-Figure 4
+Figure 4
+
Figure 5
-
Create a short-term forecast
+
Create a short-term forecast
Figure 1
diff --git a/instructor/index.html b/instructor/index.html
index f1d8109e..197a0ea9 100644
--- a/instructor/index.html
+++ b/instructor/index.html
@@ -350,7 +350,7 @@
R
@@ -912,33 +956,37 @@OUTPUT<
To find regional estimates, we use the same inputs as
-epinow()to the functionregional_epinow():+R
estimates_regional <- regional_epinow( reported_cases = regional_cases, generation_time = generation_time_opts(generation_time_fixed), - delays = delay_opts(incubation_period_fixed + reporting_delay_fixed), - rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd)) + delays = delay_opts( + incubation_period_fixed + reporting_delay_fixed + ), + rt = rt_opts( + prior = list(mean = rt_log_mean, sd = rt_log_sd) + ) )-OUTPUT
-+INFO [2024-03-28 21:35:04] Producing following optional outputs: regions, summary, samples, plots, latest -INFO [2024-03-28 21:35:04] Reporting estimates using data up to: 2020-04-28 -INFO [2024-03-28 21:35:04] No target directory specified so returning output -INFO [2024-03-28 21:35:04] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber -INFO [2024-03-28 21:35:04] Regions excluded: none -INFO [2024-03-28 22:21:26] Completed regional estimates -INFO [2024-03-28 22:21:26] Regions with estimates: 13 -INFO [2024-03-28 22:21:26] Regions with runtime errors: 0 -INFO [2024-03-28 22:21:26] Producing summary -INFO [2024-03-28 22:21:26] No summary directory specified so returning summary output -INFO [2024-03-28 22:21:26] No target directory specified so returning timingsINFO [2024-03-30 17:17:38] Producing following optional outputs: regions, summary, samples, plots, latest +INFO [2024-03-30 17:17:38] Reporting estimates using data up to: 2020-04-28 +INFO [2024-03-30 17:17:38] No target directory specified so returning output +INFO [2024-03-30 17:17:38] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber +INFO [2024-03-30 17:17:38] Regions excluded: none +INFO [2024-03-30 18:03:05] Completed regional estimates +INFO [2024-03-30 18:03:05] Regions with estimates: 13 +INFO [2024-03-30 18:03:05] Regions with runtime errors: 0 +INFO [2024-03-30 18:03:05] Producing summary +INFO [2024-03-30 18:03:05] No summary directory specified so returning summary output +INFO [2024-03-30 18:03:06] No target directory specified so returning timings+-R
@@ -949,57 +997,57 @@OUTPUT<
-Region New confirmed cases by infection date <char> <char> - 1: East Midlands 347 (216 -- 568) - 2: East of England 540 (331 -- 840) - 3: England 3621 (2184 -- 5730) - 4: London 293 (191 -- 456) - 5: North East 251 (144 -- 412) - 6: North West 561 (326 -- 869) - 7: Northern Ireland 44 (24 -- 85) - 8: Scotland 288 (160 -- 538) - 9: South East 588 (343 -- 975) -10: South West 415 (290 -- 598) -11: Wales 94 (63 -- 134) -12: West Midlands 272 (145 -- 495) -13: Yorkshire and The Humber 476 (280 -- 787) + 1: East Midlands 347 (218 -- 566) + 2: East of England 550 (336 -- 853) + 3: England 3522 (2184 -- 5621) + 4: London 300 (187 -- 468) + 5: North East 253 (145 -- 416) + 6: North West 550 (337 -- 874) + 7: Northern Ireland 44 (23 -- 89) + 8: Scotland 283 (160 -- 531) + 9: South East 592 (354 -- 986) +10: South West 420 (290 -- 613) +11: Wales 95 (67 -- 138) +12: West Midlands 272 (144 -- 493) +13: Yorkshire and The Humber 481 (291 -- 770) Expected change in daily cases Effective reproduction no. <fctr> <char> 1: Likely increasing 1.2 (0.87 -- 1.6) - 2: Likely increasing 1.2 (0.83 -- 1.6) - 3: Likely decreasing 0.92 (0.63 -- 1.3) - 4: Likely decreasing 0.78 (0.56 -- 1.1) - 5: Likely decreasing 0.91 (0.59 -- 1.3) - 6: Likely decreasing 0.87 (0.58 -- 1.2) - 7: Likely decreasing 0.66 (0.4 -- 1.1) - 8: Likely decreasing 0.91 (0.58 -- 1.4) - 9: Stable 0.99 (0.66 -- 1.4) + 2: Likely increasing 1.2 (0.85 -- 1.6) + 3: Likely decreasing 0.91 (0.63 -- 1.3) + 4: Likely decreasing 0.8 (0.55 -- 1.1) + 5: Likely decreasing 0.91 (0.6 -- 1.3) + 6: Likely decreasing 0.85 (0.6 -- 1.2) + 7: Likely decreasing 0.64 (0.38 -- 1.1) + 8: Likely decreasing 0.9 (0.6 -- 1.4) + 9: Stable 0.99 (0.68 -- 1.4) 10: Increasing 1.4 (1.1 -- 1.8) -11: Decreasing 0.56 (0.41 -- 0.74) -12: Likely decreasing 0.71 (0.42 -- 1.1) -13: Stable 1 (0.69 -- 1.4) +11: Decreasing 0.57 (0.42 -- 0.76) +12: Likely decreasing 0.71 (0.43 -- 1.1) +13: Stable 1 (0.71 -- 1.4) Rate of growth Doubling/halving time (days) <char> <char> - 1: 0.024 (-0.018 -- 0.069) 29 (10 -- -39) - 2: 0.022 (-0.023 -- 0.066) 31 (11 -- -30) - 3: -0.01 (-0.054 -- 0.033) -68 (21 -- -13) - 4: -0.03 (-0.066 -- 0.01) -23 (67 -- -11) - 5: -0.012 (-0.06 -- 0.034) -57 (20 -- -12) - 6: -0.017 (-0.063 -- 0.022) -40 (32 -- -11) - 7: -0.05 (-0.097 -- 0.0067) -14 (100 -- -7.1) - 8: -0.012 (-0.062 -- 0.049) -58 (14 -- -11) - 9: -0.0017 (-0.049 -- 0.049) -410 (14 -- -14) -10: 0.046 (0.013 -- 0.085) 15 (8.2 -- 55) -11: -0.065 (-0.095 -- -0.036) -11 (-19 -- -7.3) -12: -0.041 (-0.092 -- 0.014) -17 (50 -- -7.5) -13: 0.0025 (-0.045 -- 0.051) 270 (14 -- -15)+ 1: 0.024 (-0.018 -- 0.07) 29 (9.9 -- -40) + 2: 0.024 (-0.021 -- 0.065) 28 (11 -- -34) + 3: -0.012 (-0.054 -- 0.031) -58 (22 -- -13) + 4: -0.028 (-0.068 -- 0.013) -25 (55 -- -10) + 5: -0.012 (-0.059 -- 0.035) -57 (20 -- -12) + 6: -0.02 (-0.06 -- 0.023) -35 (30 -- -12) + 7: -0.052 (-0.1 -- 0.012) -13 (58 -- -6.8) + 8: -0.013 (-0.059 -- 0.045) -52 (15 -- -12) + 9: -0.0012 (-0.046 -- 0.048) -560 (15 -- -15) +10: 0.047 (0.013 -- 0.086) 15 (8.1 -- 54) +11: -0.065 (-0.092 -- -0.033) -11 (-21 -- -7.6) +12: -0.042 (-0.091 -- 0.015) -17 (47 -- -7.6) +13: 0.0029 (-0.041 -- 0.049) 240 (14 -- -17) ++-R
estimates_regional$summary$plots$RSummary +
Summary
EpiNow2can be used to estimate transmission metrics @@ -1427,7 +1475,7 @@Challenge
HINT : data format
-+Ensure the data is in the correct format :
@@ -1446,7 +1494,7 @@
HINT : data format
-+To estimate the effective reproduction number and growth rate, we will use the function
@@ -1883,7 +1931,7 @@epinow().Challenge
Give me a hint
-+Inspect the format of the data input.
