model_measles_gha01

Simulations examining RDT impact using Ghana as an example context. Work presented as part of Feb 2022 co-chair.

Parameters in the baseline model were adjusted to fit observed timeseries of measles incidence. Poisson-based likelihood function is maximized over one free parameter that scales total incidence and is interpreted as a reporting rate.

Important features include:

• Multi-node network used to represent the country of Ghana at the 10km scale.

• Network infectivity contagion transfer between nodes.

• Spatially varying rates of routine immunization.

• Acquisition-transmission covariance.

• Pre-specified calendar of supplemental immunization activities (SIAs).

• Maternally derived immunity.

• Overdispersion of the infection rate.

• Maximum simulation duration based on elapsed time.

• Weighted agents to represent multiple individuals per agent.

• Age-based immunity initialization to approximate endemic transmission.

• Infectivity reservoir to represent persistent exogeneous importation.

Test scenario projects incidence forward and implements outbreak response SIAs based on observed incidence.

Important features include:

• SQL-based event reporting of symptomatic incidence.

• Spatial varying reporting rates sampled from a beta distribution.

• Response interventions created dynamically using python in-process.