Proofreading and minor edits

DESCRIPTION

@@ -21,15 +21,15 @@ Authors@R:
              email = "me.dewitt.jr@gmail.com",
              comment = c(ORCID = "0000-0001-8940-1967")))
 Description: Tools to enable flexible and efficient hierarchical nowcasting of
-    right truncated epidemiological time-series using a semi-mechanistic
-    Bayesian method with support for a range of reporting and generative
+    right-truncated epidemiological time-series using a semi-mechanistic
+    Bayesian model with support for a range of reporting and generative
     processes. Nowcasting, in this context, is gaining situational awareness
     using currently available observations and the reporting patterns of
     historical observations. This can be useful when tracking the spread of
-    infectious disease in real-time as otherwise changes in trends can be
-    obfuscated by partial reporting or their detection may be delayed due to
+    infectious disease in real-time: without nowcasting, changes in trends can
+    be obfuscated by partial reporting or their detection may be delayed due to
     the use of simpler methods like truncation. While the package has been
-    designed with the epidemiological application in mind, it could be applied
+    designed with epidemiological applications in mind, it could be applied
     to any set of right-truncated time-series count data.
 License: MIT + file LICENSE
 URL: https://epiforecasts.io/epinowcast/,

README.Rmd

@@ -28,7 +28,7 @@ license](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/
 
 [![DOI](https://zenodo.org/badge/422611952.svg)](https://zenodo.org/badge/latestdoi/422611952)
 
-Tools to enable flexible and efficient hierarchical nowcasting of right truncated epidemiological time-series using a semi-mechanistic Bayesian method with support for a range of reporting and generative processes. Nowcasting, in this context, is gaining situational awareness using currently available observations and the reporting patterns of historical observations. This can be useful when tracking the spread of infectious disease in real-time as otherwise changes in trends can be obfuscated by partial reporting or their detection may be delayed due to the use of simpler methods like truncation. While the package has been designed with the epidemiological application in mind, it could be applied to any set of right-truncated time-series count data.
+Tools to enable flexible and efficient hierarchical nowcasting of right-truncated epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes. Nowcasting, in this context, is gaining situational awareness using currently available observations and the reporting patterns of historical observations. This can be useful when tracking the spread of infectious disease in real-time: without nowcasting, changes in trends can be obfuscated by partial reporting or their detection may be delayed due to the use of simpler methods like truncation. While the package has been designed with epidemiological applications in mind, it could be applied to any set of right-truncated time-series count data.
 
 ## Installation
 

README.md

@@ -17,16 +17,16 @@ contributors](https://img.shields.io/github/contributors/epiforecasts/epinowcast
 
 [![DOI](https://zenodo.org/badge/422611952.svg)](https://zenodo.org/badge/latestdoi/422611952)
 
-Tools to enable flexible and efficient hierarchical nowcasting of right
-truncated epidemiological time-series using a semi-mechanistic Bayesian
-method with support for a range of reporting and generative processes.
+Tools to enable flexible and efficient hierarchical nowcasting of
+right-truncated epidemiological time-series using a semi-mechanistic Bayesian
+model with support for a range of reporting and generative processes.
 Nowcasting, in this context, is gaining situational awareness using
 currently available observations and the reporting patterns of
 historical observations. This can be useful when tracking the spread of
-infectious disease in real-time as otherwise changes in trends can be
+infectious disease in real-time: without nowcasting, changes in trends can be
 obfuscated by partial reporting or their detection may be delayed due to
 the use of simpler methods like truncation. While the package has been
-designed with the epidemiological application in mind, it could be
+designed with epidemiological applications in mind, it could be
 applied to any set of right-truncated time-series count data.
 