@@ -1901,7 +1949,7 @@
Give me a hint
-+We can use
@@ -1967,7 +2015,7 @@dplyr::rename()to adapt the external data to fit the data input forcfr_static().Two biases that affect CFR estimation
Preferential ascertainment of severe cases
-+For diseases with a spectrum of clinical presentation, those cases that come to the attention of public health authorities and @@ -1987,7 +2035,7 @@
Preferential ascertainment of -
+During an ongoing epidemic, there is a delay between the time someone dies and the time their death is reported. Therefore, at @@ -2118,7 +2166,7 @@
OUTPUT<
-
How does {cfr} works?+To adjust the CFR, Nishiura et al., 2009 use the case and death incidence data to estimate the @@ -2191,7 +2239,7 @@
Use the
-<epidist>classWhen to use discrete distributions?+For
cfr_static()and all thecfr_*()family of functions, the most appropriate choice to pass are @@ -2235,7 +2283,7 @@Challenge
Give me a hint
-+
- Find the appropriate
@@ -2249,7 +2297,7 @@<epidist>object!Give me a hint
-+R @@ -2310,7 +2358,7 @@
OUTPUT<
-
What is the sampling process?+
-
CFR estimation during SARS outbreak, Hong Kong, 2003+Figures A and B show the cumulative numbers of cases and deaths of SARS, and Figure C shows the observed (biased) CFR estimates as a @@ -2532,7 +2580,7 @@
Interpret the early-stage CFR estimate
Give me a hint
-+We can use either visual inspection or analysis of the output data frames.
@@ -2545,7 +2593,7 @@Give me a hint
-+There is almost one month of difference.
Note that the estimate has considerable uncertainty at the beginning @@ -2563,7 +2611,7 @@
Show me the solution
-
rolling or time-varying?+The cfr package has a function called
cfr_time_varying()with different functionality that makes @@ -2644,7 +2692,7 @@Severity heterogeneity
Give me a hint
-+One way to do a stratified analysis is to apply a model to nested data. This
{tidyr}@@ -2660,7 +2708,7 @@Give me a hint
-+R @@ -2857,7 +2905,7 @@
Hospitalisation Fatality Risk (HFR)
Give me a hint
-+Use
@@ -2883,7 +2931,7 @@covidregionaldata::get_regional_data()to get COVID data from the region England from the country UK.R
Show me the solution
-+R diff --git a/create-forecast.html b/create-forecast.html index 32b775ea..36bbe78b 100644 --- a/create-forecast.html +++ b/create-forecast.html @@ -632,7 +632,7 @@
Challenge
HINT : data format
-+Ensure the data is in the correct format :
- @@ -649,7 +649,7 @@
HINT : data format
-+To estimate the effective reproduction number and growth rate, we will use the function
diff --git a/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png b/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png index cdc42cdd..7b51c170 100644 Binary files a/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png and b/fig/quantify-transmissibility-rendered-unnamed-chunk-16-1.png differ diff --git a/fig/quantify-transmissibility-rendered-unnamed-chunk-17-1.png b/fig/quantify-transmissibility-rendered-unnamed-chunk-17-1.png new file mode 100644 index 00000000..c1cb171d Binary files /dev/null and b/fig/quantify-transmissibility-rendered-unnamed-chunk-17-1.png differ diff --git a/fig/quantify-transmissibility-rendered-unnamed-chunk-20-1.png b/fig/quantify-transmissibility-rendered-unnamed-chunk-20-1.png new file mode 100644 index 00000000..19d4423d Binary files /dev/null and b/fig/quantify-transmissibility-rendered-unnamed-chunk-20-1.png differ diff --git a/images.html b/images.html index 18283750..c3c5b4a6 100644 --- a/images.html +++ b/images.html @@ -281,11 +281,11 @@epinow().Figure 1
Figure 2
Figure 3
-
-Figure 4
+Figure 4
+
Figure 5
-Create a short-term forecast
+Create a short-term forecast
Figure 1
diff --git a/instructor/aio.html b/instructor/aio.html index 496555f4..63254f13 100644 --- a/instructor/aio.html +++ b/instructor/aio.html @@ -278,7 +278,7 @@
Content from Quantifying transmission
-Last updated on 2024-03-28 | +
Last updated on 2024-03-30 | Edit this page
Estimated time: 30 minutes
@@ -398,7 +398,7 @@Bayesian inference
-Instructor Note+We refer to the Prior probability distribution and the Posterior probability distribution.
