Maplaria

Maplaria generates spatio-temporal predictions of malaria prevalence using model-based geostatistical (MBG) methods to support subnational endemicity classification and spatial aggregation of survey data.

Key Features:

• Model-based geostatistics (MBG): Implements MBG methods for spatio-temporal prediction of malaria prevalence.
• Data inputs: Accepts user-provided community malaria prevalence survey data and vector data defining administrative boundaries.
• Spatial aggregation and scaling: Produces geostatistical predictions at user-specified spatial scales and aggregates predictions to administrative units.
• Model fitting and validation: Model fitting and validation are pre-configured by experts using publicly available malaria survey datasets such as the Harvard database.
• Endemicity classification: Classifies subnational areas into endemicity levels based on predicted prevalence.

Scientific Applications:

• Subnational risk mapping: Maps malaria prevalence at subnational and national scales to inform control programme planning.
• Policy and resource allocation: Provides endemicity classifications and prevalence estimates to support health policy decisions and resource allocation.
• Surveillance and monitoring: Generates aggregated prevalence estimates from community survey data for surveillance and programme monitoring.

Methodology:

Applies model-based geostatistical (MBG) methods to user-uploaded community survey data to predict annual malaria prevalence; initial model fitting and validation are performed by experts using established malaria survey datasets (e.g., the Harvard database).