diff --git a/articles/how-to-run-ITHIM.html b/articles/how-to-run-ITHIM.html index 649cdae9..140923e8 100644 --- a/articles/how-to-run-ITHIM.html +++ b/articles/how-to-run-ITHIM.html @@ -107,11 +107,11 @@
To change to the Bogota branch for which this guide was written, navigate to the ITHIM-R folder within the command window, then type:
‘git checkout bogota’
- +The Github repository has now been downloaded to your computer and the working branch has been set to the Bogota branch which is the branch this documentation refers to.
@@ -127,7 +127,7 @@This will install the ITHIM-R package but it will also most likely initially throw an error message ‘Exited with status 1’ stating that some other R packages that are needed for the ITHIM-R package have not @@ -135,7 +135,7 @@
Once all missing R packages have been installed, go back to the ‘Build’ tab and install the ITHIM-R package again. It should now install smoothly.
@@ -150,14 +150,14 @@Finally, you will also have to install the ‘drpa’ package from Github (drpa) by first installing the ‘remotes’ package as explained above and then by typing
remotes::install_github(“meta-analyses/drpa”)
into the RStudio console.
- +5 files specific to each city saved in a directory of the city’s -name. (See inst/ext/local/bogota +name. (See inst/ext/local/bogota for example files and also below for further information). These files need to be saved in the inst/extdata/local/‘city’ folder, where ‘city’ is replaced by the appropriate city name.
global datasets - such as the dose-response relationships for the -air pollution pathway e.g.. These files can be found in the inst/extdata/global folder and usually do -not need to be changed. (See below for further information.)
Travel survey (example trips dataset). A table of all trips taken by a group of people on a given day. It also includes people who take no trips. This data should come from an official travel survey for the city where possible.
@@ -211,9 +212,9 @@Injury events (example +injuries dataset). A table of recorded road-traffic injury +(fatality) events in a city during one or more years.
Disease burden data (example +burden dataset). This gives the burden of disease for different +diseases. If no city specific information exists, country level +information can be used.
Measure
(death/YLL);
@@ -240,7 +241,7 @@ Population of city (example population dataset). This information is used to scale the Burden of Disease data to the city’s population in question.
Physical activity survey (example +physical activity dataset). This is used to represent the physical +activity levels in the city and should be taken from an official +physical activity survey of the city where possible.
sex
, age
,
@@ -273,7 +274,7 @@ Disease
interaction table. A table with a list of diseases/causes
for a specific pathway such as Air Pollution
and
Physical Activity
and also the interaction between
@@ -289,7 +290,7 @@
drpa
.Ventilation rate tables. This data can be found -in inst/extdata/global/ventilation_rate +in inst/extdata/global/ventilation_rate and details how much air is being inhaled by different people in the model population.
Once these parameter values have been updated, click on ‘Source’ and the model should run.
- +It produces pop-up windows showing the plots of the results giving the years of life lost for each scenario and required disease outcome.
@@ -330,7 +331,7 @@The health effects of ITHIM are presented as years of life lost
(YLLs) and number of attributable deaths. The background burden data for
the study areas are estimated from the Global Burden of
diff --git a/index.html b/index.html
index 7f180a71..99c41e30 100644
--- a/index.html
+++ b/index.html
@@ -77,7 +77,7 @@ We have written a We have written a The health effects of ITHIM are presented as years of life lost (YLLs) and number of attributable deaths. Background burden data for study areas are estimated from Global Burden of Disease studies. If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found. Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: The hypothetical commands You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>. The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.Website for the package
How to use the package
-how-to
guide that explains how to install the ITHIM-R package, how to run the model ITHIM-Global model using this package and how to produce summaries of the key results. Please read it here: how to run ITHIM?how-to
guide that explains how to install the ITHIM-R package, how to run the ITHIM-Global model using this package and how to produce summaries of the key results. Please read it here: how to run ITHIM?Citation
@@ -118,8 +118,8 @@ CO2 EmissionsHealth outcomes
17. Interpretation of Sectio
How to Apply These Terms to Your New Programs
<one line to give the program's name and a brief idea of what it does.>
-Copyright (C) <year> <name of author>
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see <http://www.gnu.org/licenses/>.