@@ -423,7 +423,7 @@R
-Instructor Note+This tutorial illustrates the usage of
epinow()to estimate the time-varying reproduction number and infection times. @@ -447,32 +447,27 @@Case dataR
-+head(incidence2::covidregionaldataUK)dplyr::as_tibble(incidence2::covidregionaldataUK)OUTPUT
-+date region region_code cases_new cases_total deaths_new -1 2020-01-30 East Midlands E12000004 NA NA NA -2 2020-01-30 East of England E12000006 NA NA NA -3 2020-01-30 England E92000001 2 2 NA -4 2020-01-30 London E12000007 NA NA NA -5 2020-01-30 North East E12000001 NA NA NA -6 2020-01-30 North West E12000002 NA NA NA - deaths_total recovered_new recovered_total hosp_new hosp_total tested_new -1 NA NA NA NA NA NA -2 NA NA NA NA NA NA -3 NA NA NA NA NA NA -4 NA NA NA NA NA NA -5 NA NA NA NA NA NA -6 NA NA NA NA NA NA - tested_total -1 NA -2 NA -3 NA -4 NA -5 NA -6 NA# A tibble: 6,370 × 13 + date region region_code cases_new cases_total deaths_new deaths_total + <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl> + 1 2020-01-30 East Mi… E12000004 NA NA NA NA + 2 2020-01-30 East of… E12000006 NA NA NA NA + 3 2020-01-30 England E92000001 2 2 NA NA + 4 2020-01-30 London E12000007 NA NA NA NA + 5 2020-01-30 North E… E12000001 NA NA NA NA + 6 2020-01-30 North W… E12000002 NA NA NA NA + 7 2020-01-30 Norther… N92000002 NA NA NA NA + 8 2020-01-30 Scotland S92000003 NA NA NA NA + 9 2020-01-30 South E… E12000008 NA NA NA NA +10 2020-01-30 South W… E12000009 NA NA NA NA +# ℹ 6,360 more rows +# ℹ 6 more variables: recovered_new <dbl>, recovered_total <dbl>, +# hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>, tested_total <dbl>To use the data, we must format the data to have two columns:
@@ -482,18 +477,20 @@
+OUTPUT<
confirm: number of confirmed cases on that date.Let’s use dplyr for this:
-R
-+cases <- aggregate( - cases_new ~ date, - data = incidence2::covidregionaldataUK[, c("date", "cases_new")], - FUN = sum -) -colnames(cases) <- c("date", "confirm")library(dplyr) + +cases <- incidence2::covidregionaldataUK %>% + select(date, cases_new) %>% + group_by(date) %>% + summarise(confirm = sum(cases_new, na.rm = TRUE)) %>% + ungroup()There are case data available for 489 days, but in an outbreak +
There are case data available for 490 days, but in an outbreak situation it is likely we would only have access to the beginning of this data set. Therefore we assume we only have the first 90 days of this data.
