Orientation

Two packages

estiMINT estimates transmission intensity from a measured prevalence and the setting it was measured in, returning a single equilibrium value, the EIR and the human biting rate it implies. stateMINT emulates the individual-based model and returns the prevalence or clinical-case trajectory that follows a campaign.

run_scenarios, exported from estiMINT, runs both models. It runs estiMINT first and feeds the EIR it returns into stateMINT, the order the two-stage pipeline forces, so use it unless you need one of the models on its own.

Reading paths

Readers new to Python should start at Python for Malaria Modellers, which begins with installing an interpreter, and readers already comfortable with Python should start at Getting Started, which covers installation, a first scenario, and the R idioms that do not carry over. Core Concepts explains the models and the pipeline, which nobody needs to use MINTverse.

Conventions

Python blocks are executed and the output below each one is what the code produced, while shell blocks and the occasional R block shown for comparison are transcripts. The package’s install name and import name differ, which Installation explains.

Entry points

The examples use a small set of functions, four from estiMINT and one from stateMINT. Each is documented argument by argument in the estiMINT API and the stateMINT API.