<one line to give the program's name and a brief idea of what it does.>
+Copyright (C) <year> <name of author>
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program. If not, see <http://www.gnu.org/licenses/>.
<program> Copyright (C) <year> <name of author>
-This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
-This is free software, and you are welcome to redistribute it
-under certain conditions; type 'show c' for details.
<program> Copyright (C) <year> <name of author>
+for details type 'show w'.
+ This program comes with ABSOLUTELY NO WARRANTY;
+ This is free software, and you are welcome to redistribute it'show c' for details. under certain conditions; type
show w
and show c
should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.Details
from other modes) that appear in the injury data. - This data is used to parameterise the
Poisson injury model.
A new list (injuries_list) is created containing all strike and casualty mode and age and sex combinations
- together with strike and casualty mode distances (add_distance_columns.R) for the baseline and all scenarios. For
+ together with strike and casualty mode distances (add_distance_columns()
) for the baseline and all scenarios. For
the whw model, any strike mode and casualty pairs where strike mode equals casualty mode
are removed as fatalities for these combinations have already been added to the nov matrix.
Combinations which do not have a non-zero strike or casualty mode distance
- are also removed. - This list will later be used in the injuries_function_2.R function
+ are also removed. This list will later be used in the injuries_function_2()
function
to predict fatality counts using the Poisson injury regression model.
The casualty and strike mode exponents used to account for the safety in number effect are added to both the injuries_for_model and injuries_list.
loop through the scenarios:
assign the relative risk for the given disease, age group, quantile and scenario to the - relevant people in the synthetic population by calling the AP_dose_response.R function
AP_dose_response()
functionThis function performs the following steps:
generate distance and duration matrices by age, sex, mode and scenario from the ithim_object$trip_scen_sets - for the synthetic population by calling the dist_dur_tbls.R function
dist_dur_tbls()
function
find the total mode distances for each scenario and scale this up to the distance travelled by the entire population by using the demographic information for the city
in order to scale the distances by age, sex, mode and scenario to the entire population, the proportion of @@ -84,7 +84,7 @@
the distances_for_injury_function.R function is called which creates a list inj_distances that is added +
the distances_for_injury_function()
function is called which creates a list inj_distances that is added
to ithim_object containing the following matrices:
true_distances (population mode distances by age and sex with all walking modes and all car modes combined and bus drivers added where relevant)
injuries_list (list of all strike, casualty, age, sex and mode distance combinations for baseline diff --git a/reference/get_synthetic_from_trips.html b/reference/get_synthetic_from_trips.html index 4cd6e45f..ae7de05b 100644 --- a/reference/get_synthetic_from_trips.html +++ b/reference/get_synthetic_from_trips.html @@ -69,18 +69,18 @@
The columns from the TRIP_SET are put into the correct order
multiply the trip distances, stage distances, and durations by the day_to_week scalar and then divide by 7 to get the distances and durations of an 'average' day of the week
add bus_driver and truck trips if required (add_ghost_trips.R)
add bus_driver and truck trips if required (add_ghost_trips()
)
add personal motorcycle trips if needed (call the appropriate function)
add commercial motorcycle trips if required (add_ghost_trips.R)
add commercial motorcycle trips if required (add_ghost_trips()
)
build the synthetic population by creating a data set that contains the (non-zero) participant ids and demographic information from the trip data set and adds work and leisure MMET - values by calling create_synth_pop.R (non travel entries in the trip data set are also removed)
adds car driver trips if required (add_ghost_trips.R)
call the ithim_setup_baseline_scenario.R function to get the baseline data into the correct format
+ values by calling create_synth_pop()
(non travel entries in the trip data set are also removed)
adds car driver trips if required (add_ghost_trips()
)
call the ithim_setup_baseline_scenario()
function to get the baseline data into the correct format
for the creation of the different scenarios
create the required scenarios by calling the appropriate function
add walk to pt trips and combine the scenarios into one dataframe by calling the - walk_to_pt_and_combine_scen.R function
walk_to_pt_and_combine_scen()
function