@@ -542,18 +539,18 @@Delays and data
+ +Time of symptom onset +Incubation period +- Time of case report Incubation period + time from symptom onset to case notification + - Time of hospitalisation Incubation period + time from symptom onset to hospitalisation - @@ -781,34 +778,81 @@Time of symptom onset -Incubation period -R
reported_cases <- cases[1:90, ] + estimates <- epinow( reported_cases = reported_cases, generation_time = generation_time_opts(generation_time_fixed), - delays = delay_opts(incubation_period_fixed + reporting_delay_fixed), - rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd)) + delays = delay_opts( + incubation_period_fixed + reporting_delay_fixed + ), + rt = rt_opts( + prior = list(mean = rt_log_mean, sd = rt_log_sd) + ) )+OUTPUT
-WARN [2024-03-28 21:34:59] epinow: There were 8 divergent transitions after warmup. See +WARN [2024-03-30 17:17:33] epinow: There were 1 divergent transitions after warmup. See https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup to find out why this is a problem and how to eliminate them. - -WARN [2024-03-28 21:34:59] epinow: Examine the pairs() plot to diagnose sampling problems +WARN [2024-03-30 17:17:33] epinow: Examine the pairs() plot to diagnose sampling problems -++ ++++++Using an appropriate number of samples and chains is crucial for +ensuring convergence and obtaining reliable estimates in Bayesian +computations using Stan. Inadequate sampling or insufficient chains may +lead to issues such as divergent transitions, impacting the accuracy and +stability of the inference process.
+For the purpose of this tutorial, we can add more configuration +details to get an useful output in less time. You can specify a fixed +number of
+samplesandchainsto the +stanargument using thestan_opts()+function:The code in the previous chunk can take around 10 minutes. We expect +this chunk below to take approximately 3 minutes:
+++R +
+++estimates <- epinow( + # same code as previous chunk + reported_cases = reported_cases, + generation_time = generation_time_opts(generation_time_fixed), + delays = delay_opts( + incubation_period_fixed + reporting_delay_fixed + ), + rt = rt_opts( + prior = list(mean = rt_log_mean, sd = rt_log_sd) + ), + # [new] set a fixed number of samples and chains + stan = stan_opts(samples = 1000, chains = 3) +)Results
We can extract and visualise estimates of the effective reproduction number through time:
-+-R
estimates$plots$RThe uncertainty in the estimates increases through time. This is +
The uncertainty in the estimates increases through time. This is because estimates are informed by data in the past - within the delay periods. This difference in uncertainty is categorised into Estimate (green) utilises all data and Estimate @@ -818,15 +862,15 @@
RForecast
(purple) is a projection ahead of time.We can also visualise the growth rate estimate through time:
-+-R
estimates$plots$growth_rateTo extract a summary of the key transmission metrics at the +
To extract a summary of the key transmission metrics at the latest date in the data:
-+R
@@ -837,24 +881,24 @@OUTPUT<
+3: Effective reproduction no. 0.88 (0.55 -- 1.3) +4: Rate of growth -0.016 (-0.068 -- 0.037) +5: Doubling/halving time (days) -42 (19 -- -10)measure estimate <char> <char> -1: New confirmed cases by infection date 7232 (4047 -- 13301) +1: New confirmed cases by infection date 7097 (3893 -- 12496) 2: Expected change in daily cases Likely decreasing -3: Effective reproduction no. 0.89 (0.58 -- 1.4) -4: Rate of growth -0.015 (-0.063 -- 0.043) -5: Doubling/halving time (days) -47 (16 -- -11)As these estimates are based on partial data, they have a wide uncertainty interval.
- +cases 7097 (3893 – 12496).
From the summary of our analysis we see that the expected change in daily cases is Likely decreasing with the estimated new confirmed -cases 7232 (4047 – 13301).
- -
The effective reproduction number \(R_t\) estimate (on the last date of the -data) is 0.89 (0.58 – 1.4).
- +data) is 0.88 (0.55 – 1.3). +
The exponential growth rate of case numbers is -0.015 (-0.063 – -0.043).
The exponential growth rate of case numbers is -0.016 (-0.068 – +0.037).
- +-42 (19 – -10).