 ## Installation

vignettes/germany-age-stratified-nowcasting.Rmd

@@ -41,7 +41,7 @@ options(mc.cores = 2)
 
 # Data
 
-Nowcasting is effectively the estimation of reporting patterns for recently reported data. This requires data on these patterns for previous observations and typically this means the time series of data as reported on multiple consecutive days (in theory non-consecutive days could be used but this is not yet supported in `epinowcast`).
+Nowcasting is effectively the estimation of reporting patterns for recently reported data. This requires data on these patterns for previous observations, which typically means the time series of data as reported on multiple consecutive days (in theory non-consecutive days could be used but this is not yet supported in `epinowcast`).
 
 Here we use COVID-19 hospitalisations by date of positive test in Germany stratified by age group available from up to the 1st of September 2020 (with 40 days of data included prior to this) as an example of data available in real-time and hospitalisations by date of positive test available up to 20th of October to represent hospitalisations as finally reported. These data are sourced from the [Robert Koch Institute via the Germany Nowcasting hub](https://github.com/KITmetricslab/hospitalization-nowcast-hub/wiki/Truth-data#role-an-definition-of-the-seven-day-hospitalization-incidence) where they are deconvolved from weekly data and days with negative reported hospitalisations are adjusted.
 
@@ -69,7 +69,7 @@ retro_nat_germany
 #> 6027:     2021-07-23       DE       80+       5  2021-09-01
 ```
 
-Similarly we then find the data that were available on the 20th of October for these dates which will serve as the target "true" data.
+Similarly we then find the data that were available on the 20th of October for these dates, which will serve as the target "true" data.
 
 
 ```r
@@ -81,7 +81,7 @@ latest_nat_germany <- nat_germany_hosp |>
 
 # Data preprocessing
 
-`epinowcast` works by assuming data has been preprocessed into the reporting format it requires coupled with meta data for both reference and report dates. `enw_preprocess_data()` can be used for this though users can also use the internal functions to produce their own custom preprocessing steps. It is at this stage that arbitrary groupings of observations can be defined which will then be propagated throughout all subsequent modelling steps. Here we have data stratified by age and so grouped by age group but in principle this could be any grouping or combination of groups independent of the reference and report date models. Here we also assume a maximum delay required to make the model identifiable. We set this to 40 days due to evidence of long reporting delays in this example data but note that in most cases the majority of right truncation occurs in the first few days and that increasing the maximum delay has a non-linear effect on run-time (i.e a 20 day delay will be much faster to fit a model for than a 40 day delay). Note also that under the current formulation delays longer than the maximum are ignored so that the adjusted estimate is really for data reported after the maximum delay rather than for finally reported data.
+`epinowcast` works by assuming data has been preprocessed into the reporting format it requires, coupled with meta data for both reference and report dates. `enw_preprocess_data()` can be used for this, although users can also use the internal functions to produce their own custom preprocessing steps. It is at this stage that arbitrary groupings of observations can be defined, which will then be propagated throughout all subsequent modelling steps. Here we have data stratified by age, and hence grouped by age group, but in principle this could be any grouping or combination of groups independent of the reference and report date models. We furthermore assume a maximum delay required to make the model identifiable. We set this to 40 days due to evidence of long reporting delays in this example data. However, note that in most cases the majority of right truncation occurs in the first few days and that increasing the maximum delay has a non-linear effect on run-time (i.e. a model with a maximum delay of 20 days will be much faster to fit than with 40 days). Note also that under the current formulation delays longer than the maximum are ignored so that the adjusted estimate is really for data reported after the maximum delay rather than for finally reported data.
 
