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sntmethods

Analytical methods for Sub-National Tailoring (SNT) of malaria control strategies. sntmethods turns DHS/MIS survey microdata and routine health facility data into survey-weighted indicators, model-based geostatistical surfaces, and incidence estimates for sub-national program planning. It is the analytical companion to sntutils (data I/O, cleaning, dictionaries, spatial validation).

What this package does

Three workflows, each with a dedicated guide on the package website:

  • DHS survey analysis — survey-weighted, design-correct estimates for 100+ indicators across 16 domains, with confidence intervals and admin stratification. Every family ships a machine-readable data dictionary, and you can inspect the available DHS variables before building any indicator.
  • Spatial modeling (MBG) — model-based geostatistics that turns cluster-level survey data into continuous raster surfaces and population-weighted admin estimates, via fit_mbg_indicator() (single indicator) or run_mbg_pipeline() (full pipeline across 14 indicator families).
  • Routine data: incidence & TPR — malaria incidence from facility data using the N0-N5 cascade, test positivity with structured fallbacks, and STL + Mann-Kendall trend analysis.

All estimators return tidy, long-format tables keyed by admin level (adm0, adm1, adm2) with point, ci_l, ci_u, numerator, denominator, and indicator metadata — so survey and modeled estimates stack cleanly.

Installation 

# install.packages("pak")
pak::pkg_install("ahadi-analytics/sntmethods")

Spatial features need GDAL/GEOS/PROJ system libraries, and the MBG workflow additionally needs INLA and the mbg engine. See Get started → Install for the full instructions.

Quick start 

library(sntmethods)

# Discover surveys and inspect variables BEFORE building indicators
ge <- dhs_read(path = "path/to/parquet", file_type = "GE",
               survey_type = "DHS", country_code = "TG")
kr <- dhs_read(path = "path/to/parquet", file_type = "KR",
               country_code = "TG", survey_year = 2017)
make_dhs_raw_dictionary(kr)        # full variable list for the recode

# Compute a survey-weighted indicator
fever <- calc_fever_dhs(dhs_kr = kr, gps_data = ge,
                        shapefile = shp_admin, admin_level = c("adm0", "adm1"))

See the Get started guide for the full tour, and inst/scripts/ for complete, runnable examples.

Documentation

Guide Topic
Get started Overview, installation, a short end-to-end tour
DHS survey analysis Survey discovery, variable inspection, 16 indicator domains
Spatial modeling (MBG) fit_mbg_indicator() and run_mbg_pipeline()
Routine data: incidence & TPR N0-N5 cascade, TPR fallbacks
Trend analysis STL + Mann-Kendall + Sen’s slope
Methodology & conventions Indicator specs, naming, dictionaries
Reference Every exported function

Indicator methodology specs (variable mappings, inclusion criteria, WHO/WMR references) live as machine-readable YAML in inst/methods/.

  • sntutils — companion utilities (data I/O, dictionaries, cleaning, spatial validation)
  • DHS.rates — standard DHS mortality rate calculations
  • mbg — model-based geostatistics engine (IHME)

License

MIT