The doubling time (the time taken for case numbers to double) is --47 (16 – -11).
@@ -919,7 +963,7 @@
confirm: number of confirmed cases for a region on a given date. -+R
@@ -947,33 +991,37 @@OUTPUT<
To find regional estimates, we use the same inputs as
-epinow()to the functionregional_epinow():+R
estimates_regional <- regional_epinow( reported_cases = regional_cases, generation_time = generation_time_opts(generation_time_fixed), - delays = delay_opts(incubation_period_fixed + reporting_delay_fixed), - rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd)) + delays = delay_opts( + incubation_period_fixed + reporting_delay_fixed + ), + rt = rt_opts( + prior = list(mean = rt_log_mean, sd = rt_log_sd) + ) )-OUTPUT
-+INFO [2024-03-28 21:35:04] Producing following optional outputs: regions, summary, samples, plots, latest -INFO [2024-03-28 21:35:04] Reporting estimates using data up to: 2020-04-28 -INFO [2024-03-28 21:35:04] No target directory specified so returning output -INFO [2024-03-28 21:35:04] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber -INFO [2024-03-28 21:35:04] Regions excluded: none -INFO [2024-03-28 22:21:26] Completed regional estimates -INFO [2024-03-28 22:21:26] Regions with estimates: 13 -INFO [2024-03-28 22:21:26] Regions with runtime errors: 0 -INFO [2024-03-28 22:21:26] Producing summary -INFO [2024-03-28 22:21:26] No summary directory specified so returning summary output -INFO [2024-03-28 22:21:26] No target directory specified so returning timingsINFO [2024-03-30 17:17:38] Producing following optional outputs: regions, summary, samples, plots, latest +INFO [2024-03-30 17:17:38] Reporting estimates using data up to: 2020-04-28 +INFO [2024-03-30 17:17:38] No target directory specified so returning output +INFO [2024-03-30 17:17:38] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber +INFO [2024-03-30 17:17:38] Regions excluded: none +INFO [2024-03-30 18:03:05] Completed regional estimates +INFO [2024-03-30 18:03:05] Regions with estimates: 13 +INFO [2024-03-30 18:03:05] Regions with runtime errors: 0 +INFO [2024-03-30 18:03:05] Producing summary +INFO [2024-03-30 18:03:05] No summary directory specified so returning summary output +INFO [2024-03-30 18:03:06] No target directory specified so returning timings+-R
@@ -984,57 +1032,57 @@OUTPUT<
-Region New confirmed cases by infection date <char> <char> - 1: East Midlands 347 (216 -- 568) - 2: East of England 540 (331 -- 840) - 3: England 3621 (2184 -- 5730) - 4: London 293 (191 -- 456) - 5: North East 251 (144 -- 412) - 6: North West 561 (326 -- 869) - 7: Northern Ireland 44 (24 -- 85) - 8: Scotland 288 (160 -- 538) - 9: South East 588 (343 -- 975) -10: South West 415 (290 -- 598) -11: Wales 94 (63 -- 134) -12: West Midlands 272 (145 -- 495) -13: Yorkshire and The Humber 476 (280 -- 787) + 1: East Midlands 347 (218 -- 566) + 2: East of England 550 (336 -- 853) + 3: England 3522 (2184 -- 5621) + 4: London 300 (187 -- 468) + 5: North East 253 (145 -- 416) + 6: North West 550 (337 -- 874) + 7: Northern Ireland 44 (23 -- 89) + 8: