This tool can be used to explore the prediction of the model presented in Nelli et al. (2020), under different scenarios of health centre distribution, age and sex of the patients and season, in the Komoé district (South-west Burkina Faso).
On the input panel you can select a subset of clinics to include or exclude from the model. You can also select sex ang age (in years) of the patient and the season.
In this way, you can evaluate the contribution of each clinic in the public health network to case reporting and overall health centre accessibility, and highlight hotspots of un-covered areas under different and custom scenarios (for example in the case of one or more clinics being closed).
Note that you won't see great differences in the outputs when changing age, sex and season, because these variables didn't strongly affect the final model (please see detailes on the paper). However, this version of the tool shows how such (and potentially other) variables can be included if needed.
To begin, click on the 'Generate/Update Maps' button. You will generate the maps starting from the entire set of 64 clinics in the study area.
Please note that this process can take up to 2-3 minutes to complete. Don't worry if you don't see any progress bar (it will be implemented soon); the server is running.
You can then select a subset of clinics to include in the model by swithiching them on and off. Each time you select a new subset you have to click on 'Generate/Update Maps' again.
Please note that this is a lighter version of the full tool. Here you can see only the general map of reporting probability and the effective catchment areas. For the full version of the tool (where you can generate the map of probability of reporting at each individual clinic, please click here!
To report a bug, or for any question, please write to email@example.com .
Map of overall reporting probability