This is a simulated data and full demonstration project to go along with the epidemiar package. This version of the project will need epidemiar v3.1.0 or higher.
Forecast report edits:
Updated all input and function calling to use epidemiar version 3.1.0.
Demonstration scripts were added/updated for running multiple weeks of reports at once or creating model regression objects for caching for later use.
Corrected multiple display issues that could appear in various situations. The issue with non-connected points (or wrongly connected points) in environmental time series graphs when there were imputed values was fixed. The legend labeling spacing bug depending on ggplot package version was fixed. The environmental time series graph legend was updated reflecting the newer gap filling methods of missing data in epidemiar.
GEE scripts
The javascript code to be run in the GEE web browser was updated to version 3.1 which has a much more interactive and stream-lined user interface, and is able to be used as a GEE app for short time ranges: https://dawneko.users.earthengine.app/view/epidemiar-demo
Includes an example script using the new epidemia-gee package (example only, as the python package is designed for a different dataset).
Validation report:
New section for week-ahead time-series: These graphs show the observed case counts and each of the week-ahead predictions as a series (i.e. all one-week ahead predictions are a series, all two-week, etc.) for the forecast model. These graphs can be used to visualize how the predictions are behaving n-number of weeks ahead of the target forecast week.
The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) Forecasting System is a set of tools coded in free, open-access software, that integrate surveillance and environmental data to model and create short-term forecasts for environmentally-mediated diseases. The updated EPIDEMIA forecasting system now is a core set of two packages for supplying functions, epidemiar and clusterapply, and a demonstration data project, epidemiar-demo, for simulated data and example scripts. There is also a fourth package available, epidemia-gee, that demonstrates the ability to connect directly to Google Earth Engine from R via python (with a sample script included in epidemiar-demo).
epidemiar: https://github.com/EcoGRAPH/epidemiar/releases/latest
clusterapply: https://github.com/EcoGRAPH/clusterapply/releases/latest
epidemiar-demo: https://github.com/EcoGRAPH/epidemiar-demo/releases/latest
epidemia-gee: https://github.com/EcoGRAPH/epidemia_gee/releases/latest