Scotland 283 (160 -- 531) + 9: South East 592 (354 -- 986) +10: South West 420 (290 -- 613) +11: Wales 95 (67 -- 138) +12: West Midlands 272 (144 -- 493) +13: Yorkshire and The Humber 481 (291 -- 770) Expected change in daily cases Effective reproduction no. <fctr> <char> 1: Likely increasing 1.2 (0.87 -- 1.6) - 2: Likely increasing 1.2 (0.83 -- 1.6) - 3: Likely decreasing 0.92 (0.63 -- 1.3) - 4: Likely decreasing 0.78 (0.56 -- 1.1) - 5: Likely decreasing 0.91 (0.59 -- 1.3) - 6: Likely decreasing 0.87 (0.58 -- 1.2) - 7: Likely decreasing 0.66 (0.4 -- 1.1) - 8: Likely decreasing 0.91 (0.58 -- 1.4) - 9: Stable 0.99 (0.66 -- 1.4) + 2: Likely increasing 1.2 (0.85 -- 1.6) + 3: Likely decreasing 0.91 (0.63 -- 1.3) + 4: Likely decreasing 0.8 (0.55 -- 1.1) + 5: Likely decreasing 0.91 (0.6 -- 1.3) + 6: Likely decreasing 0.85 (0.6 -- 1.2) + 7: Likely decreasing 0.64 (0.38 -- 1.1) + 8: Likely decreasing 0.9 (0.6 -- 1.4) + 9: Stable 0.99 (0.68 -- 1.4) 10: Increasing 1.4 (1.1 -- 1.8) -11: Decreasing 0.56 (0.41 -- 0.74) -12: Likely decreasing 0.71 (0.42 -- 1.1) -13: Stable 1 (0.69 -- 1.4) +11: Decreasing 0.57 (0.42 -- 0.76) +12: Likely decreasing 0.71 (0.43 -- 1.1) +13: Stable 1 (0.71 -- 1.4) Rate of growth Doubling/halving time (days) <char> <char> - 1: 0.024 (-0.018 -- 0.069) 29 (10 -- -39) - 2: 0.022 (-0.023 -- 0.066) 31 (11 -- -30) - 3: -0.01 (-0.054 -- 0.033) -68 (21 -- -13) - 4: -0.03 (-0.066 -- 0.01) -23 (67 -- -11) - 5: -0.012 (-0.06 -- 0.034) -57 (20 -- -12) - 6: -0.017 (-0.063 -- 0.022) -40 (32 -- -11) - 7: -0.05 (-0.097 -- 0.0067) -14 (100 -- -7.1) - 8: -0.012 (-0.062 -- 0.049) -58 (14 -- -11) - 9: -0.0017 (-0.049 -- 0.049) -410 (14 -- -14) -10: 0.046 (0.013 -- 0.085) 15 (8.2 -- 55) -11: -0.065 (-0.095 -- -0.036) -11 (-19 -- -7.3) -12: -0.041 (-0.092 -- 0.014) -17 (50 -- -7.5) -13: 0.0025 (-0.045 -- 0.051) 270 (14 -- -15)+ 1: 0.024 (-0.018 -- 0.07) 29 (9.9 -- -40) + 2: 0.024 (-0.021 -- 0.065) 28 (11 -- -34) + 3: -0.012 (-0.054 -- 0.031) -58 (22 -- -13) + 4: -0.028 (-0.068 -- 0.013) -25 (55 -- -10) + 5: -0.012 (-0.059 -- 0.035) -57 (20 -- -12) + 6: -0.02 (-0.06 -- 0.023) -35 (30 -- -12) + 7: -0.052 (-0.1 -- 0.012) -13 (58 -- -6.8) + 8: -0.013 (-0.059 -- 0.045) -52 (15 -- -12) + 9: -0.0012 (-0.046 -- 0.048) -560 (15 -- -15) +10: 0.047 (0.013 -- 0.086) 15 (8.1 -- 54) +11: -0.065 (-0.092 -- -0.033) -11 (-21 -- -7.6) +12: -0.042 (-0.091 -- 0.015) -17 (47 -- -7.6) +13: 0.0029 (-0.041 -- 0.049) 240 (14 -- -17) ++-R
estimates_regional$summary$plots$RSummary +
Summary
EpiNow2can be used to estimate transmission metrics @@ -1463,7 +1511,7 @@Challenge
HINT : data format
-+Ensure the data is in the correct format :
@@ -1482,7 +1530,7 @@
HINT : data format
-+To estimate the effective reproduction number and growth rate, we will use the function
@@ -1708,7 +1756,7 @@epinow().Prerequisites
-Instructor Note+The periods are relevant: Period 1 – 15 days where CFR is zero to indicate this is due to no reported deaths; Period from Mar 15 – Apr 26 @@ -1936,7 +1984,7 @@
Challenge
Give me a hint
-+Inspect the format of the data input.
