The app is live here!
To run the app locally, ensure run_me_first.R is run before
application code to initialise covariate data!
- Start with all districts in India
- Districts are filtered and ranked by modelling outputs + other relevant epidemiological covariates
- Shortlisted districts are grouped into transmission (low, medium, high) using national state-level data, and districts are selected from each group based on local transmission and infrastructure for surveillance (not implemented in app)
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To operate the application, navigate to the Edit Sidebar tab. Drag filtering covariates from left to right, and edit the order of covariates if needed. Set the number of districts to be retained by each filter once it appears in the sidebar at far left.
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Press the “Update filters” button to see your preferences reflected in the Filtering Maps, Inspect Districts, and Filtering Table tabs:
- The filters are visualised in sequence in the Filtering Maps tab.
- The shortlisted districts are visualised in a scrollable map in the Inspect Districts tab, and
- The final set of districts are downloadable from the Filtering Table tab.
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To view all available filtering covariates, summarised by district, navigate to the All Covariates tab. Log-transfrom the covariates if needed.
There are several covariates included in this tool:
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Plasmodium falciparum temperature suitability, 2010 (Malaria Atlas Project)
This layer shows the temperature suitability for Plasmodium falciparum transmission globally, calculated using a dynamic biological model and spatial time series temperature data. The temperature data used was a time series across an average year (1950-2000).
Gething PW., Van Boeckel TP., Smith DL., Guerra CA., Patil AP., Snow RW., Hay SI., Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax Parasites & Vectors. May 2011 4: 92. https://doi.org/10.1186/1756-3305-4-92 -
Plasmodium falciparum parasite rate, 2021 (Malaria Atlas Project)
This layer is a time-aware mosaic data set showing predicted age-standardised parasite rate for Plasmodium falciparum malaria for children two to ten years of age (PfPR2-10) for each year. We are using PfPR2-10 estimates for 2021.
Weiss DJ, Lucas TCD, Nguyen M, et al. Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000–17: a spatial and temporal modelling study. Lancet 2019; published online June 19. https://doi.org/10.1016/S0140-6736(19)31097-9 -
Predicted travel time to nearest cities in 2015 (Malaria Atlas Project)
This is a predictive map showing the estimated time to travel (in minutes) from every point on earth to the nearest city (in terms of travel time). Contains data from OpenStreetMap © OpenStreetMap contributors.
Weiss DJ., Nelson A., Gibson HS., Temperley WH., Peedell S., Lieber A., Hancher M., Poyart E., Belchior S., Fullman N., Mappin B., Dalrymple U., Rozier J., Lucas TCD., Howes RE., Tusting LS., Kang SY., Cameron E., Bisanzio D., Battle KE., Bhatt S., Gething PW., A global map of travel time to cities to assess inequalities in accessibility in 2015 Nature. January 2018 553: 333–336. http://doi.org/10.1038/nature25181 -
Human population density estimates (WorldPop project)
WorldPop program provides high resolution, open and contemporary data on human population distributions.
https://www.worldpop.org/methods/populations -
dhps540E predicted resistance, 2021
Predicted median estimated prevalence of the dhp540E marker. -
dhps540E predicted resistance (uncertainty), 2021
Standard deviate of the estimated prevalence of the dhp540E marker. -
kelch13 predicted resistance, 2021
Predicted median estimated prevalence of kelch13 markers (any markers, ie not wildtype). -
kelch13 predicted resistance (uncertainty), 2021
Standard deviate of the estimated prevalence of kelch13 markers (any markers, ie not wildtype).
Covariate data are summarised for each district in
district_summary.csv. For accessibility, human population density, Pf
parasite rate and Pf temperature suitability, these summaries are the
mean value of the dataset within the raster masked by the district
boundary. For model outputs, the mean of both the median model
prediction and model uncertainty are provided, as well as the standard
deviation of median model predictions (where the former is an average of
between-prediction uncertainty, and the latter is the a measure of
variation in median predictions across a district). There are the
columns k13_median, k13_mediansd, k13_sd. k13_median and
k13_sd are means of the median prediction and standard deviation of
the k13 model in the district, respectively, while k13_mediansd is the
standard deviation of model median predictions.
All code successfully run with following software versions:
R version 4.4.2 (2024-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.1.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/London
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rsconnect_1.5.0 leaflet_2.2.2 cowplot_1.1.3 sortable_0.5.0 sf_1.0-19
[6] terra_1.8-42 markdown_2.0 DT_0.33 lubridate_1.9.4 forcats_1.0.0
[11] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4 readr_2.1.5 tidyr_1.3.1
[16] tibble_3.3.0 ggplot2_3.5.2 tidyverse_2.0.0 viridisLite_0.4.2 shiny_1.10.0
loaded via a namespace (and not attached):
[1] gtable_0.3.6 bslib_0.8.0 xfun_0.51 learnr_0.11.5
[5] htmlwidgets_1.6.4 tzdb_0.5.0 vctrs_0.6.5 tools_4.4.2
[9] crosstalk_1.2.1 generics_0.1.3 proxy_0.4-27 pkgconfig_2.0.3
[13] KernSmooth_2.23-24 RColorBrewer_1.1-3 assertthat_0.2.1 lifecycle_1.0.4
[17] compiler_4.4.2 farver_2.1.2 textshaping_0.4.1 fontawesome_0.5.3
[21] codetools_0.2-20 litedown_0.6 httpuv_1.6.15 sass_0.4.9
[25] htmltools_0.5.8.1 class_7.3-22 yaml_2.3.10 jquerylib_0.1.4
[29] later_1.4.1 pillar_1.11.0 ellipsis_0.3.2 classInt_0.4-11
[33] cachem_1.1.0 mime_0.12 commonmark_1.9.2 tidyselect_1.2.1
[37] digest_0.6.37 stringi_1.8.7 labeling_0.4.3 rprojroot_2.1.0
[41] fastmap_1.2.0 grid_4.4.2 cli_3.6.5 magrittr_2.0.3
[45] e1071_1.7-16 withr_3.0.2 scales_1.4.0 promises_1.3.2
[49] timechange_0.3.0 rmarkdown_2.29 ragg_1.3.3 hms_1.1.3
[53] memoise_2.0.1 evaluate_1.0.3 knitr_1.49 rlang_1.1.6
[57] Rcpp_1.1.0 xtable_1.8-4 glue_1.8.0 DBI_1.2.3
[61] rstudioapi_0.17.1 jsonlite_2.0.0 R6_2.6.1 systemfonts_1.1.0
[65] units_0.8-5