Distance sampling for malaria reporting probability, full version

This can take up to 5-10 minutes

This tool was developed by the University of Glasgow, as part of the MiRA collaborative project (Malaria in Insecticide Resistant Africa)

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 KomoƩ 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.

INSTRUCTIONS

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 10 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 the full version of the full tool. Here you can generate the general map of reporting probability and the effective catchment areas. In addition you will generate the map of probability of reporting at each individual clinic. However, this process can take several minutes. For a lighter version of the tool (where you can generate only the general map of reporting probability and the effective catchment areas), please click here!

To report a bug, or for any question, please write to luca.nelli@glasgow.ac.uk .

Map of overall reporting probability

Map of catchment areas

Maps of reporting probability to each of the selected clinics

Clinic 1 (Banfora-Secteur 15)

Clinic 2 (Banfora-Secteur 2)

Clinic 3 (Banfora-Secteur 6)

Clinic 4 (Banfora-Secteur 8)

Clinic 5 (Beguele)

Clinic 6 (Boko)

Clinic 7 (Boulo)

Clinic 8 (Boussara Brousse)

Clinic 9 (Dandougou)

Clinic 10 (Degue-Degue)

Clinic 11 (Deregoue 1)

Clinic 12 (Diamon)

Clinic 13 (Diarabakoko)

Clinic 14 (Diaya)

Clinic 15 (Doutie)

Clinic 16 (Dramandougou)

Clinic 17 (Fandiora)

Clinic 18 (Farandjan)

Clinic 19 (Folonzo)

Clinic 20 (Gouandougou)

Clinic 21 (Gouindougouba)

Clinic 22 (Kankounandeni)

Clinic 23 (Karfiguela)

Clinic 24 (Kassande)

Clinic 25 (Kimini)

Clinic 26 (Koflande)

Clinic 27 (Kouendi)

Clinic 28 (Kouere)

Clinic 29 (Koutoura)

Clinic 30 (Labola Fokara)

Clinic 31 (Letiefesso)

Clinic 32 (Madiasso)

Clinic 33 (Mangodara)

Clinic 34 (Mondon)

Clinic 35 (Moussodougou)

Clinic 36 (Nafona)

Clinic 37 (Niangoloko-Secteur 1)

Clinic 38 (Niangoloko-Secteur 3)

Clinic 39 (Niangoloko-Secteur 5)

Clinic 40 (Nofesso)

Clinic 41 (Norkama)

Clinic 42 (Noumoutiedougou)

Clinic 43 (Ouangolodougou)

Clinic 44 (Ouo)

Clinic 45 (Panga)

Clinic 46 (Poikoro)

Clinic 47 (Sampobien)

Clinic 48 (Sideradougou)

Clinic 49 (Siniena)

Clinic 50 (Sirakoro)

Clinic 51 (Sokoura)

Clinic 52 (Sokoura 2)

Clinic 53 (Soubakaniedougou)

Clinic 54 (Takaledougou)

Clinic 55 (Tarfila)

Clinic 56 (Tengrela)

Clinic 57 (Tiefora)

Clinic 58 (Tiekouna)

Clinic 59 (Timperba)

Clinic 60 (Torandougou)

Clinic 61 (Torokoro)

Clinic 62 (Toumousseni)

Clinic 63 (Yendere)

Clinic 64 (Zangazoli)