Merge branch 'develop'

README.md

@@ -1,6 +1,6 @@
 This is a R project for creating demo EPIDEMIA forecasting reports using the epidemiar package. The final report presents a malaria forecasting report for 47 woredas in the Amhara region for the past 18 weeks through 8 weeks forecasted into the future (26 total weeks). Malaria is broken out by species: _Plasmodium falciparum_ and mixed species, and also _P. vivax_. The report includes environmental and epidemiological surveillance data.
 
-Demo is based off a real implementation for forecasting malaria in the Amhara region of Ethiopia, however surveillance data in this demo have been *simulated*. These data should not be used beyond this demonstration of what a disease report could look like, and should not be taken as indictative of actual epidemiological data.
+Demo is based off a real implementation for forecasting malaria in the Amhara region of Ethiopia, however surveillance data in this demo have been *simulated*. These data are artificial and should not be used for research of public health purposes. These data are only for this demonstration of what a disease report could look like.
 
 Open `epidemiar_demo.RProj`, and the main script to open and run is `run_epidemiar_demo.R`.
 

documentation/walkthrough.Rmd

@@ -2,7 +2,7 @@
 title: |
   | EPIDEMIA Malaria Forecasting System: 
   | Detailed Walk-through Guide
-subtitle: For epidemiar-demo version 1.0.0
+subtitle: For epidemiar-demo version 1.0.1
 author: |
   | Dawn Nekorchuk, Michael Wimberly, and EPIDEMIA Team Members
   | Department of Geography and Environmental Sustainability, University of Oklahoma
@@ -28,7 +28,7 @@ knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
 
 This detailed walk-through will explain each section of the `run_epidemiar_demo.R` script in the `epidemiar-demo` R project. 
 
-All surveillance data in this demo is *simulated* and for *demo use only*. The environmental data, from Google Earth Engine, is real. This walkthrough is adapted from documentation given to our colleagues in Ethiopia. 
+All surveillance data in this demo is *simulated* and for *demo use only*. The epidemiolgoical data are artificial and should not be used for research of public health purposes. The environmental data, from Google Earth Engine, is real. This walkthrough is adapted from documentation given to our colleagues in Ethiopia. 
 
 For more details on the `epidemiar` package, see the vignettes:
 
@@ -143,6 +143,8 @@ report_woredas <- readxl::read_xlsx("data/woredas.xlsx", na = "NA") %>%
 
 For the epidemiology data, we will need weekly case counts per woreda of confirmed _P. falciparum_ & mixed, and _P. vivax_ malaria. To merge data from multiple files, we will use a "data corral" subfolder `data_epidemiological` in the `epidemia-demo` folder. 
 
+The data in this demo project have been **simulated**. These data should not be used beyond this demonstration of what a disease report could look like, and should not be taken as indictative of actual epidemiological data.
+
 The `corral_epidemiological()` function will merge all xlsx files in this directory, so you can have multiple files, e.g. one for each year, month, or even by week. No special file names are expected, so you are free to use your standard naming convention.  The script is expecting the file to be in your standard format. Specifically, it is looking for the fields:  `Woreda/Hospital`, `Budget Year`, `Epi- Week`, `Blood film P. falciparum`, `RDT P. falciparum`, `Blood film P. vivax`, `RDT P. vivax`. 
 
 There should be a line for each week and woreda, even for missing data. Any missing (NA) values in the data will be filled in by linear interpolation inside of the epidemiar modeling functions. Gaps in the data, like missing weeks for a woreda, will trigger an error and stop the script. A log file of missing dates will be written, `log_missing_report_epidemiology.csv`, which can be opened with Excel.