@@ -1954,7 +2002,7 @@
Give me a hint
-+We can use
@@ -2020,7 +2068,7 @@dplyr::rename()to adapt the external data to fit the data input forcfr_static().Two biases that affect CFR estimation
Preferential ascertainment of severe cases
-+For diseases with a spectrum of clinical presentation, those cases that come to the attention of public health authorities and @@ -2040,7 +2088,7 @@
Preferential ascertainment of -
+During an ongoing epidemic, there is a delay between the time someone dies and the time their death is reported. Therefore, at @@ -2065,7 +2113,7 @@
Bias due to delayed reporting
-
Instructor Note+We can showcase this last bias using the concept described in this
@@ -2185,7 +2233,7 @@{cfr}vignette.OUTPUT<
-
How does {cfr} works?+To adjust the CFR, Nishiura et al., 2009 use the case and death incidence data to estimate the @@ -2258,7 +2306,7 @@
Use the
-<epidist>classWhen to use discrete distributions?+For
cfr_static()and all thecfr_*()family of functions, the most appropriate choice to pass are @@ -2302,7 +2350,7 @@Challenge
Give me a hint
-+
- Find the appropriate
@@ -2316,7 +2364,7 @@<epidist>object!Give me a hint
-+R @@ -2377,7 +2425,7 @@
OUTPUT<
-
What is the sampling process?+
-
CFR estimation during SARS outbreak, Hong Kong, 2003+Figures A and B show the cumulative numbers of cases and deaths of SARS, and Figure C shows the observed (biased) CFR estimates as a @@ -2599,7 +2647,7 @@
Interpret the early-stage CFR estimate
Give me a hint
-+We can use either visual inspection or analysis of the output data frames.
@@ -2612,7 +2660,7 @@Give me a hint
-+There is almost one month of difference.
Note that the estimate has considerable uncertainty at the beginning @@ -2630,7 +2678,7 @@
Show me the solution
-
rolling or time-varying?+The cfr package has a function called
cfr_time_varying()with different functionality that makes @@ -2711,7 +2759,7 @@Severity heterogeneity
Give me a hint
-+One way to do a stratified analysis is to apply a model to nested data. This
{tidyr}@@ -2727,7 +2775,7 @@Give me a hint
-+R @@ -2924,7 +2972,7 @@
Hospitalisation Fatality Risk (HFR)
Give me a hint
-+Use
@@ -2950,7 +2998,7 @@covidregionaldata::get_regional_data()to get COVID data from the region England from the country UK.R
Show me the solution
-+R diff --git a/instructor/create-forecast.html b/instructor/create-forecast.html index 2213a79a..1584066a 100644 --- a/instructor/create-forecast.html +++ b/instructor/create-forecast.html @@ -634,7 +634,7 @@
Challenge
HINT : data format
-+Ensure the data is in the correct format :
- @@ -651,7 +651,7 @@
HINT : data format
-+To estimate the effective reproduction number and growth rate, we will use the function
diff --git a/instructor/images.html b/instructor/images.html index 432b597c..b4f49d0c 100644 --- a/instructor/images.html +++ b/instructor/images.html @@ -283,11 +283,11 @@epinow().Figure 1
Figure 2
Figure 3
-
-Figure 4
+Figure 4
+
Figure 5
-Create a short-term forecast
+Create a short-term forecast
Figure 1
diff --git a/instructor/index.html b/instructor/index.html index f1d8109e..197a0ea9 100644 --- a/instructor/index.html +++ b/instructor/index.html @@ -350,7 +350,7 @@