 Another key modelling choice we make at this stage is to model overall hospitalisations jointly with age groups rather than as an aggregation of age group estimates. This implicitly assumes that aggregated and non-aggregated data are not comparable (which may or may not be the case) but that the reporting process shares some of the same mechanisms. Another way to approach this would be to only model age stratified hospitalisations and then to aggregate the nowcast estimates into total counts after fitting the model.
 

vignettes/germany-age-stratified-nowcasting.Rmd.orig

@@ -47,7 +47,7 @@ options(mc.cores = 2)
 
 # Data
 
-Nowcasting is effectively the estimation of reporting patterns for recently reported data. This requires data on these patterns for previous observations and typically this means the time series of data as reported on multiple consecutive days (in theory non-consecutive days could be used but this is not yet supported in `epinowcast`).
+Nowcasting is effectively the estimation of reporting patterns for recently reported data. This requires data on these patterns for previous observations, which typically means the time series of data as reported on multiple consecutive days (in theory non-consecutive days could be used but this is not yet supported in `epinowcast`).
 
 Here we use COVID-19 hospitalisations by date of positive test in Germany stratified by age group available from up to the 1st of September 2020 (with 40 days of data included prior to this) as an example of data available in real-time and hospitalisations by date of positive test available up to 20th of October to represent hospitalisations as finally reported. These data are sourced from the [Robert Koch Institute via the Germany Nowcasting hub](https://github.com/KITmetricslab/hospitalization-nowcast-hub/wiki/Truth-data#role-an-definition-of-the-seven-day-hospitalization-incidence) where they are deconvolved from weekly data and days with negative reported hospitalisations are adjusted.
 
@@ -62,7 +62,7 @@ retro_nat_germany <- nat_germany_hosp |>
 retro_nat_germany
 ```
 
-Similarly we then find the data that were available on the 20th of October for these dates which will serve as the target "true" data.
+Similarly we then find the data that were available on the 20th of October for these dates, which will serve as the target "true" data.
 
 ```{r}
 latest_nat_germany <- nat_germany_hosp |>
@@ -73,7 +73,7 @@ latest_nat_germany <- nat_germany_hosp |>
 
 # Data preprocessing
 
-`epinowcast` works by assuming data has been preprocessed into the reporting format it requires coupled with meta data for both reference and report dates. `enw_preprocess_data()` can be used for this though users can also use the internal functions to produce their own custom preprocessing steps. It is at this stage that arbitrary groupings of observations can be defined which will then be propagated throughout all subsequent modelling steps. Here we have data stratified by age and so grouped by age group but in principle this could be any grouping or combination of groups independent of the reference and report date models. Here we also assume a maximum delay required to make the model identifiable. We set this to 40 days due to evidence of long reporting delays in this example data but note that in most cases the majority of right truncation occurs in the first few days and that increasing the maximum delay has a non-linear effect on run-time (i.e a 20 day delay will be much faster to fit a model for than a 40 day delay). Note also that under the current formulation delays longer than the maximum are ignored so that the adjusted estimate is really for data reported after the maximum delay rather than for finally reported data.
+`epinowcast` works by assuming data has been preprocessed into the reporting format it requires, coupled with meta data for both reference and report dates. `enw_preprocess_data()` can be used for this, although users can also use the internal functions to produce their own custom preprocessing steps. It is at this stage that arbitrary groupings of observations can be defined, which will then be propagated throughout all subsequent modelling steps. Here we have data stratified by age, and hence grouped by age group, but in principle this could be any grouping or combination of groups independent of the reference and report date models. We furthermore assume a maximum delay required to make the model identifiable. We set this to 40 days due to evidence of long reporting delays in this example data. However, note that in most cases the majority of right truncation occurs in the first few days and that increasing the maximum delay has a non-linear effect on run-time (i.e. a model with a maximum delay of 20 days will be much faster to fit than with 40 days). Note also that under the current formulation delays longer than the maximum are ignored so that the adjusted estimate is really for data reported after the maximum delay rather than for finally reported data.
 
 Another key modelling choice we make at this stage is to model overall hospitalisations jointly with age groups rather than as an aggregation of age group estimates. This implicitly assumes that aggregated and non-aggregated data are not comparable (which may or may not be the case) but that the reporting process shares some of the same mechanisms. Another way to approach this would be to only model age stratified hospitalisations and then to aggregate the nowcast estimates into total counts after fitting the model.