My team is currently exploring the use of the Modeling app. We have a few questions related to data modeling, configuration, and best practices, and would appreciate your guidance:
Is there a standard or acceptable radius for how far a weather station’s measurements can be considered representative of a community? Or, what is the maximum size or radius should a weather station cover accurately?
Is it possible to configure modeling so that cases can be analyzed based on patients’ residential location (e.g., coded at community level using an option set) instead of—or in addition to—the reporting facility (org unit), and can option sets be used as a selectable alternative to org units for this purpose?
Is it possible to limit the data sets processed by the analytics table by program (aggregate, tracker event) so that data sets like climate data are only processed during off-hours (e.g. 12am to 7am)
4.What is the value of using the local climate dataset versus global datasets for predictive modeling, and should we advise the country to implement local sensors?
In addition to triaging this to the team, I might as well provide you with some of the related resources:
Regarding acceptable radius this seems to differ from case to case (elevation and other factors) and which dataset is being used (CHIRPs, ERA5-Land…etc).
I do recommend giving more details about the use-case and why it’s important to handle this using optionsets instead or using a data element with OU because it’s necessary to know whether this will work for the final ‘analytics’ you’re expecting.
I’m pretty sure for this, you can schedule a job in the schedular app and you can select which programs to skip under the ‘skip programs’ dropdown
